IntroductionWelcome| 00:04 | Hi! I am Corey Koberg, and I'd like
to welcome you to Google Analytics
| | 00:07 | Essential Training.
| | 00:09 | In this course, I'll show you how the
free Web Analytics tool from Google can
| | 00:13 | help you dramatically improve your
ability to understand and improve your site.
| | 00:17 | I'll show you how to evaluate your
marketing efforts, and determine not just
| | 00:21 | how your visitors are reaching your
site, but how to evaluate the value of
| | 00:25 | each of those visitors.
| | 00:26 | We'll also take a look at exactly how
your visitors are interacting with and
| | 00:30 | using your site's content.
| | 00:32 | We'll show you how to track all of your
marketing through Google Analytics, even
| | 00:35 | your offline print, radio, or even TV ads.
| | 00:39 | We'll identify the areas of your
site that perform well, and perhaps more
| | 00:43 | importantly, we'll identify
areas that are holding you back.
| | 00:47 | Whether you're new to Web analytics,
or you just want a closer look at how to
| | 00:51 | utilize Google Analytics, we'll show you
all the essentials you need to know to
| | 00:55 | take advantage of this powerful and free tool.
| | 00:58 | I've spent years developing
techniques and best practices for some of the
| | 01:02 | world's largest brands and Web sites.
| | 01:05 | This course will teach you how to
utilize those same techniques, while
| | 01:08 | analyzing your site's data.
| | 01:10 | We've got lots to cover, so let's get started.
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| How to get the most from this course| 00:00 | Until now, high-powered Web analytics
has only been available to those with
| | 00:04 | serious budgets, and serious IT skills.
| | 00:07 | It's not an overstatement to say that
Google Analytics has revolutionized the
| | 00:11 | industry by making an enterprise class
analytics package available for free to
| | 00:15 | anyone who wants it.
| | 00:16 | Literally millions of people are now
taking the opportunity to understand and
| | 00:20 | evaluate their sites, from the C-suite
of the enterprise, all the way down to the
| | 00:23 | hobbyist with a blog.
| | 00:25 | This course is all about getting you
comfortable with using the interface,
| | 00:29 | analyzing the reports, and
evaluating your marketing campaigns.
| | 00:32 | While I'll touch on the process of
getting an account, and a basic install, we're
| | 00:37 | focusing on the analysis, and using
the tool here, and less on the technical
| | 00:41 | details or very advanced
concepts, like custom filters.
| | 00:44 | I assume that if you have an
enterprise or otherwise complex Web environment,
| | 00:48 | that you have IT resources to assist
you with the code changes to your site.
| | 00:53 | I'm certainly not attempting to replace
the need for a professional programmer
| | 00:56 | or IT staff here, and I will for the
most part be discussing how to use Google
| | 01:00 | Analytics, not the finer details of how
to install, implement, or configure it.
| | 01:05 | If you do find yourself in need of
more advanced help, code or otherwise,
| | 01:09 | there's an entire network of Google
certified consultants who can help.
| | 01:11 | If you are starting with a fresh account,
in order to see these reports, you will
| | 01:15 | need to get some data in there.
| | 01:16 | Depending on how much traffic your
site receives, it may mean you need to wait a
| | 01:20 | couple of weeks to get some visitor
data in the database and populate the
| | 01:23 | reports enough before you can
start to see interesting things.
| | 01:26 | I'll use a couple of different data sources
here for my examples throughout the course.
| | 01:29 | I'd love to show you all the great
report examples from real clients with brand
| | 01:34 | names you'd recognize and Web sites
that you probably visit, but as you can
| | 01:38 | imagine, companies treat their
analytics data pretty confidentially.
| | 01:41 | So instead, I will stick to both my
company's Web sites, both cardinal path on
| | 01:45 | our older site with our former name,
websharedesign.com, as well as some examples
| | 01:49 | from the Google Store.
| | 01:51 | On our site, you'll see data from
visitors to the pages and blog posts in those
| | 01:54 | sites that discuss analytics consulting,
training, and the other services we
| | 01:58 | offer, but we don't actually
sell anything on the Web, per se.
| | 02:01 | So to get those examples,
I'll use the Google Store.
| | 02:04 | Now, this isn't the Google shopping
comparison engine, but rather an actual
| | 02:08 | e-commerce store on the Web that sells
Google branded T-shirts, backpacks, and
| | 02:12 | the other merchandise.
| | 02:13 | They were kind enough to share their
data with all of you, so we can see real,
| | 02:17 | live data in action.
| | 02:18 | I've tried to structure this course to
show you as closely as possible what you
| | 02:22 | will actually see on your screen.
| | 02:24 | But keep in mind that this
product is rapidly evolving.
| | 02:27 | Google actually pushes in updates to their
interface literally every single month,
| | 02:31 | so things are constantly
being updated and improved.
| | 02:33 | Now the good news is that the majority
of these essential fundamental concepts
| | 02:38 | that I presented here are
unlikely to change too drastically.
| | 02:41 | So while a dropdown may become a link,
or move from one side to the other, and
| | 02:44 | a particular report may change the
wording or the name, if you focus on the
| | 02:48 | concepts, rather than the actual pixel placement,
I'm confident it won't slow you down too much.
| | 02:54 | Lastly, the topic of Google Analytics and
Web analytics as a whole is a massive one.
| | 02:59 | I couldn't possibly cover everything
there is to discuss here in one course,
| | 03:03 | so I have worked hard to consolidate
the most important fundamental concepts
| | 03:06 | here; the things that are truly
essential to getting you well on your way.
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| What's new in this update?| 00:00 | As you may have noticed, this
course was updated in late 2011.
| | 00:04 | Some of you may even be wondering about the
updates, and whether you should watch it again.
| | 00:07 | Some of the changes were simply to
reflect the new interface, but we also have
| | 00:11 | some new features that didn't exist
before, such as the amazing new Flow
| | 00:14 | Visualization reports.
| | 00:15 | I think these reports will completely
change the way you think about content
| | 00:19 | analysis and funnels in Google Analytics.
| | 00:21 | We also have new movies on real-time
analytics features that are unlike anything
| | 00:24 | we've seen in Google Analytics before.
| | 00:26 | We also can now perform analysis on page
load times with the Site Speed reports,
| | 00:30 | as well as new AdWords reports, and a new
account and profile settings interface.
| | 00:35 | There are certainly some exciting new
tools to check out, but let's talk about
| | 00:38 | those simple interface updates for a moment.
| | 00:40 | As you may know, Google releases new
features all the time, and doesn't adhere to
| | 00:44 | strict version releases.
| | 00:45 | This means that some of the
interface may look slightly different on your
| | 00:48 | screen as it does here in the movies.
| | 00:49 | Our goal is to update the video any time
there's a change in functionality, but
| | 00:53 | we realize there are
times when it won't line up
| | 00:55 | pixel for pixel with what
you see in your account.
| | 00:57 | In most cases, the concept is the still
the same, even if the buttons have moved
| | 01:01 | position or are renamed.
| | 01:02 | One humorous example of this is when
Google changed the Visitor section of
| | 01:05 | reports to be renamed the Audience
reports, literally when I was in the booth
| | 01:09 | in the middle of recording this.
| | 01:10 | So you'll see, even in this updated
version of the course, that sometimes that
| | 01:14 | section is labeled Visitors, and
sometimes it's labeled Audience.
| | 01:17 | But in some ways that serves as a good
example, since the underlying reports are
| | 01:20 | all the same; just the name has been changed.
| | 01:23 | So be on the lookout for that and other
minor changes that can and will happen
| | 01:26 | as Google continues to
update the analytics tool.
| | 01:28 | And of course, if you watched this
prior to the update, and you think you
| | 01:31 | can figure out changes to the new
interface, then you may not need to watch
| | 01:34 | this whole course again. You could
just skip to the movies that cover the
| | 01:37 | new features.
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1. What Is Web Analytics?The pitfalls of hit counting and turning data into information| 00:00 | Modern business intelligence and Web
analytics tools give us the ability to
| | 00:04 | collect a staggering amount of data.
| | 00:06 | In fact, the problem now is not that we
can't get the data; it's that we have so
| | 00:10 | many tools and sources of data, we can't
possibly process at all, and as a result,
| | 00:15 | we're being buried by it.
| | 00:17 | The goal of today's analytics is not
just to collect the data and store it away
| | 00:20 | in meaningless charts and graphs, or
worse yet, just store it to rot in the
| | 00:24 | digital vaults, but rather we want to
take that raw data, organize it into
| | 00:30 | something that we can use, something
actionable, something that has importance
| | 00:36 | to us, and meaning to us as people.
| | 00:39 | From a business perspective, we need to
be able to answer all the questions that
| | 00:43 | are fundamental to effectively
marketing and growing our business, and while
| | 00:47 | these questions can get very
specific and complex, they don't have to be.
| | 00:51 | Some of the most important
questions to ask ourselves, especially in the
| | 00:55 | beginning, can be simple, fundamental
questions such as, how is the Web site doing?
| | 01:01 | This desire to analyze our
Web site isn't necessarily new.
| | 01:05 | Not long after the Web was created,
site owners were looking for ways to
| | 01:08 | understand what was
happening on sites they built.
| | 01:11 | You may even remember when every
site seemed to have something like this.
| | 01:14 | I'll admit I thought these were fascinating
when the technology first appeared. I put
| | 01:19 | them on all of my sites, and refreshed
each day to see how many hits had piled
| | 01:23 | up, but the truth is, they tell us almost nothing
that we can take action on to improve our site.
| | 01:28 | The limitations were obvious, so then
we got a little more sophisticated with
| | 01:32 | our first-generation log analyzer
analytics tools, but the truth is, it's just
| | 01:36 | that hit counter over time. It's still
not too actionable. Even the most
| | 01:40 | sophisticated of these, that had
bandwidth and server utilization stats, really
| | 01:45 | don't get to the fundamental
question of, how is the Web site doing?
| | 01:48 | They tell us how the Web server is
performing, but that's just a commodity these days.
| | 01:52 | What we really care about is the
performance of the content, and how the visitors
| | 01:57 | are interacting with the site.
None of these tell us that.
| | 02:01 | So how does modern Web analytics
answer those fundamental questions?
| | 02:04 | Let's take an example.
| | 02:06 | This a result of an e-mail blast that
was sent directing traffic to the site.
| | 02:09 | We are split testing two versions of the
e-mail to see which one was more effective
| | 02:13 | at generating sales.
| | 02:15 | Each line here represents one of the versions,
which are aptly named Version1 and Version2.
| | 02:20 | We can see that these e-mails brought
in roughly the same amount of visitors --
| | 02:23 | about 10,000 -- and nearly the same per
visit revenue value of $0.15 and $0.13.
| | 02:28 | Now, you'd be forgiven for assuming
that there wasn't much action to see
| | 02:32 | here; they're so similar.
| | 02:34 | But as Web analysts, we need to
consider all the data, or we risk missing
| | 02:38 | valuable, but perhaps varied in sites.
| | 02:41 | With two blasts a day, this brings us
to approximately 20,000 visitors per day.
| | 02:44 | At those values per visit, that $0.02
difference adds up to $12,000 per month.
| | 02:51 | In other words, the entire salary of
a well paid analyst, and then some.
| | 02:55 | This is why it's so important to
get away from counting hits, and into
| | 02:58 | understanding user behavior, and
evaluating quality sources of traffic, and the
| | 03:02 | performance of all the elements of our site.
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| Web analytics: A tool and a process| 00:00 | Before we jump right into the tool and
the interface, it's important that we
| | 00:03 | back up and think about what Web
analytics is really all about.
| | 00:07 | There are dozens of definitions out
there, but the venerable Wikipedia provides
| | 00:10 | one that I really like.
| | 00:12 | Web analytics is the study of online
behavior in order to improve it. And the
| | 00:16 | critical part here is the in order to
improve it section. We're not just here
| | 00:20 | to look at pretty pie
charts for sake of fancy reports.
| | 00:23 | We need to find those insights that
will allow us to take action, and actually
| | 00:27 | improve our sites based
on those pretty reports.
| | 00:29 | It's perhaps just as important to
realize what a Web analytics is not. Let's
| | 00:33 | take this by way of a common analogy.
| | 00:36 | Web analytics software is like an x-ray machine.
| | 00:39 | In this case, the patient is your Web site,
and the x-ray machine is Google Analytics.
| | 00:44 | The tool will identify and display exactly
what's going on under the skin of your Web site,
| | 00:49 | much like this x-ray will
detect and display the broken bones.
| | 00:53 | However, it won't end your pain, and
it won't ultimately fix your problem.
| | 00:57 | For that, you're going to need someone
who can actually read and interpret the
| | 01:00 | x-ray, and perform the analysis necessary to
actually take action, and improve the situation.
| | 01:06 | This is why even the best or most expensive
analytics tools still won't do it for you.
| | 01:11 | You still need smart people like
yourselves at the helm of the tool in order to
| | 01:15 | get any real improvement.
And like any good doctor,
| | 01:18 | we're going to assess
the entire situation.
| | 01:21 | We're going to perform triage to
figure out which areas need the attention
| | 01:24 | first, such as prioritizing
the skull over the finger.
| | 01:28 | After we address that appropriately,
maybe by prescribing some drugs to deal
| | 01:32 | with the swelling, we move on to the
next issue, and we continue down the line,
| | 01:37 | constantly analyzing, adjusting,
improving, and optimizing.
| | 01:42 | Web analytics is a process, and set
it and forget it has no place here.
| | 01:46 | We first perform the measurement, we
apply analysis and learn from what we see,
| | 01:51 | and then we take action.
| | 01:53 | Our site is constantly changing,
our marketing is changing, the tools
| | 01:57 | themselves are changing, and the Web
is continually changing; even the world
| | 02:01 | itself is ever changing.
| | 02:03 | We need to make sure our analytics
process doesn't become stale, but rather
| | 02:07 | evolves and keeps up with those changes
in order to ensure our site is always
| | 02:11 | performing at its best.
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2. What Does Analytics Measure?Defining goals and conversions: Why do you have a web site?| 00:00 | Goals are a fundamental concept of any
analytics package, and without goals,
| | 00:04 | you have nothing more than a fancy hit counter.
| | 00:06 | Goals are what will allow us to
evaluate quality traffic from poor
| | 00:10 | performing traffic.
| | 00:11 | In other words, in order for us to
say that site A is sending you much more
| | 00:14 | valuable traffic than site B, we need
some criteria to base that on, and that's
| | 00:18 | where goals come in.
| | 00:20 | This will all become clear as we examine
the anatomy of a Web site visit, and the
| | 00:23 | role that analytics can play in that evaluation.
| | 00:26 | The first step is to attract traffic.
This is fairly obvious, and it's where the
| | 00:30 | vast majority of online marketing is devoted.
| | 00:33 | There are many options to getting
visitors to your site, some paid, like
| | 00:36 | AdWords, Microsoft
AdCenter, Ask.com, Facebook ads.
| | 00:41 | Even low cost or free options, though,
like organic search engine traffic or
| | 00:47 | e-mail marketing, cost you time,
effort, and resources, which are often more
| | 00:51 | precious than money.
| | 00:52 | We also have to consider all of the offline
marketing, where we publish our Web
| | 00:56 | site address in hopes that
customers will pull up our site the next time
| | 00:59 | they hop on the Web.
| | 01:00 | There is no doubt that a lot of effort
goes into getting visitors to our site,
| | 01:04 | and traditionally, this is where
almost all of our focus has been.
| | 01:08 | We mistakenly believe that if we get
enough visitors to our site, somehow that
| | 01:12 | will be good enough, but just dropping
them off on the front door of our site
| | 01:15 | and hoping for the best isn't good enough.
| | 01:17 | We need them to actually take that next step,
| | 01:21 | whether it's to put something in their
shopping cart, fill out a lead gen form,
| | 01:25 | download coupons, or even just find
our phone number so they can pick up the
| | 01:28 | phone and give us a call. And not all
visits behave the same, by any stretch of
| | 01:32 | imagination. There is high performing
traffic, and low performing traffic, and
| | 01:36 | everything in between.
| | 01:38 | In order to analyze which sources and
types of traffic are valuable, we need to
| | 01:42 | track what those visitors are doing
when they're on the site; what content
| | 01:46 | works, how are they using the site,
and ultimately figure out how to segment
| | 01:50 | them to understand why
they're doing what they're doing.
| | 01:53 | Finally, we're going to measure how
many folks, and which segments of those
| | 01:57 | people, reach that final step, and
convert. Whether it's putting money in your
| | 02:01 | bank account, or filling out that lead
gen form, we get to tell our analytics
| | 02:05 | package exactly what we consider a
successful visit by setting goals, and
| | 02:10 | calculating our conversion
rate based on our goals.
| | 02:13 | One very important point about goals
is not to overlook intermediate goals.
| | 02:17 | We're often so focused on that last
step, such as a shopping cart checkout,
| | 02:21 | that we forget about all the factors in
between that contribute to the sale, and
| | 02:25 | get them to take that next step.
| | 02:27 | Think about an actual grocery cart.
| | 02:29 | Once you've filled it up, and gotten in
the checkout line, the chances that you
| | 02:32 | take that next step and pay are very good,
because you've taken all the previous
| | 02:36 | steps that lead up to that point.
| | 02:38 | We have equivalents online, and so when
we're doing our Web analytics analysis,
| | 02:43 | tracking these intermediate
steps and funnels is very valuable.
| | 02:48 | Determining our primary and secondary goals
is critically important, but not difficult.
| | 02:53 | We simply ask ourselves, why do you have
a Web site? What is the purpose of your
| | 02:58 | site, and what do you want
them to do when they visit?
| | 03:00 | If you have an e-commerce site, then your
primary goal is simple: you want people
| | 03:05 | to check out with your shopping cart,
and put money in your pocket. Simple
| | 03:09 | enough, but don't forget about
intermediate or secondary goals as well.
| | 03:13 | But the reality is, most businesses
are not e-commerce companies, where they
| | 03:16 | accept credit cards over the Web.
This doesn't mean you don't have goals.
| | 03:20 | Many Web sites are designed
to generate business leads.
| | 03:22 | If you have a contact or lead gen form
on your site, that is a perfect goal, and
| | 03:27 | a fantastic way to
determine good traffic from bad.
| | 03:31 | Mailing lists are another great
example; one where we can easily put a value on
| | 03:35 | each goal conversion.
| | 03:36 | For example, if you know that you
average $500 in sales for every 1000 people
| | 03:42 | on your weekly e-mail newsletter,
you can easily calculate how much each
| | 03:46 | additional signup is worth.
| | 03:47 | E-mail marketing lends itself very
well to tracking via analytics, in both
| | 03:51 | getting people to sign up for your list,
as well as tracking the success of the
| | 03:55 | visits generated from sending out those mails.
| | 03:58 | Now, perhaps your goals to get the
phone to ring. There are many ways to track
| | 04:02 | both the number of visits that reach
that Contact Us info page, but also ways to
| | 04:07 | integrate your analytics with tracking
the ringing of your actual phone system. Or
| | 04:12 | perhaps you know that getting the
results of an industry study in the hands of
| | 04:16 | prospective clients is likely to influence them.
| | 04:18 | Well then tracking the downloads of
that study or white paper is a great
| | 04:22 | intermediate or soft goal.
| | 04:25 | Maybe you're a publisher, and your
goal is to get folks to click on ads or
| | 04:29 | affiliate links; we can do that too.
| | 04:30 | Now, this one is interesting, because it's
often the opposite of the previous goal.
| | 04:36 | If you just launched a new tech
support knowledgebase, it's very likely
| | 04:40 | you're trying to shift calls away
from your expensive call center, towards
| | 04:44 | the online knowledgebase.
| | 04:46 | So you certainly want to measure that
goal, and perhaps even measure contact
| | 04:50 | requests as a negative goal.
Don't forget about other areas of your
| | 04:54 | business and Web site.
| | 04:55 | For example, many of us have a career
section on our site. We know hiring can
| | 05:00 | be a costly and arduous process,
| | 05:02 | so many times we can even
associate a value with resume submission.
| | 05:06 | If we know that it generally takes X
amount of resumes to find the right
| | 05:09 | candidate, then we can put a value on
each resume submission or job application
| | 05:13 | that we receive through the site, and
we can evaluate which job board sites are
| | 05:17 | sending us quality traffic by tracking and
measuring the application process on our site.
| | 05:22 | So as you can see, there's no shortage
of goals that we can track on our site.
| | 05:26 | These goals are fundamental to our
ability to gain insights and perform analysis.
| | 05:31 | Later chapters, we'll
discuss how to implement goals.
| | 05:33 | For now, we want to be thinking in the
back of your mind why you have a site,
| | 05:37 | and what goals you're going to track.
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| Understanding data: Averages, segments, trends, and context| 00:00 | Misinformation is often
worse than no information.
| | 00:03 | In this example, I'll demonstrate two things.
| | 00:06 | First, that it is dangerous here at
lynda having so many people who are
| | 00:09 | handy with Photoshop,
| | 00:11 | but also, that averages can lie, even
if the underlying data is 100% accurate.
| | 00:14 | For example, if I told you that I've
been very involved with charitable giving
| | 00:19 | lately, where we've been delivering
Christmas presents for children. How involved?
| | 00:23 | Well, if you believe the numbers from NORAD,
| | 00:25 | my partner here and I personally
delivered presents to an average of over
| | 00:29 | 750,000,000 homes per year.
| | 00:31 | It was quite exhausting, because to do
that, we flew an average of 36,000,000
| | 00:36 | miles; exhausting indeed!
| | 00:38 | Now for the moment, let's agree to
forgo discussions of my partner's existence,
| | 00:43 | and focus on my stats.
| | 00:44 | My math is accurate, but the
statements are extremely misleading.
| | 00:48 | Considering the fact that Christmas last
year fell within a week of the due date
| | 00:52 | of my son's birth, you can bet I
wasn't out delivering presents thousands of
| | 00:55 | miles away. In fact, I barely left the house.
| | 00:58 | But the stats of 750,000,000 houses
and 36,000,000 miles are still true,
| | 01:02 | because my partner did all the work,
and I'm just taking credit via the
| | 01:05 | average. Now granted,
| | 01:08 | this may be an extreme example, but we
see similar types of this phenomenon all the
| | 01:12 | time in Web analytics, where averages
and aggregates can lie and mislead, even
| | 01:16 | when the numbers are completely accurate.
| | 01:18 | For example, if you have a hundred
visitors to your site, and 99 of them don't do
| | 01:22 | a thing, but the next one spends $1000.
| | 01:26 | Would it be accurate to say that on
average visitors to your site tend to
| | 01:29 | spend $10 per visit?
| | 01:32 | Well, yes, technically it's true, but
it leads us to conclude the wrong thing
| | 01:35 | entirely in our analysis of visitor behavior.
| | 01:38 | Averages and aggregates have their place,
but most often, the real insights lie
| | 01:42 | when we can segment out groups of
visitors, and understand their behavior. Then
| | 01:46 | we can see who's really driving the
success, and who's just using averages to take
| | 01:50 | the credit.
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| Introducing segments| 00:00 | Segmentation is the first step in true
Web analysis, and we have tons and tons of
| | 00:04 | ways to segment visitors in Google Analytics.
| | 00:07 | We'll look at each of these in detail
throughout this course, but for now I'd
| | 00:10 | like to touch on the types of
segments we'll be looking at.
| | 00:12 | One of the most common differentiator
that comes to mind when we think of the
| | 00:16 | different groups of visitors to
our site is where they are from.
| | 00:19 | There are plenty of ways for us to
break down how different users from different
| | 00:23 | parts of the world interact with our
site. Traffic sources are another
| | 00:27 | critically important segment.
| | 00:28 | How many conversions came from this
morning's e-mail blast? How did it compare
| | 00:32 | to our Facebook campaign?
| | 00:33 | We'll also want to segment out
visitors who do certain things on our site.
| | 00:37 | The traffic segment that completes my
checkout is certainly a different segment
| | 00:42 | than those who came and immediately
hit the Back button, and I'd like to know
| | 00:45 | more about what made them
have such different reactions.
| | 00:48 | We'll see that we can analyze segments
of traffic that don't initially come to
| | 00:51 | mind when we are talking about Web
analytics, such as traffic from newspapers,
| | 00:55 | TV, radio, print campaigns,
direct mail, and so on.
| | 00:59 | And it's very important to be able to
identify and analyze how these segments perform.
| | 01:03 | One of the ways we can segment is by
intent, and the keywords that visitors
| | 01:08 | type into search engines can give
us some insight into that intent.
| | 01:11 | Variations of similar searches can
indicate very different groups of people,
| | 01:15 | with different motivations and
intentions for visiting your site, and thus
| | 01:18 | keywords can play an important
role in our segmentation and analysis.
| | 01:22 | And depending on that motivation and
intent, the landing page you hit when you
| | 01:26 | arrive on our site might be very
successful, or not, and thus the ability to
| | 01:31 | segment by landing
page can be quite revealing.
| | 01:33 | If you're split testing your ads -- and
by the way, if you are not, you should
| | 01:37 | be -- then we'll want to understand how the
segments that saw one ad performed versus another.
| | 01:43 | Even things like what type of browser
you use can shed light on the visit.
| | 01:47 | After all, what do we know about a large
percentage of Safari users? Mac users, right?
| | 01:52 | Now, Mac users are certainly a different
segment. In fact, Apple ran an entire ad
| | 01:56 | campaign focusing on just
how different that segment is.
| | 02:00 | But even beyond the operating system,
we know that folks who took the time to
| | 02:03 | install Firefox or Chrome were at
least savvy enough to do so, and also cared
| | 02:08 | enough about their browsing experience
to take the time to customize it, versus
| | 02:12 | just using the default.
| | 02:14 | We can also get reports that tell us
about things like the connection speed,
| | 02:18 | screen resolution, and other
segmentation information that will help us design
| | 02:22 | sites that are optimal for our user base.
| | 02:24 | As you can see, there are many
different types of segmentation built right into
| | 02:28 | the reports, and we've just
scratched to the surface.
| | 02:31 | As we get further into this course,
you'll see that there will actually be very
| | 02:34 | few situations where we don't
utilize a segment of some kind or another.
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| Understanding trends and context| 00:01 | As we saw in the segmentation
intro, we can use that segmentation
| | 00:04 | functionality to evaluate performance.
Say, for example, which traffic source
| | 00:08 | is converting better?
| | 00:09 | This is very important to know, after all,
if you have one source converting at
| | 00:13 | 94%, and one at just 28%, obviously one
is more valuable to you than the other. Or is it?
| | 00:21 | Just like averages and aggregates lie,
we'll need to make sure we don't consider
| | 00:25 | metrics in isolation. We'll consider the
full context of the numbers we are seeing
| | 00:29 | to truly understand what's going on.
| | 00:31 | Now clearly, in this case, if I have to
choose one over the other, I'll take the
| | 00:35 | 28% any day over the 94%,
| | 00:36 | because 28% of 80,000 is a lot,
and 94% of 17 is very little.
| | 00:42 | But context is about much
more than just Web metrics.
| | 00:47 | We tend to get so focused on them
that we forget the other aspects of our
| | 00:50 | business that are interrelated.
| | 00:51 | For example, we have a lot
of travel related clients,
| | 00:56 | some here in the Caribbean, and I can
tell you that their Web site will convert
| | 00:59 | a lot differently on a day like
this, than on a day like this.
| | 01:05 | So we wouldn't want to conclude the
changes we made to the site were a disaster,
| | 01:09 | and we should revert them right away,
without realizing those numbers in the
| | 01:12 | context of this external weather event.
And more than merely understanding why
| | 01:16 | our performance is that way,
| | 01:18 | if we are savvy about our analytics, we
can actually use that to our advantage.
| | 01:21 | For example, when Hurricane Ivan was
blowing over one of our clients resorts, we
| | 01:26 | saw a huge spike in traffic.
| | 01:28 | Now digging into our analytics,
we found it was almost all due to
| | 01:30 | hurricane related searches.
| | 01:32 | Initially there was some concern
that this was effectively bad press that
| | 01:35 | would do damage to the brand,
| | 01:37 | but some creativity allowed
us to react and take advantage.
| | 01:41 | In Caribbean resorts it's
common to offer a hurricane guarantee.
| | 01:44 | In this case, the guarantee offers
the chance to come back during better
| | 01:47 | weather with all kinds of
upgrades and freebies.
| | 01:50 | So by changing the homepage from the
picture of the sunny paradise to a
| | 01:54 | huge flash page all about the hurricane
guarantee, they were able to salvage that traffic,
| | 01:58 | generate new stories in the press, and
get many folks to associate in their mind
| | 02:02 | the guarantee with their brand, not
just the scenes of horrific hurricanes, all
| | 02:07 | because they knew how to use
analytics to their advantage.
| | 02:11 | Taking metrics into context
is important in lots of ways.
| | 02:14 | I'm reminded of the CEO of the auto
company who was ecstatic showing off this
| | 02:17 | chart trending down, and showing how much money
they were losing every day. So why was he happy?
| | 02:23 | Because he wasn't losing as much as
they were expected to lose, and not as much
| | 02:26 | as their competitors were losing.
| | 02:27 | Keeping the data in relative context is
always important. And you can imagine the
| | 02:32 | plight of the Web marketing manager
for this hotel next to the Eiffel Tower.
| | 02:36 | Bidding on keywords and ranking in
the search engines just got a whole lot
| | 02:40 | tougher for the Hilton in Paris a few
years back when searches on Paris Hilton
| | 02:44 | suddenly got way more popular.
| | 02:46 | And the point here is that it had
nothing to do with their Web site, their
| | 02:49 | campaigns, their analytics, or really even
their business, but it had a huge effect on
| | 02:54 | the online keyword searches.
| | 02:56 | And we hear this all the time; oh,
they're not really my competitor, they just
| | 02:58 | have the same name, or we just
share the same keywords.
| | 03:01 | Well, then you are competition online.
| | 03:04 | We call this accidental competition, and
it's important to realize that your
| | 03:07 | competitors offline often have
little to do with your competitors online.
| | 03:11 | In this next graph, I'll point out two
aspects of analysis that are critical to
| | 03:15 | making correct decisions about our site.
| | 03:17 | I'll give you a second to
guess what industry this is.
| | 03:20 | It's actually travel as well.
| | 03:23 | Now, in my house, booking travel
usually involves my wife and I doing some
| | 03:26 | research when we get a chance, but then
not actually booking until we can both
| | 03:30 | get a free minute to sit down, confer,
look at the calendar, etcetera. This
| | 03:34 | always seems to work out to a Sunday
night when there is no big plans, and we
| | 03:37 | actually have a few minutes.
| | 03:39 | But just because that's how it works
in my house, I can't make the mistake of
| | 03:42 | thinking my personal bias is the same
as all my clients. In fact, it looks like,
| | 03:47 | based on this convergence
graph, that's not the case at all.
| | 03:50 | This graph tells me most people roll
into work Monday morning and say, I can't
| | 03:53 | handle this, I need a vacation, and jump online.
| | 03:57 | So the key here is that, besides
not letting our personal bias cloud the data,
| | 04:01 | we also need to recognize the prevailing
trends in our industry, such as days of the week.
| | 04:06 | We certainly don't want to compare how
a campaign did that ran on a Sunday and
| | 04:09 | one that ran on a Monday,
because unless it was massively different,
| | 04:13 | Monday will win every time, and
we will conclude the wrong thing.
| | 04:16 | Most trends are more than just
days of the week, but seasonal as well.
| | 04:20 | For example, if one of your keyword was
fireworks, and you saw a huge spike here
| | 04:25 | around the beginning of July, do you
assume that your new AdWord's campaign
| | 04:28 | must be the reason? Of course not.
| | 04:30 | The searches go up for everyone around
that time, because of the 4th of July.
| | 04:34 | Now, that's reasonably obvious to us.
| | 04:36 | But what if we saw an even bigger spike
earlier, like in November. Could it be from Halloween?
| | 04:43 | Well, we know the first step in
our analysis is segmentation, and we
| | 04:47 | immediately see that all the traffic is
coming from India, and centered around
| | 04:51 | Diwali festival keywords, which, like our 4th
of July, causes a spike in firework searches.
| | 04:56 | Now, this trend wasn't initially obvious
to us, but by using the tools available,
| | 05:01 | we can understand whether we can claim
success due to our marketing campaign, or
| | 05:05 | was it simply a rising tide that floats
all boats, and had essentially nothing
| | 05:09 | to do with our actual site or marketing.
| | 05:10 | Now the key here is that understanding
these trends will allow us to compare
| | 05:15 | apples to apples by controlling for
those external factors, and keeping our
| | 05:19 | data in context.
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|
|
3. Google Analytics FundamentalsHow does Google Analytics work?| 00:00 | In order to understand how to install
Google Analytics, and to interpret what
| | 00:04 | our reports are showing us, it's
important to talk about the basics of how
| | 00:07 | Google Analytics works.
| | 00:09 | Let's look at a typical Web site visit.
| | 00:10 | Let's say a person is here on their
laptop, and they want to visit your site, so
| | 00:14 | they open their Web browser.
| | 00:15 | Now, your Web site files are hosted on a
standard Web server, just waiting for a
| | 00:19 | browser to request them, and when the
browser makes this request, that request
| | 00:23 | is logged to the server logs.
When Web analytics was born, it was
| | 00:27 | exclusively with software that lived
here at the datacenter, and processed those
| | 00:31 | request logs from the server.
| | 00:32 | There are a lot of problems with
running your own analytics servers.
| | 00:35 | You need the servers themselves,
databases, datacenter space, and highly
| | 00:39 | skilled admins to run it all and keep
things secure, which can be very costly
| | 00:43 | and time consuming.
| | 00:44 | The way Google Analytics works is
based on the current generation of Web
| | 00:48 | analytics tools, and has a small but
important difference from the older
| | 00:52 | log file based systems.
| | 00:53 | When the Web server returns the page
back to your browser, it has some embedded
| | 00:57 | JavaScript, which is actually the
code that runs Google Analytics.
| | 01:01 | That Google Analytics code snippet
collects information from your browser, and
| | 01:05 | sends it directly to the databases
in Google's powerful datacenters.
| | 01:09 | So when you and I go to log into the
Google Analytics, we open up the reporting
| | 01:12 | interface that is connected to those databases.
| | 01:15 | It actually has almost nothing to do
with your Web server that hosts your Web
| | 01:18 | site, other than the fact that your
pages have that JavaScript snippet included.
| | 01:22 | It takes about three hours from the
time a visitor sees the Web page until
| | 01:27 | the data from that visit
shows up in our reports.
| | 01:29 | Although lately, Google has made great
strides here. It's actually not uncommon
| | 01:33 | to see the data show up in under an hour.
| | 01:35 | There are three main
advantages to a system like this.
| | 01:39 | First is accuracy; utilizing features
on the browser like cookies makes it
| | 01:43 | much more accurate.
| | 01:44 | The second is cost. All of the cost
of maintaining the hardware, databases,
| | 01:49 | bandwidth, datacenters, and IT
resources is handled and paid for by Google,
| | 01:54 | which is nice, considering
they give it all way for free.
| | 01:56 | The third is the globally
distributed infrastructure.
| | 01:59 | This helps your page load faster, but
also brings the reliability of Google's
| | 02:03 | network to the product.
| | 02:04 | After all, how many times have you
gone to google.com and had it not load?
| | 02:09 | Overall, this is a great step forward
from the old days of Web analytics in that
| | 02:12 | it's far easier, we get more
accuracy, and all for far less cost.
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| Setting up an account| 00:00 | The first step of installing Google
Analytics on your site is to create a
| | 00:03 | Google Analytics account.
| | 00:04 | Now, your Google Analytics account is
simply one of the many services, like Gmail,
| | 00:08 | that utilize your overall Google account.
| | 00:11 | So it may be confusing when we talk
about a Google account versus a Google
| | 00:14 | Analytics account, but don't worry,
we'll walk you through the process, and
| | 00:17 | we'll go over the differences
in detail later in this chapter.
| | 00:19 | I also want to point out that Google
changes the sign up process all the time as
| | 00:22 | they evolve their account creation process.
| | 00:24 | However, even if the page layout or
form specifics are tweaked, the basic concept
| | 00:29 | hasn't changed in years, and isn't likely to.
| | 00:31 | You create an overall Google account,
and then we link the services, like
| | 00:34 | Analytics, AdWords, or even Picasa,
to that overall Google account login.
| | 00:39 | If you don't have a Google Analytics
account yet, you'll likely find yourself in
| | 00:42 | one of three categories.
| | 00:44 | One, you have no prior Google account
at all, and you're starting with scratch.
| | 00:47 | Two, you have a Google account,
| | 00:50 | such as Gmail, or Picasa,
or Google, but not AdWords.
| | 00:53 | Or, you have a Google account that's
already associated with an AdWords account,
| | 00:58 | but not yet a Google Analytics account.
| | 00:59 | Let's actually take this last one first,
because it's the most critical to get
| | 01:03 | right from the start.
| | 01:04 | If you have an AdWords account already,
we want to create your Google Analytics
| | 01:08 | account via that AdWords account, or
it may cause issues later when you want
| | 01:12 | them to talk to each other.
| | 01:13 | So the first thing you want to do is
actually go ahead and log in to AdWords.
| | 01:17 | From here, click on the Tools and
Analysis tab, and then the Google Analytics
| | 01:21 | link. At this point, since you don't
have a Google Analytics account associated
| | 01:25 | with AdWords, it's going
to ask you two questions.
| | 01:27 | You can either create a free Google
Analytics account, or if you already have
| | 01:31 | one, we can link it here.
| | 01:32 | If you already have one, we'll
address linking in a different video.
| | 01:35 | So for now, we'll assume
that you don't have one yet.
| | 01:38 | We'll click on this first radio
button, Continue, and that's really is.
| | 01:42 | At this point, it's already added
Google Analytics on to your account, and
| | 01:45 | launched you right into the new
account, new profile process creation here.
| | 01:48 | We'll walk everyone through this
process in a second, but let's catch the other
| | 01:52 | folks up to this point first.
| | 01:53 | Now, for the rest of you who
aren't AdWords users, when you go to
| | 01:57 | google.com/analytics,
you'll be faced with this screen.
| | 02:00 | If you don't have an account of any kind yet,
we can go ahead and just click Sign Up Now.
| | 02:04 | Here you're going to go through the
standard Google account creation process.
| | 02:07 | And remember, you can use your existing
e-mail, such as your corporate e-mail here;
| | 02:10 | it doesn't have to be a Gmail
account, and probably shouldn't.
| | 02:13 | You fill out this form; now that everyone has
a Google account, we will go ahead and log in.
| | 02:18 | So whether you already had an account,
or whether you just created one, we
| | 02:21 | are all at the same space right here.
| | 02:24 | This is the same screen we saw inside
the AdWords interface, and we're all going
| | 02:27 | to use it to sign up for our first profile.
| | 02:30 | Here we simply type in the URL of our
Web site, and give it an account name that
| | 02:33 | means something to you.
| | 02:34 | Select your Country, select your Time
Zone, go ahead and read through the Terms
| | 02:38 | of Service. If you agree, select
the check box; Create a New Account.
| | 02:42 | As far as account creation goes, that's it.
| | 02:44 | The only step left is to add the
tracking code onto your site, which we'll do
| | 02:47 | in the next video.
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| Installing tracking code on a site| 00:00 | In the last movie, we just created an
account, and now we are at this point where
| | 00:03 | it's asking us to take the final step,
which is to install the tracking code in
| | 00:06 | your site that will
actually perform the tracking.
| | 00:09 | Installing Google Analytics isn't
like installing a software product.
| | 00:12 | All you are going to do here is paste
this JavaScript they give you on every
| | 00:15 | page of your site, and Google will
take care of the rest. That's it.
| | 00:18 | There really isn't anything to install,
per se, other than just pasting this code.
| | 00:22 | Now, what you see here is the new
version of the code, known as the Async code,
| | 00:26 | which is much faster and more flexible.
| | 00:28 | There are a few options, depending on
how your site is configured, and advanced
| | 00:31 | users will want to modify the code to do
things like track multiple accounts on
| | 00:35 | one page, or set up advanced
segmentation with custom variables, but to get the
| | 00:39 | primary functionality of the tool
right out of the box, Google Analytics can
| | 00:42 | get the data it needs by just making sure
this code is copied to the top of every page.
| | 00:47 | In this Essentials course, we don't
want to get too far into the advanced
| | 00:50 | configurations, but we will take just a
moment to quickly touch on some of the options.
| | 00:54 | If all you have is a single simple domain,
then this code here is what you want.
| | 00:58 | If you have a domain on a site that
has multiple subdomains that you want to
| | 01:01 | track all as one -- for example, maybe
you have a shopping cart on one, or a blog,
| | 01:05 | or anything else on a different
subdomain -- you can select this second radio
| | 01:08 | button here, and use this code.
| | 01:10 | There is also an option here if you
have multiple top-level domains, which are
| | 01:14 | basically different URLs of
different Web sites entirely.
| | 01:17 | This is a fairly advanced configuration
with multiple steps here. You are going
| | 01:20 | to have to add the additional code on
to the links in the individual sites to
| | 01:23 | get this working properly,
and seen as a one big site.
| | 01:26 | This configuration is a bit outside
the scope of this course. If you do find
| | 01:29 | yourself in this situation, you can go
ahead and click on this question mark,
| | 01:33 | you can do some reading with that,
and seek out some assistance if needed.
| | 01:38 | Back to this page. The second
tap up here is the Advanced tab.
| | 01:41 | We are going to be faced with the same
options we had before, with a little more
| | 01:44 | detail, but we also have a new one;
a site built for a mobile phone.
| | 01:48 | It's going to give us some code we can
put on our site that we don't necessarily
| | 01:51 | have to rely on JavaScript to run.
| | 01:53 | Now, JavaScript isn't going to run on
some more basic mobile phones without a
| | 01:57 | JavaScript-enabled browser,
| | 01:58 | so this is going to use
special server-side code.
| | 02:00 | We have code samples
here in PHP, Perl, JSP, ASP.
| | 02:04 | The last one here is just a completely
custom one, where we can edit the code
| | 02:08 | inside here if we want to do some more
advanced techniques we alluded to earlier,
| | 02:12 | that requires you to add
specific code to do custom tracking.
| | 02:15 | We also have the option of tracking
AdWords here back on standard tab by
| | 02:19 | clicking this check box here, and the
option to link your AdWords account.
| | 02:22 | Now, instead of utilizing this check box,
we suggest you check out the video we
| | 02:26 | have called linking your AdWords
account, which will show you how to do this
| | 02:29 | through the AdWords account's interface instead.
| | 02:31 | Now, the vast majority of you are going
to select this first tab with this first
| | 02:35 | radio button for a single Web site,
and that's what we are going to do here.
| | 02:38 | Then we are going to come over here,
click in here to select all the code, copy
| | 02:43 | that into our memory, and the next
thing what we are going to have to do is
| | 02:45 | paste it on our site. Now, notice it
wants us to paste it on every single page,
| | 02:49 | and also that it needs to go specifically
right before that closing head tag.
| | 02:53 | Now at this point, I need to pause and
point out that if you are not familiar
| | 02:56 | with your site's code, you'll want to
seek some help from your administrator.
| | 02:59 | Everyone's site is different, and you
need to make sure you are doing it the
| | 03:02 | right way for your site.
| | 03:03 | On my particular site, we run WordPress,
and one advantage of this is that it
| | 03:07 | has a common header across all pages,
which means, I only need to paste my
| | 03:11 | code once in that header file, and it will
show up automatically in all the pages on my site.
| | 03:15 | So let's go ahead and
take a look at that example.
| | 03:17 | In my case, I am going to go here to
my Web site editor, click on the Editor,
| | 03:21 | and I have got all the different files
of the site over here on the right. I am
| | 03:24 | going to come over here and select the
header file, and then what I am going to
| | 03:27 | do is I am going to scroll down in this file,
and I am looking for that closing head tag.
| | 03:33 | The closing head tag is just the bracket,
and then a forward slash, and then word head.
| | 03:38 | What I want to do is paste the
code right before that particular tag.
| | 03:41 | Now, we have some other code here that you can
ignore, as it's not part of Google Analytics.
| | 03:47 | I should also point out that we tend to
do a lot of customization and beta testing,
| | 03:51 | So if you are looking at my particular
site, www.cardinalpath.com, it might not
| | 03:54 | be the greatest plain vanilla example.
But in this case, we ignore these three
| | 03:58 | lines, and what we see right up here is
our Google Analytics tracking code, which
| | 04:02 | is almost right before the head tag.
| | 04:04 | Okay, we've got that pasted in; make sure
it's right before the head tag. Come down
| | 04:08 | here; update the file.
| | 04:10 | Since I have updated my site with this
code, I want to check and make sure that
| | 04:12 | it is there, so I am going to go to my
site. I am going to refresh the page to
| | 04:16 | make sure I have the latest code, and then
I am going to check View Page Source to
| | 04:19 | make sure that that code
actually did get updated on my site.
| | 04:22 | I scroll down here, I find the
closing head tag, and here I see my Google
| | 04:26 | Analytics tracking code
pasted in as it should be.
| | 04:30 | Now, I want to be clear;
| | 04:31 | instead of pasting the code over and
over on each page, I took a shortcut by
| | 04:34 | using a header file
that's automatically included.
| | 04:37 | Now, this is because I'm using one of
many Web sites that offer packages, such as
| | 04:40 | WordPress, Drupal, Django, Movable Type,
et cetera, that give me that option.
| | 04:44 | If you have that option,
which many of you will,
| | 04:46 | I highly recommend that you take advantage.
| | 04:48 | But if have a site built with just
individual HTML files that doesn't offer this
| | 04:51 | automated header, that's okay too.
| | 04:54 | It just means you need to go in and
paste the code on every single page of
| | 04:57 | your site manually.
| | 04:58 | After you've done that, we can go
back to our Analytics window here, we can
| | 05:02 | click Save, and assuming we have success,
we will be able to go back up here to
| | 05:07 | our Standard Reporting, and start
to see some data in our accounts.
| | 05:11 | So here we have our analytics reports,
but we don't see any data yet, because we
| | 05:14 | just installed the tracking code.
| | 05:16 | It can take one to four hours for this
data to start streaming into our account.
| | 05:19 | However, the other thing that I want
to point out to you is that even if we
| | 05:22 | refresh this page in one to four hours,
we may not see any data here, because by
| | 05:26 | default, Google Analytics' date range
is set to show up to the most complete
| | 05:30 | day, which was actually yesterday.
| | 05:32 | Today's date isn't included by default.
| | 05:34 | If I go over here and look at my date range, I
am going to see the last month, up to yesterday.
| | 05:38 | If I want to include today's date in
there as well, I can simply click on
| | 05:42 | today's date, click Apply, and update.
And what we see is this tracking code has
| | 05:47 | actually already started to
collect some visitors for today.
| | 05:49 | So as you have seen, for a basic
site, installing the tracking code is
| | 05:53 | actually very simple. You just paste it in,
so that it appears on every page of your site.
| | 05:57 | However, if you do get this wrong,
it can have a drastic effect on your
| | 06:00 | ability to get accurate data. Sometimes
having misinformation is worse than no information.
| | 06:06 | So if you have a complex site that
includes things like multiple domains,
| | 06:09 | redirects, iFrames, Ajax, or Flash, or
if you are just not comfortable with
| | 06:13 | code, make sure you seek help if you
need it, so we can analyze your data
| | 06:17 | with confidence.
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|
|
4. Google Analytics Report FundamentalsUnderstanding accounts and profile administration| 00:00 | Overall, Google has worked very hard to
make Google Analytics both easy to use,
| | 00:03 | but still powerful, which can
be a difficult balancing act.
| | 00:06 | Before we dig into the reports and
analysis, it's worth understanding a few
| | 00:09 | things about the hierarchy of Google Analytics.
| | 00:11 | One of the more confusing, but
important, details is the difference between a
| | 00:15 | Google account, and a Google Analytics account.
| | 00:18 | We will start here at google.com/analytics.
| | 00:19 | When we click this Access Analytics
button, it's going to prompt us for user name
| | 00:25 | to log in, but this is not
our Google Analytics account.
| | 00:28 | This is actually the user ID
associated with our overall Google account.
| | 00:32 | In this case, I will use a test account.
| | 00:34 | Now, this particular account has
access to several different Google
| | 00:38 | Analytics accounts.
| | 00:40 | If we view this dropdown on the left-
hand side, we can see all the different
| | 00:43 | Google Analytics accounts that this
particular Google account has access to. In this
| | 00:48 | case, it's Cardinal Path account, the
Google Store, a Test Account, and so on.
| | 00:52 | In the top bar here, we can see the
actual user name that I am logging into
| | 00:56 | my overall Google account with,
that has access to all these different
| | 00:59 | Google Analytics accounts.
| | 01:00 | Let's talk for a second
about this user name up here.
| | 01:03 | This could be a Gmail account,
but we don't recommend it.
| | 01:07 | We recommend that you instead use your
corporate login ID, and the reason why is
| | 01:11 | that it's very difficult to
know who surfer4578@gmail.com is.
| | 01:16 | In understanding who has access to
which profiles and which accounts can
| | 01:19 | get very confusing when you can't readily
identify the people behind those user accounts.
| | 01:24 | If you require your organization to use
only corporate e-mail addresses, there is
| | 01:27 | less confusion, and everybody
knows who has access to what.
| | 01:31 | So in our accounts list here, we will
see a list of Google Analytics account,
| | 01:35 | and under that, a list of Web property IDs.
| | 01:37 | These are generally used to
differentiate when you have different Web
| | 01:40 | sites linked inside your account,
for example, microsites, or even
| | 01:44 | different subdomains.
| | 01:46 | In this case, we have several of those
associated with the Cardinal Path account.
| | 01:49 | If we then click the Plus button to expand
each of these Web property IDs, we will
| | 01:53 | see the different profiles that
belong to those Web property IDs.
| | 01:57 | To start viewing your reports and
dashboards, just click on the blue profile
| | 02:01 | link. If you're already in the reports
section, you can easily view a list of
| | 02:05 | profiles from the dropdown in the top
left-hand corner. And here we'll see the
| | 02:09 | same accounts, and we will see the
same Web property IDs, and we can expand
| | 02:12 | that all the way down to
the individual profiles.
| | 02:15 | Profiles are extremely powerful, and
advanced users we will find all kinds
| | 02:18 | of ways to tweak them,
| | 02:19 | but they can be a little
bit confusing at first.
| | 02:21 | Think of each profile like a
database that can be configured completely
| | 02:25 | independently of the others.
| | 02:27 | In some ways, it's like you have another
instance of the analytics tracking code on your site
| | 02:31 | where you can track things just
a little bit differently.
| | 02:33 | You can create up to 50 of these
profiles, and they can be for different sites, or
| | 02:37 | more likely, just alternate
databases for the same site.
| | 02:40 | If you do have different Web sites,
unless you're planning on treating those Web
| | 02:43 | sites as a single cohesive entity,
where users will cross from one Web site to
| | 02:48 | the other, and maintain a single
analytics tracking session that you want to see
| | 02:52 | as one big session, it's actually
better to put one Web site per Google
| | 02:57 | Analytics account, rather than trying
to put different profiles for each Web
| | 03:00 | site in the same account.
| | 03:02 | This is particularly true if you have
multiple AdWords accounts for each of the
| | 03:05 | different Web sites.
| | 03:06 | You generally want to maintain a one-to-
one ratio between your AdWords accounts,
| | 03:10 | and your Google Analytics accounts.
| | 03:12 | So if you have four AdWords accounts,
you generally want to have four Google
| | 03:15 | Analytics accounts to match each of those four.
| | 03:18 | Again, they can all be under the same
Google account login, just different
| | 03:22 | Google Analytics accounts.
| | 03:23 | Okay, so when will I have another profile then?
| | 03:25 | Well, it's often useful to have
multiple profiles or databases within the same
| | 03:29 | account itself. For example, in this account,
| | 03:32 | you can see that under a single Web
site, I have multiple different profiles.
| | 03:37 | It's often useful to have a main profile
where you will do the majority of your analysis.
| | 03:41 | You can also have a test profile where
you can make mistakes, try new filters,
| | 03:44 | and generally try new things
with no fear of hurting your data.
| | 03:47 | You'll also want to have a raw or
unfiltered profile, which has only pristine
| | 03:52 | data, and acts a bit of an insurance
policy against mistakes in your main one,
| | 03:56 | and one where you can always go back and do a
sanity check against that raw, unfettered data.
| | 04:01 | In this case, I also have another
profile that has only internal traffic, and
| | 04:05 | you could do one that just has
your AdWords traffic, for example,.
| | 04:08 | To create a new profile, it's relatively easy.
| | 04:10 | From the Accounts List screen, we are
going to click on the little gear button in
| | 04:13 | the top right-hand corner.
| | 04:14 | And here, our breadcrumbs tell us that
we are looking at all the accounts, in
| | 04:19 | which case, we are in the Cardinal
Path account, and now I am down here at
| | 04:21 | the Web property level.
| | 04:23 | To create a new profile inside this Web
property, I simply click on New Profile,
| | 04:28 | and select a profile name.
| | 04:29 | In this case, I am going to
say AdWords Traffic Only.
| | 04:32 | Select your Time Zone, and
then click Create Profile.
| | 04:35 | We can see the profile that we just
created in the dropdown here: AdWords
| | 04:38 | Traffic Only. I can also come here
to select the tracking code for that
| | 04:41 | particular profile, and look at
the Web property settings overall.
| | 04:45 | To create a new Web property, I can come
up here and click on the account, click
| | 04:50 | on the button that says New Web
Property, and give a new Web property a name.
| | 04:55 | We can put in the URL of the
Web site that we want to track,
| | 04:58 | but it's important to note that
this actually doesn't mean anything.
| | 05:01 | I can put www.apple.com here, but
that doesn't mean that I'm going to be
| | 05:06 | automatically tracking Apple's Web site.
| | 05:08 | This is just a name that helps me
remember where I'm going to be using this, and
| | 05:12 | these labels that can just
help us organize our account.
| | 05:15 | It doesn't actually mean that you're
going to necessarily be tracking this.
| | 05:18 | Google Analytics is going to
track based on where you put the tracking
| | 05:21 | script, not on what you enter into this form.
| | 05:23 | Again, select our Time
Zone, click Create property.
| | 05:27 | So here we see the Lynda Test Account
that we just created with the default URL
| | 05:31 | that we selected to remind
ourselves where we are going to put it.
| | 05:34 | If I click inside of that account, I
can see the profiles, in this case, just one. I
| | 05:39 | can create a new profile,
just like we said before.
| | 05:41 | In this case, I want to create raw
profile, select Profile, and here we have
| | 05:47 | our new Web property ID, with the
two profiles that we just created.
| | 05:50 | If we create a new Web property ID, then we
will have to put a new tracking code on the site,
| | 05:55 | as each Web property ID has its own
specific tracking number. Once we've placed
| | 06:00 | that tracking code on the site, to see
the reports, we can either come up here
| | 06:03 | and select one of the tabs for the
profile that we are in, or I can use this
| | 06:06 | left dropdown to select the
particular profile that I would like to see.
| | 06:12 | In this case, we see our tracking
status is, Tracking Not Installed, because I
| | 06:15 | haven't actually placed
that code on the site just yet.
| | 06:17 | Let's go into one that's already been
selected. Here I go into my Accounts List,
| | 06:21 | I am going to select a particular
Web property ID, and select one of
| | 06:25 | the profiles in there.
| | 06:27 | Here I can go to my Standard Reporting
tab, and see the standard reports that are
| | 06:31 | associated with this particular profile.
| | 06:32 | As I am browsing the individual reports,
and I'm inside a report where I want to
| | 06:37 | see the same data inside another profile,
I can simply come up here, and select
| | 06:41 | a different profile than the one that
I'm currently in. While the report will
| | 06:46 | stay the same, the data that we will see will
be coming from the profile that we just selected.
| | 06:50 | Understanding how to structure accounts
and profiles, as well as to navigate the
| | 06:53 | Accounts List page, is the first
step to enabling Google Analytics, and
| | 06:57 | analyzing your Web site.
| | Collapse this transcript |
| Navigating the reports and the Data Over Time chart| 00:00 | When you log into Google Analytics, and
select the profile you wish to analyze,
| | 00:03 | you are greeted with this screen.
| | 00:05 | We navigate the interface using the
tabs at the top, and the navigation down the
| | 00:09 | left-hand side for each tab.
| | 00:11 | Let's start up here with the Home tab.
| | 00:12 | In the left side navigation, we see the
Real-Time reports, Intelligence Events
| | 00:18 | and the Dashboards, all of which will
be discussed in detail in later videos.
| | 00:22 | At the bottom of the left side
navigation, you also see the Help files.
| | 00:25 | These are expandable links with
relevant help articles, and a search box to
| | 00:29 | help you search the Google help files
if you didn't find what you are looking
| | 00:32 | for within these links. The
| | 00:34 | Standard Reporting tab includes
all the built-in reports for that
| | 00:36 | particular profile.
| | 00:37 | The main sections that we see are the
Visitors section, which includes reports
| | 00:41 | about demographics, behavior,
technology, social media, and mobile.
| | 00:46 | The Advertising section, which will show
multiple reports specific to AdWords, if
| | 00:50 | you've linked your AdWords and
Google Analytics accounts together.
| | 00:53 | The Traffic Sources section, which
includes reports broken down by medium, or other
| | 00:57 | campaign data, and reports that are
dedicated to search engine optimization.
| | 01:01 | And the Content section, which gives you
details about the performance of your
| | 01:04 | content, the engagement with your
content, and Site Search reports, if you have
| | 01:08 | set up Google Analytics to work with
an internal Site search box that you
| | 01:11 | have on your Web site.
| | 01:13 | And last, and certainly not
least, the Conversions section.
| | 01:15 | This houses reports about goals you've set
up, Ecommerce, if you've enabled it for
| | 01:19 | your profile, and multi-channel funnel
reports to give you greater insights into
| | 01:22 | how users will find your site over time.
| | 01:25 | In this course, we'll be covering
most of the reports on the Standard
| | 01:27 | Reporting tab, and how you can use
them to find greater insights to help you
| | 01:31 | improve and optimize your site.
| | 01:33 | One feature that you're likely to see on
almost every report in Google Analytics
| | 01:36 | is the data over time chart.
| | 01:38 | This gives us a great 10,000 foot
view, but to really utilize it, we can
| | 01:42 | configure it to show much more.
| | 01:43 | For example, let's take a look at the
traffic sources report that shows our
| | 01:46 | organic, or free search engine traffic,
which is the kind of comes from Google, or Bing.
| | 01:53 | Here, we can immediately see from the data
over time graph that our visits during
| | 01:57 | this time period seem to increase
during weekdays, and fall off on weekends.
| | 02:01 | We can also click on any of the
metrics above the data over time chart to see
| | 02:05 | different metrics over time, including
any of the metrics on the Site Usage tab
| | 02:08 | data, such as pages per visit, average
| | 02:10 | time on site, percentage new
visits, or bounce rate.
| | 02:13 | As I click on each one of these,
we'll see the numbers included in this
| | 02:16 | particular graph are going to change to
reflect the metric that I'm looking at
| | 02:20 | this particular time.
| | 02:21 | In this case, I can see bounce rate
varying between 0 and 100% for the particular
| | 02:26 | data range that we've got selected.
| | 02:27 | I can also move to each of my goal
sets, and see metrics that are related
| | 02:31 | to those goal sets.
| | 02:32 | I could also move to Ecommerce, and see
metrics that are related to Ecommerce if
| | 02:35 | we had that enabled in this particular profile.
| | 02:38 | The next option I'd like to highlight
on this is the option to compare a metric,
| | 02:41 | where we can choose to pick a second
metric to view over time on the same chart.
| | 02:45 | In this case, we saw that our weekend
traffic is different than our weekday
| | 02:49 | traffic, but I'd like to understand a little
bit more about who those types of users are.
| | 02:53 | So I can come here to Compare Metric,
I can select % New Visits to understand a
| | 02:58 | little bit more about who these people are.
| | 03:00 | In this case, I can see that during the
weekend, when our traffic drops off, our
| | 03:04 | percentage of new visitors is
increasing in almost inverse proportion.
| | 03:08 | So perhaps these people are learning
Google Analytics for their personal
| | 03:10 | sites, versus the weekday traffic
tends to be business users that have
| | 03:14 | visited our site before.
| | 03:16 | By comparing two metrics on the same
graph, I'm able to see two different
| | 03:19 | aspects about that visit that give me
a little bit more information about who
| | 03:22 | those visitors are, and
how they're using my site.
| | 03:25 | When looking for trends over larger
time periods such as this, it may be
| | 03:28 | more useful to reduce the individual
day volatility by looking at entire
| | 03:32 | weeks or months at a time.
| | 03:34 | I can do that using the Graph By
buttons over here to select either an entire
| | 03:37 | month, or an entire week.
| | 03:40 | Another feature available on some
reports is the ability to plot rows in
| | 03:44 | addition to the overall trend.
| | 03:46 | Here I can see that the top two terms
show some confusion over whether our name
| | 03:50 | is two words or one.
| | 03:51 | If I select the check boxes here to
the left of these individual keywords, and
| | 03:55 | then scroll down and click Plot Rows, I
can now see the metrics associated with
| | 03:59 | just those two particular
keywords, as well as the overall.
| | 04:03 | In this case, I've still got the ability
to compare two metrics turned on, which
| | 04:06 | can get a bit confusing.
| | 04:07 | So let's go over and click the X
here; that will remove the ability to
| | 04:10 | compare two metrics.
| | 04:12 | Now what I see is my primary metric
of Visits, as compared to all of the
| | 04:16 | visitors, versus those who typed in
Cardinal Path with the space as a keyword,
| | 04:20 | versus those who used all just one word.
| | 04:22 | In this case, I see some good news.
| | 04:24 | As our site traffic is increasing, the
overall visits is made up much more of
| | 04:28 | people who are using the correct two
words, rather than the incorrect single one
| | 04:32 | word, which is dropping off as
a percentage of overall traffic,
| | 04:35 | so this is somewhat reassuring.
| | 04:36 | The data over time graph is a
staple of Google Analytics, and a more
| | 04:39 | versatile tool than most people
realize, that allow us to visualize our data
| | 04:43 | quickly and effectively.
| | Collapse this transcript |
| Selecting and comparing date ranges| 00:00 | One of the most critical steps of
doing Web analysis is defining and
| | 00:03 | comparing date ranges.
| | 00:05 | This is a powerful feature in Google
Analytics, and could be used in nearly every
| | 00:08 | report in the interface.
| | 00:09 | In each report, we will only see
the data for the date range selected.
| | 00:12 | We'll come here, and select the profile
that we're interested in doing analysis on.
| | 00:16 | And when we first login, what we're
going to see here is the last 30 days,
| | 00:22 | up until yesterday.
| | 00:25 | The reason for this is because the
current day is not yet complete, and so it
| | 00:28 | could skew our data.
| | 00:29 | If I want to select the entire month of
October, I simply click on the month of October.
| | 00:33 | If I want to select the entire month of
September, I click on September, and it
| | 00:37 | will auto pre-fill those dates for me.
| | 00:39 | I click Apply, and then I will see
the date range change to the month of
| | 00:42 | September, and the corresponding data down
here will update to show just that date range.
| | 00:47 | I can also select any
custom date range that I want.
| | 00:50 | For example, I could put up here, going
all the way back to 2009, and I can put
| | 00:54 | my cursor here to select the
particular date that I want.
| | 00:57 | In this case, I want to do September
1, 2009 through September 1, 20011.
| | 01:05 | Click Apply, it will
automatically update.
| | 01:08 | Now in this case, because I've
selected a long date range, I probably want to
| | 01:11 | back this down to either weeks or
months in order to smooth out some of the
| | 01:15 | volatility that we'll see.
| | 01:18 | Okay, that looks better.
| | 01:19 | I can understand trends much easier.
| | 01:20 | Now, note that I see a large drop off
here at the end. That's because my date
| | 01:24 | range only includes up to September 1.
| | 01:26 | So, because I'm looking at this on a
monthly basis, the month of September of
| | 01:30 | 2011 only includes data for one day.
| | 01:33 | In this case, I probably want to modify
this to go up through August 31, to be a
| | 01:39 | more accurate comparison
on a month by month basic.
| | 01:43 | One of the most powerful features is
to be able to compare the current time
| | 01:45 | period, versus a time period in past.
| | 01:48 | For example, if I come here, and I
want to select the month of October 2011,
| | 01:53 | versus the previous month: September. I
select the Compare to past check box, and
| | 01:57 | you can see it pre-fills in the time period.
| | 01:59 | I can change the time period that I'm
comparing to by putting my cursor here in
| | 02:02 | the first box, and
selecting whichever period I want.
| | 02:05 | In this case, I can compare August
1, or the entire month of September.
| | 02:10 | One thing to be careful for is noting,
when you're changing the date range, which
| | 02:14 | particular box you have selected.
| | 02:16 | Notice that the top one always needs to
be the most recent, versus the most past.
| | 02:20 | So in this case, if I wanted to do
October 2011, I need to put that here in the
| | 02:24 | most recent, versus the past of September.
| | 02:26 | Now that I have the bottom one
selected, I can select September, and it
| | 02:30 | will give me the accurate October versus
September, which are the dates I want to analyze.
| | 02:35 | Now, notice what you see here.
| | 02:37 | Because I'm still selected on month by
month basis, I only see two single data
| | 02:41 | points for the month of
September, versus the month of October.
| | 02:45 | I want to go back and push this back to
the daily granularity, so that I can see
| | 02:49 | each of those months
overlaid one on top of the other.
| | 02:51 | In this overlay, I can see the
performance for any individual metric, and how one
| | 02:55 | month that versus the others.
| | 02:56 | As I can see, they both had a bit of
a run up here in the beginning,
| | 02:58 | they both tended to smooth out, there
was some separation here, and then there
| | 03:02 | was a large bump for the orange one
here, which represents the month of
| | 03:05 | September, as we got towards the end of
the month, and then they came back down
| | 03:09 | towards together at the end.
| | 03:12 | Getting a high level view on the data
over time graph up here is interesting,
| | 03:15 | but it's not just here that changes
with the date range comparisons; it's every
| | 03:19 | report inside of Google Analytics.
| | 03:20 | For example, if I were to come down here
to the Traffic Sources report, and look
| | 03:24 | at my All Traffic sources report, I
would see different sources and mediums over
| | 03:28 | those particular date rages.
| | 03:30 | Here we can see that from October
versus September, there was a 60% drop in the
| | 03:35 | traffic that was referred from google.com.
| | 03:38 | Perhaps more interestingly is referrals
from reddit.com in the month of October
| | 03:42 | were 5000, but just 68 back in September.
| | 03:46 | So we saw 7000% increase in the
number of referrals that came from the
| | 03:50 | reddit.com site over those two months.
| | 03:53 | In addition to the traffic sources, I
may come down here to my Content reports,
| | 03:56 | and take a look at some of the reports
that we've got about the content of our
| | 04:00 | site, and how visitors are
interacting with it.
| | 04:02 | One thing that jumps out at us right
away is a large spike in the number of
| | 04:05 | page views here on this particular day.
| | 04:08 | If I want to investigate this a little
bit closer, I can go and change my date
| | 04:11 | range to just include that day;
| | 04:13 | October 24 versus September 24.
| | 04:15 | So on the top, I select just the day
October 24, versus September 24, and all of my
| | 04:23 | reports are going to change
to just reflect those two days.
| | 04:26 | As I scroll down here, it's going
to become apparent to me where those
| | 04:29 | differences in page views are. I see
| | 04:31 | we jumped from 2,700 all the way up to
43,000 page views for this particular page.
| | 04:36 | One thing that I want you to be careful
about when we're doing these comparisons here
| | 04:39 | is to make sure we're
looking at apples to apples.
| | 04:41 | Yes, in this case, I am comparing the
24th of the month versus the 24th of the
| | 04:45 | month, but in terms of user behavior,
that's not necessarily the most important
| | 04:48 | thing to think about.
| | 04:49 | For example, one of these days is a
Monday, and one these days is a Saturday, so
| | 04:53 | on most sites, you can expect to see
very different traffic, and comparing those
| | 04:57 | two days, one versus the
other, doesn't make a lot of sense.
| | 04:59 | The other thing that I want to be
careful about, in addition to days of the
| | 05:03 | week, is just the overall number of days.
| | 05:05 | If I'm talking about a quantity, such
as the number of transactions, the amount
| | 05:09 | of revenue, the number of visitors, I
want to make sure the number of days that
| | 05:12 | I have in each date range is the same.
| | 05:14 | For example, if I compare the
month of September, I have 30 days, whereas
| | 05:18 | the month of October is 31 days,
| | 05:20 | so we have a different number of
days being counted in each one of those.
| | 05:23 | Another thing to keep in mind
is in September we had a holiday.
| | 05:27 | So September the 5th on a Monday is not
going to necessarily be the same as that
| | 05:30 | same first Monday in the month of October,
because one is going to be a holiday,
| | 05:35 | versus the other one is
going to be a regular work day.
| | 05:37 | We can expect to see different numbers
there, and comparing those two may not be
| | 05:40 | an apples to apples comparison.
| | 05:43 | We can see a few other examples where
this can cause us problems in our analysis.
| | 05:46 | For example, if I look at this case, I
can see some troubling results here.
| | 05:50 | In January, the green line here, I can
see that in almost all cases, actually in
| | 05:54 | every case, things were
better than they were in July.
| | 05:57 | So fast forward seven months, and my
traffic has dropped, my revenue has dropped,
| | 06:01 | and in general, I'm doing worse,
and this is very concerning.
| | 06:05 | The problem here is I haven't
really looked at those apples to
| | 06:07 | apples comparisons.
| | 06:08 | What we want to do is break this
down on a year to year basis, where we're
| | 06:12 | comparing the same time periods in one year,
versus the same time period in the next.
| | 06:16 | In this case, when we do that, what we
can see is we're actually doing better.
| | 06:20 | From 2007 to 2008; in 2008, my
metrics are up across the board.
| | 06:25 | The reason it didn't look like it in
the top was because I was comparing the
| | 06:28 | Christmas time period, versus the
middle of the summer, and if you have any
| | 06:31 | kind of seasonal traffic, which many of
you do, you maybe comparing times that
| | 06:36 | don't make sense to compare
against each other, and a year over year
| | 06:39 | comparison may make more sense.
| | 06:40 | We'll come back to this ability to
restrict, include, and compare by date range
| | 06:44 | repeatedly throughout our analysis,
and seeing what's changed is often more
| | 06:47 | powerful than the absolute value in isolation.
| | Collapse this transcript |
| Using annotations to make notes in data| 00:00 | Analytics can tell us what happened,
but it often struggles with the why.
| | 00:03 | As analysts, that job falls to us.
| | 00:06 | It's incredibly useful for us to be
able to provide our own context, and provide
| | 00:09 | more background about
what's happening on our site.
| | 00:11 | So in this case, we can see
that the visits went up, but why?
| | 00:15 | Was it a great blog post? Did we run a
particularly effective ad campaign? Was
| | 00:19 | that the day the site went haywire,
and counted every visitor five times?
| | 00:22 | As we research this, we'll want to
treat the answers to that with as much care
| | 00:25 | as we treat the original raw data.
Particularly if we work in a group where
| | 00:29 | multiple people will be accessing the
same profiles, we can use annotations
| | 00:32 | to inform them of these events,
changes, and perhaps explain some of these
| | 00:35 | anomalies to the site data.
| | 00:37 | However, even if you work alone, these
can be extremely useful if you remember
| | 00:41 | six months from now what you changed
today, and what happened, and what the
| | 00:44 | results of your research was.
| | 00:45 | So to help in that, you can now add
these helpful annotations to your graph, to
| | 00:49 | help remember these significant
changes and events.
| | 00:51 | We simply click on this little
drawer button down here, which will open
| | 00:55 | the annotations app.
| | 00:56 | From here, we can see what
previous annotations were.
| | 00:58 | In this case, I've already made
some notes about what happened here.
| | 01:01 | As we can see, this was the day that an
e-mail marketing campaign was launched.
| | 01:04 | I also have the ability to go
in and create a new annotation.
| | 01:08 | Our first option over here is Visibility.
| | 01:09 | We can make this a Shared or a Private note.
| | 01:12 | Shared will be amongst other people who
have access to this GA account and this
| | 01:15 | profile will be able to visit.
| | 01:17 | If I set it to Private, that means that
only the specific user logged in to this
| | 01:20 | account, such as myself, will
be able to view this annotation.
| | 01:24 | You only have the ability to edit and
delete annotations for an account that was
| | 01:27 | made under your own user name.
| | 01:29 | So I can type in here --
| | 01:30 | let's say there was a
promotion on the blogger.com homepage.
| | 01:37 | Now, if I go ahead and save that, we
will see that this was the case here, and I
| | 01:41 | will be able to remember that each
time I come back to this account, and we'll
| | 01:43 | have that information for future
reference, for myself and for others.
| | 01:46 | We can use annotations to document and
show things like when profile changes
| | 01:51 | were made, such as when we applied a
new filter, when we set up new goals, when
| | 01:55 | we linked an AdWords account, when we
started advertising, when we enabled
| | 01:58 | Ecommerce; any major change to a
profile or an account should be noted.
| | 02:03 | We can also detect significant
anomalies, and maybe some of the possible
| | 02:07 | causes. Even if it's known, unknown,
suspected; any information that we can add
| | 02:12 | that will help ourselves, or the next person
researching, we want to go ahead and note.
| | 02:16 | Annotations allow us to provide context
and meaning to the data that will save
| | 02:20 | our team a tremendous amount of
research time, preserve that information for
| | 02:23 | future analysis, and help us spot and
explain trends and events that impact your
| | 02:27 | business, both for good and for bad.
| | Collapse this transcript |
| Using the help tools| 00:00 | When I first started using Google
Analytics, there were no books, no training
| | 00:03 | courses, and the help files weren't too helpful.
| | 00:06 | Luckily times have changed.
| | 00:07 | Google Analytics' help files have been
markedly improved over the last few years,
| | 00:10 | and you have lots of options beyond that to
make sure you get the most out of the tool.
| | 00:14 | Let's say we are taking a look here at the
Traffic Sources report, and particularly
| | 00:17 | the Direct Traffic report.
| | 00:19 | As you are looking at this report,
you may have some questions, and need a
| | 00:21 | reminder on what some of these things mean.
| | 00:22 | For example, if you don't remember
exactly what the Bounce Rate definition is,
| | 00:26 | you can take a look up here
at this little question mark.
| | 00:27 | If you click on that, it's going to give you a
definition of what each of these metrics are.
| | 00:31 | You might see these little question
marks throughout the Google Analytics
| | 00:34 | reports that can give you a hint about
what that particular definition, metric,
| | 00:38 | or dimension might be.
| | 00:40 | In addition to these little question
marks, there are also some contextual help
| | 00:43 | all the way down here at the
bottom, under the Help heading.
| | 00:46 | We will see here is The Direct Traffic
Report. If we click on that, it's going
| | 00:49 | to give us information about the particular
report that we are looking at, at this time.
| | 00:52 | If you want more general or a broader
context of help, you can click on the Help
| | 00:56 | Center link down here at the bottom.
| | 00:58 | That's going to launch a new window.
| | 01:00 | That's going to give you the ability to
take a look at things like how to set up
| | 01:03 | your tracking; how to manage
individual accounts, users, and data.
| | 01:06 | When we click on this, we will see some
links that come over here, based on those
| | 01:09 | particular categories on the right-hand side.
| | 01:12 | In addition, we have some topics up
here that are dedicated specifically to
| | 01:15 | analyzing your data once you have your
accounts set up correctly. For example,
| | 01:18 | if we click on the Analyze one here,
we may see the Conversion topic down
| | 01:22 | here. If we click on that, it will take us to
articles specifically related to conversions.
| | 01:27 | Goals are a primary way that we measure
conversions, so if we click on Goals, we
| | 01:30 | will see articles, such as how to set
up goals, and of course, we are going to
| | 01:34 | have an entire section of that
dedicated here in this course,
| | 01:36 | but you can never have too many options.
| | 01:38 | In that respect, let's take a look at
a few more options that we've got here.
| | 01:42 | If we go all the way back here to the
main one, we come out, and we can just go to
| | 01:46 | google.com/analytics, which is the
main page here for Google Analytics, and
| | 01:50 | across the top navigation,
we have a few options.
| | 01:52 | First let's click on the Support tab.
| | 01:55 | We have some free support
resources that are available to us.
| | 01:57 | You have Setup Checklist here, as well as
the Help Center we just took a look at.
| | 02:01 | There is also the Google Code site,
which is a bit more of a technical
| | 02:04 | documentation for those
looking to do an installation.
| | 02:07 | If we click here, we are going to see
things about the APIs, different types
| | 02:10 | of tracking codes, and more to do with the
actual code and technical parts of the setup.
| | 02:15 | Coming back to the site here, we also
have the User Forum, which is a forum that
| | 02:20 | is operated by Google, but not
necessarily staffed by Google.
| | 02:23 | It's going to have other users who
are trying to help each other, and answer
| | 02:26 | questions that may come up.
| | 02:27 | At the bottom here, we have the
professional services that are available, if you
| | 02:30 | need that level of help.
| | 02:32 | The next tab we have here is the Education.
| | 02:34 | There's the Google Analytics Individual
Qualification test. If you want to get
| | 02:38 | this certified, you can go here to
learn more about the online test, and
| | 02:41 | actually take the test itself.
| | 02:43 | There is also some links here for in-
person trainings that are available,
| | 02:46 | as well as some videos that have been
recorded on the Google Analytics YouTube channel.
| | 02:51 | And last but not least, there is a
Google Analytics blog, which can help to keep
| | 02:53 | you up to date with the many and
frequent changes to the interface that can come
| | 02:57 | from time to time, as well as posts by
industry experts who can give you tips
| | 03:01 | and tricks to keep your analytics skills sharp.
| | 03:04 | Google Analytics has been designed to
make it easy for the novice Web-analyst,
| | 03:07 | and the veteran alike, by providing
varying levels of helpful resources to match
| | 03:11 | your stage of expertise, and the
size of your analysis challenges.
| | Collapse this transcript |
|
|
5. Detailed ReportsViewing data in different formats (overview, tabular, pie, bar, compare to site)| 00:00 | Google Analytics not only records
the data for each visit, it gives us
| | 00:03 | tremendous power and flexibility in how
we can view and analyze the data that's
| | 00:07 | already been recorded.
| | 00:08 | This section will show us a few of the
most common and useful ways we can view
| | 00:11 | and visualize the data.
| | 00:12 | For example, let's say we navigate here
to the Traffic Sources report, and click
| | 00:16 | on the All Traffic sources.
| | 00:18 | Now, this is one of my favorite reports.
| | 00:20 | In fact, this is the first one I'm going
to go to if I get a new client, because
| | 00:23 | it provides so much information
about the current state of the site.
| | 00:26 | When we first log in, we are going to
see a table format here with source and medium
| | 00:29 | on the left-hand side.
| | 00:30 | It's going to be source and medium, and
then it's going to be a column with data
| | 00:33 | about each one of these different rows.
| | 00:35 | So the visits, the pages per visit, the
average time on site, the percentage of new
| | 00:39 | Visits, the bounce rate, for each one
of these source medium combinations.
| | 00:43 | So each one of these rows that talks
about how people arrived at the site from
| | 00:47 | the source and the medium is known as a dimension.
| | 00:49 | In Google Analytics', our columns are known as metrics.
| | 00:51 | These metrics provide information and
data about each of these individual rows,
| | 00:55 | and these rows are known as dimensions.
| | 00:57 | Now, this tabular format
presents a lot of data,
| | 01:00 | but for us humans, it's not particularly
easy to understand information when
| | 01:03 | it's just a bunch of numbers in a big table.
| | 01:05 | So Google Analytics gives us some
idea of how to visualize this information
| | 01:08 | that's going to help us gain some insights.
| | 01:10 | The first option is to take these
numbers, and turn it into a pie chart.
| | 01:13 | We will click this second button
here, and we are going to select the
| | 01:16 | percentage with the pie chart.
| | 01:18 | So when I look down these numbers here
on the left-side in Visits, I can see 55,
| | 01:21 | 52, 50; all kind of very close to each other.
| | 01:24 | Then I have a step down here to 33, and another
step down to 15, and then large grouping
| | 01:29 | down here into single digits,
down all the way down to 1600.
| | 01:32 | We can see this easily represented on the
pie chart over here, where I see the big three,
| | 01:36 | I see the step down to 33,
| | 01:38 | I see the other ones here, as well as
all of the long tail that's going to be
| | 01:41 | clustered together in the
aggregate here in the grays.
| | 01:44 | Or if you prefer bar charts, we can do that too.
| | 01:46 | If we go down here in this button, and
click on the Performance tab, what we are
| | 01:49 | going to see is that same
information represented here in bar charts.
| | 01:53 | So we can see our big three, then we can
see the drop down to 33, as well as the
| | 01:56 | drop to 15, and all of the also-rans as well.
| | 01:59 | Now this is useful, but it's not as
useful as it could be, because what we
| | 02:02 | essentially have is a single metric
that's repeated here in two different ways.
| | 02:06 | What if we switch to another
metric, such as bounce rate?
| | 02:10 | Now, we have shown before that a good
bounce rate can be correlated directly to
| | 02:13 | our revenue and other performance indicators.
| | 02:15 | Performance is a good word to focus on,
because that's what we really want here.
| | 02:18 | Volume on the left, and performance on
the right. The question is, as I look
| | 02:22 | at these different bounce rates, I
don't really know if these are good bounce
| | 02:26 | rates, or bad bounce rates,
or how they stack up.
| | 02:28 | Fortunately, we have an easy way to see that.
| | 02:30 | We can see if these are performing above
average or below average by switching over
| | 02:34 | to the next option, which is the Comparison.
| | 02:35 | What we are going to see here is these
dimensions compared to the site average.
| | 02:40 | I was looking at bounce rate, so I'm
going to go down and select Bounce Rate.
| | 02:44 | From here, I can see the two things that I
need to know to evaluate this bounce rate.
| | 02:47 | First is, how is it
performing to the site average?
| | 02:50 | Each of these bars is relative to the site
average, which is right down the middle.
| | 02:53 | So if you are on the right-hand side, you
are performing worse than the site average.
| | 02:56 | If you are on the left-hand side, on
the green, you are performing better
| | 02:59 | than the site average.
| | 03:00 | Now keep in mind, this is bounce rate,
where lower is good, so a negative 30 is
| | 03:03 | actually a good thing.
| | 03:04 | So here I can see that my number
one referral, google.com, is performing
| | 03:09 | right about average.
| | 03:10 | It's average performing traffic, but
the next one down here, Direct, is slightly
| | 03:14 | worse than average, and the next one
down here, blogger.com, with 50,000 visits,
| | 03:18 | is performing significantly
worse than the average.
| | 03:21 | However, from here, the 33,000 coming
from google organic is performing 33%
| | 03:25 | above the site average, and as we go
down, we see even better performing traffic
| | 03:29 | until we get down here to the gmail
blog over blogspot, which has a 53% better
| | 03:33 | bounce rate than the site average.
| | 03:35 | So in these two columns, we have the
two critical things we need to know.
| | 03:38 | We need to know how it's performing,
but also the number of visits to let us
| | 03:42 | know what this is in the context of volume.
| | 03:44 | After all, if something has a great bounce
rate, but only brings one or two visits,
| | 03:48 | it isn't necessary that
important to me as a site owner.
| | 03:51 | I need to understand how it's
performing, but also what percentage of volume
| | 03:54 | is this traffic that's coming from there,
| | 03:56 | or how big of an impact that high
performance is going to have on my site.
| | 03:59 | With this view, you can look and
evaluate individual traffic sources by the
| | 04:02 | number of visits from those sources, and
then evaluate their performance based on
| | 04:05 | bounce rate, for example,
| | 04:06 | and I'll always keep that number of
visits in view, so I have that context.
| | 04:11 | But if you want to get into some really
detailed analysis, such as looking at
| | 04:14 | trends by segmenting out different cities,
we can utilize the Pivot view to look
| | 04:18 | at all of that together by city.
| | 04:19 | To do that, we simply
click on the Pivot view here.
| | 04:23 | In terms of metrics, we set this up as we
did before, with Visits, and Bounce Rate.
| | 04:28 | In this case, I am going to pivot by city.
| | 04:29 | What we see here are source/medium
combinations, just like we had before down
| | 04:35 | the left-hand side. In each of these,
| | 04:36 | we can see the visits,
and the bounce rate.
| | 04:38 | We can see the total here, which is
the same column we had before, but now I
| | 04:41 | have also got it broken
down by individual City.
| | 04:44 | So I can see here that people from
London brought 1200 visits, and had a 69%
| | 04:48 | bounce rate, compared to Sao Paulo
at 124 visits, and a 73% bounce rate.
| | 04:52 | If I want to get really specific, I
can break this down to a secondary
| | 04:55 | dimension, and all kinds of other analysis,
which we won't get too far into for now.
| | 04:59 | But the point here is to show you,
you can get really, really specific, and
| | 05:02 | understand lots of different ways of
viewing the same data, all within the same table.
| | 05:06 | Now, there is one more data view
available for us, and that's the Term Cloud view.
| | 05:11 | This view is often used for keywords, so
let's try in this in the Organic Search report.
| | 05:16 | If we apply the Term Cloud view here,
and increase the number of rows to 50, we
| | 05:20 | can see the top 50 keywords
that brought traffic to our site.
| | 05:23 | The clear winners are in the
bigger font, and the darker color.
| | 05:26 | So far, we see Google Store,
Google Shop; things we would expect.
| | 05:29 | This is based on visits,
| | 05:31 | and it gets even better if we switch
our metric away from visits to something
| | 05:34 | more interesting, like average order value.
| | 05:36 | To do that, I am going to switch to the
Ecommerce tab, and select Average Order Value.
| | 05:41 | I am also going to increase my rows back to 50.
| | 05:46 | Here we get some very interesting data.
| | 05:47 | We can start to see the terms that are
bringing in the highest average order
| | 05:50 | values, not just the
highest number of visits.
| | 05:53 | And some words definitely jump out
at us. This is actionable data.
| | 05:56 | Term clouds tend to be
real crowd pleasers if
| | 05:58 | we include them in presentations, and
they are great conversation starters for
| | 06:01 | content and media teams.
| | 06:02 | As we get deeper into our analysis, we
will find that we can uncover far more
| | 06:05 | actionable data if we know how and
when to take advantage of each of these
| | 06:08 | view options.
| | Collapse this transcript |
| Navigating data with site usage, goals, and e-commerce metrics| 00:00 | As we saw in the video on views, Google
Analytics provides data broken down by
| | 00:04 | columns of metrics, and those
columns are grouped into tabs.
| | 00:07 | As we see here in the All Traffic Sources
report, we have several tabs available to us.
| | 00:11 | The first is a Site Usage tab, and
Site Usage is going to give us some
| | 00:14 | information about how people are
actually using the information on our site,
| | 00:18 | sometimes called engagement metrics.
| | 00:20 | We have visits, pages per visit, average
time on site, percentage of those visits that were new
| | 00:25 | visitors, bounce rate, etcetera.
| | 00:28 | This information is broken down
by this dimension of source/medium.
| | 00:31 | So we can see, for each of these
different sources, how those metrics are doing.
| | 00:35 | In other words, when people come from
google.com, how long are they staying on
| | 00:39 | the site versus someone who comes over
from YouTube.com. But we may also want
| | 00:44 | to view those by how those
particular visits are achieving our goals.
| | 00:47 | So if we click over to the Goal Set 1,
we're going to get a different set of columns.
| | 00:51 | Here, we can see how google.com, and
YouTube, and the other sources are doing as
| | 00:55 | far as the number of visits they bring,
but also completing our orders, viewing
| | 00:59 | software downloads, hitting our Contact Us page;
| | 01:02 | these are all goals that I have defined
as things that I want people to do on my
| | 01:06 | site, and this is going to evaluate
each of these different traffic sources on
| | 01:09 | how well they achieve those goals.
| | 01:11 | We can also see the overall goal
conversion rate, as well as some information
| | 01:14 | about the per visit goal value.
| | 01:16 | Now, in my Goal Set 2, I've
defined some engagement metrics.
| | 01:20 | These particular goals I've defined as,
I want to see people who browsed my site
| | 01:25 | over five minutes, I also have a goal
of people visiting more than four pages,
| | 01:29 | I have a very ambitious goal of people
visiting over 10 pages, and then I can
| | 01:33 | see the goal conversion rate
for this particular set of goals.
| | 01:35 | Again, all of these are based back
on the dimension that I have; in this
| | 01:38 | case source and medium.
| | 01:39 | The last tab that I have
here is the Ecommerce tab.
| | 01:42 | If you have an e-commerce site, and you
have Ecommerce enabled, this can be a
| | 01:45 | really, really critical tab.
| | 01:47 | This is going to give us the dollars
and cents, exactly how much each of these
| | 01:51 | visits are worth; how much each of
these traffic sources are bringing in.
| | 01:54 | In this case, we can see those
same sources: google.com, blogger.com,
| | 01:58 | youtube.com, etcetera, and how much
revenue each of those visits resulted in,
| | 02:01 | how many transactions, the average value of
those transactions, this e-commerce conversion rate;
| | 02:07 | these can be really, really valuable
columns for us to see, because we can start
| | 02:10 | to put a value on each of these
things these visits are doing.
| | 02:13 | If you are involved in the AdSense
program, you also may seen an additional tab
| | 02:16 | here, as well as some information about
the ads that your site is displaying.
| | 02:21 | One useful thing to do here is use the
Compare to past feature in the date range.
| | 02:25 | If I click on the Date Range selector
up here, and click on Compare to
| | 02:28 | past; in this case, let's compare June
versus July. What I see is the same
| | 02:39 | report, except I have an additional
row here, where I'm going to see what the
| | 02:42 | percent has changed from the
July visits, versus the visits in June.
| | 02:46 | In this case, I can see that there was
a 23% drop in visits from google.com.
| | 02:50 | What's really interesting is, if I
scroll on down here, I can see that on the
| | 02:55 | Gmail blog over at blogspot.com, there is
a 92% drop in visits from the month of
| | 03:01 | June to the month of July.
| | 03:02 | The other thing I can notice is
there is a corresponding drop in revenue.
| | 03:06 | As you notice this column here of
Revenue, we see a 97% drop; going from $6,800
| | 03:13 | down to just $145 in the month of July.
| | 03:17 | This is some pretty insightful data.
| | 03:18 | This is something we can definitely want
to see in terms of the value that those
| | 03:21 | visits are bringing.
| | 03:22 | However, we can see a pretty tight
correlation between a drop in visits, and a
| | 03:26 | drop in revenue, which would be expected.
| | 03:28 | In just a minute, we'll see
that this isn't always the case.
| | 03:31 | The Ecommerce tab is useful in lots of places.
| | 03:33 | Let's take a look at the Keywords report.
| | 03:34 | If I click on Keywords -- now it may be
interesting to see how much e-commerce revenue
| | 03:39 | we're deriving from each of these
keywords. In other words, we know how valuable
| | 03:43 | each of these keywords were from visits,
but how much money were each of these
| | 03:46 | ones? Is there a particular
keyword that's driving value?
| | 03:48 | In this case, I want to
click back on my Ecommerce tab.
| | 03:51 | Here on the Ecommerce tab, if we
scroll down, we can see the different
| | 03:54 | keywords which brought folks to our
shop, and we also can see the number of
| | 03:57 | visits they brought.
| | 03:58 | By default, we're going to be sorted
by visits, but I am interested in which
| | 04:01 | keywords were the most valuable,
| | 04:03 | so I'm going to go ahead
and sort by revenue.
| | 04:05 | I do that by clicking on the
Revenue column, which is going to sort, in
| | 04:08 | descending order, the amount of revenue.
| | 04:10 | And one thing I notice in the second
one here is that the term google t-shirts:
| | 04:14 | in the month of July, 97 people searched
on this term; in the month of June, 95,
| | 04:18 | so you would expect the revenue
to be approximately the same.
| | 04:21 | However, what we see is that in the
month of July, there is a 426% increase from
| | 04:26 | the month of June, even though the
amount of visits only went up by 2.
| | 04:30 | So we can see there is not
always a correlation from there.
| | 04:33 | By having this extra column, and
actually understanding what the value is, we
| | 04:36 | can see exactly what that is; we don't
need to rely on visits to give us some
| | 04:40 | inference that may or may not hold true
about what the value of that keyword is.
| | 04:43 | Let's take a look at a few more examples.
| | 04:46 | The Visits tab is great, because it
gives us context, and insight, and visits
| | 04:50 | are, of course, one of the most
important things on our site.
| | 04:52 | But not moving beyond the Visits tab,
and staying on the Visits tab all the time
| | 04:56 | is highly dangerous, especially if
we have goals and e-commerce set up.
| | 04:59 | Let's take the case of this actual client.
| | 05:01 | Now, in this case, we were originally
working on some pay-per-click analysis, and
| | 05:06 | the client wasn't particularly
interested in it, pointing out that, in this case,
| | 05:09 | the number three medium, cost-per-click --
which is our pay-per-click -- was, as he
| | 05:13 | put it, a drop in the bucket.
| | 05:15 | If you look at the
referral traffic: 953,000 visits.
| | 05:19 | The claim was, this is where
my real traffic comes from.
| | 05:21 | I don't know why we're wasting our time
down here with this pay-perclick stuff; it
| | 05:24 | doesn't amount to anything.
| | 05:26 | Now, the problem was, at this
time, visits were all we had.
| | 05:28 | There was no revenue set up, because
there was no e-commerce tracking enabled.
| | 05:32 | Now, it stands to reason that he was
right. More visitors does equal more
| | 05:35 | business, but when it comes to data-
driven analysis, we're going to need
| | 05:39 | more than a gut feel.
| | 05:40 | When we've got the performance data to
show how the quality of these visits
| | 05:42 | stacked up, we see a
completely different story.
| | 05:45 | In this case, although there were
almost a million visits coming through on the
| | 05:48 | referrals, it only amounted to $15,000.
| | 05:52 | Although there were only 58,000 visits
coming from the pay-per-click -- the so-called
| | 05:56 | drop in the bucket --
this amounted to $11,000.
| | 06:01 | At this point, once we see the actual
value of these, we can see that not all
| | 06:04 | visits are created the same, and you
certainly can't claim that it's a drop
| | 06:06 | in the bucket any more.
| | 06:08 | The vast majority of Google
Analytics users don't have goals defined, or
| | 06:11 | e-commerce configured.
| | 06:12 | Now, for those of you sitting at home,
are you flying blind? Are you looking at
| | 06:16 | this, and thinking it's a drop in the bucket?
| | 06:18 | Later on, we'll show you how to
configure goals of your own, so you don't have
| | 06:20 | to rely on the Visits tab as your
sole performance indicator, which you
| | 06:24 | definitely shouldn't do.
| | 06:25 | But picking the proper
metric isn't easy.
| | 06:27 | Let's take the following case, where
we're asked to select the best campaigns.
| | 06:31 | So, the goal here is to pick out the
best campaign, and we are going to highlight
| | 06:34 | some different ways that we might
evaluate this, based on these metrics.
| | 06:37 | Now, we'll start out with a bang: ROI.
This is really what we're after, right?
| | 06:41 | Return on investment is the name of
the game, and although a 273% return on
| | 06:46 | investment is pretty good, there
is no question that 1000% is better.
| | 06:49 | In this case, it might be over.
| | 06:50 | We pick the bottom one, and move on
with it, and no one would blame us for
| | 06:53 | doing so, but just for fun,
we take a little look further.
| | 06:56 | Now, per visit value, we get
reinforcement of the same thing.
| | 06:59 | $1.41 on top, versus $3.22 below.
| | 07:02 | So if we are doing pay-per-click,
again, the bottom one is the way to go.
| | 07:05 | But what about Revenue?
| | 07:07 | We haven't brought any context here.
| | 07:08 | Well, on top brings 14,000 plus,
and the bottom only $7500 in revenue.
| | 07:14 | ROI is an easily manipulated value,
because it doesn't necessarily depend on the
| | 07:20 | number of visits, or any absolute numbers.
| | 07:22 | So even though you have an ROI of a
thousand, if you're looking for revenue, you
| | 07:24 | may be more interested in a 273%
ROI that brings you $14,000.
| | 07:28 | But we haven't really talked about the cost.
| | 07:32 | If you're doing advertising, to bring in
that revenue, you may have had to pay for it.
| | 07:36 | In this case, we get $9,000 versus $1500.
| | 07:37 | So in looking at all these different metrics,
how do we figure out which one is the best?
| | 07:43 | The bottom line we're
really looking for is net profit.
| | 07:45 | How much did I get, versus how
much did I have to pay for it?
| | 07:48 | And these two are almost exactly the same.
| | 07:50 | Even though every metric
was wildly different, and showed one
| | 07:53 | was vastly better than the other,
the bottom line at the end of the day:
| | 07:56 | they're about the same.
| | 07:57 | Let's look at another case.
| | 07:59 | How about these two?
| | 08:01 | One campaign brought 10,000
visits, and one brought 6000 visits.
| | 08:04 | Now, given that, by and large, most of
the folks that come in don't have goals
| | 08:08 | configured, don't have e-commerce,
don't have anything else to judge the value
| | 08:11 | of the campaign other than visits, it's
pretty clear that the top one's the winner.
| | 08:15 | But what about when we start looking
a little deeper; when we start looking at
| | 08:17 | things like impressions, and clicks?
| | 08:19 | If you're paying for each one of
those clicks, it gets a little bit more
| | 08:22 | tricky, because now money is going
out the door, so to get those visits,
| | 08:26 | how much did I have to pay?
| | 08:27 | In this case, even though 10,000 is
definitely better than 6000, if I had to pay
| | 08:31 | $9,000, versus just $774, that
might change the game considerably.
| | 08:36 | We also haven't looked at
what the value of that was.
| | 08:38 | Remember, visits aren't revenue.
| | 08:41 | When we look at the revenue one --
look at this 14,7 versus 38.
| | 08:44 | When we get back to that all
important net profit, the top campaign brought
| | 08:48 | $5,700, while the bottom one brought $38,000.
| | 08:53 | With each of these metrics I've picked,
it seems like the opposite one won.
| | 08:56 | After all, if you torture that data
long enough, it will confess to anything,
| | 09:00 | and agencies love to take advantage of
this to make you think that they're loser
| | 09:04 | campaigns are huge winners.
| | 09:05 | And if you don't understand these
metrics, it's probable that you believe them.
| | 09:09 | Understanding which tab to use,
which metrics to use, and which ones are
| | 09:13 | important in which situations, could keep
you from choosing the campaign with twice
| | 09:16 | the visits that would lose you $32,000.
| | Collapse this transcript |
| Sorting data with inline and advanced filters| 00:00 | Inline filters are a simple but powerful
tool to allow us to quickly control and
| | 00:04 | consolidate the data that we're
analyzing in the data table and its graphs.
| | 00:07 | Before we begin, let's talk about terminology.
| | 00:10 | There are three primary types of filters;
| | 00:12 | profile filters, inline
filters, and advanced filters.
| | 00:15 | If we take a look at the profile
settings in the Filters tab, here we'll see
| | 00:20 | some advanced profile filters that
will restrict some of the data that we can
| | 00:24 | get into the profiles.
| | 00:25 | When we talk about inline or
advanced filters, we're not talking about
| | 00:29 | profile filters at all.
| | 00:30 | Rather, we're talking about the filters at
the top of the data table and the reports.
| | 00:34 | Let's take a look.
| | 00:35 | As we move here to the Languages
report, in the Visitors section, under
| | 00:39 | Demographics, we can see all the different
languages of people who are visiting our site.
| | 00:43 | If I was interested in analyzing just the
Spanish language visitors, I could click on es.
| | 00:49 | The problem is that different browsers
report Spanish in different ways, and I
| | 00:53 | want to capture all the Spanish language users.
| | 00:55 | So what I can do is come here to the
filter box at the top of the table, and
| | 00:59 | I can click on es, click the
magnifying glass to run the filter, and this
| | 01:04 | will capture all the different lines
that contain es, whether it's just by
| | 01:07 | itself, whether it's Spanish,
whether it's going to be a Latin American,
| | 01:10 | Caribbean, Mexican, etcetera.
| | 01:12 | From here, I'm able to evaluate them
individually, and see how many visits
| | 01:15 | came from each different version,
but I can also see what the group
| | 01:19 | represents as a whole.
| | 01:20 | On the scorecard across the top, I can
see the metrics for all of them summed
| | 01:24 | together, and the data
over time graph updates as well.
| | 01:26 | So I can see here, there were 20,000
visits in total from Spanish users on my
| | 01:30 | site, who stayed for an average of 2.9
pages per visit, 1 minute and 6 seconds,
| | 01:35 | 92% of them were new visits, etcetera.
There are tons of uses for this type of
| | 01:39 | filtering inside these reports.
| | 01:41 | Let's take a look at some more.
Click on over to the All Traffic report
| | 01:45 | inside the Traffic Sources tab.
| | 01:49 | Here, we see the different sources and
mediums that bring traffic to the site.
| | 01:52 | Maybe your question is, how many blogspot
blogs are bringing traffic to your site?
| | 01:56 | If we simply type in blogspot here in
the filter box, hit Return, and what we'll
| | 02:01 | see down here is a list of all the
different blogspot blogs that are bringing
| | 02:05 | traffic to my site, and the scorecard,
and the data over time graph will update to
| | 02:08 | reflect the overall aggregate
numbers for those blogspots put together.
| | 02:12 | If I am interested in individual
blogspot blogs, I can look down here and see
| | 02:16 | all the different metrics
associated with each one of those blogs.
| | 02:19 | Another possible example is, what if we
were interested in the number of Apple
| | 02:21 | devices sending traffic to our site?
| | 02:24 | If I click on Visitors, and Mobile
Devices, we can filter for Apple.
| | 02:31 | Type Apple into the search box, and what
we're going to see is the data table down
| | 02:35 | here is restricted to only things that
contain Apple, and we can see individually
| | 02:39 | which Apple products are bringing them,
as well as the total amount that Apple is
| | 02:43 | bring in to my site across the top.
| | 02:45 | Let's head over to the organic
search report to see what else we can do.
| | 02:49 | This report under Traffic Sources > Sources
> Search, and Organic is going to default
| | 02:54 | to show the keywords that
users search to find our site.
| | 02:56 | When we talk about keywords that bring
people to my site, we can break these
| | 03:00 | down into two different kinds of keywords;
| | 03:02 | keywords that indicate someone is
already familiar with my site, my brand, or my
| | 03:06 | company, and the more generic
keywords, where someone was not necessarily
| | 03:09 | familiar with my company at all, but
was simply looking for some information, or
| | 03:13 | a solution to their problem.
| | 03:14 | For example, if you typed in the word
Google, or any derivative of that, you're
| | 03:17 | probably already familiar
with the company Google;
| | 03:20 | you're not just looking
for a generic search engine.
| | 03:22 | Now, if you type in something like
Google merchandise, you might not be looking
| | 03:26 | for the Google Store specifically, but
just a store that happens to sell Google
| | 03:31 | stuff. In fact, you may not know such
a thing as the Google Store even exists.
| | 03:34 | When we're working on SEO for your site,
most people tend to want to focus on
| | 03:39 | these non-branded terms.
| | 03:41 | These are the terms that are going to
bring you net new visitors, and let's face it,
| | 03:44 | if you're not ranking for your own
name, then your SEO has other issues.
| | 03:48 | When we're ranking for some of these
generic keywords, it can take a bit more
| | 03:51 | effort, and we want to measure our
progress using Google Analytics.
| | 03:54 | To do this, we'll go over here next to the
filter box, and we're going to click Advanced.
| | 03:57 | This is going to drop down some
more options to filter our data.
| | 04:00 | For our purposes, we want to
exclude keywords containing our brand name,
| | 04:05 | Google, and click Apply.
| | 04:06 | So we type in google, we're going to change
Include to Exclude Keyword, and click Apply.
| | 04:12 | Now, I've definitely got rid of my
branded term of Google, but I've got a few
| | 04:16 | more terms in this list that would
qualify as branded terms, or people who are
| | 04:20 | familiar with my brand.
| | 04:21 | For example, I see YouTube in here,
in different spelling variations; I see
| | 04:24 | Android. Those are things that
have to do with my brand as well.
| | 04:28 | So let's go back to the Advanced
filter, and we have this ability to run
| | 04:31 | multiple filters on here.
| | 04:32 | We have an and operator.
| | 04:34 | We can click this again;
| | 04:35 | we can select a dimension of Keyword.
| | 04:37 | I can choose to Exclude, and I can put
in words containing things like -- now we
| | 04:41 | have YouTube; we have some people who
spelled YouTube with a space, and others,
| | 04:45 | So I am just going to put tube in here
in general, and that should cover most
| | 04:48 | of those. Click Apply.
| | 04:49 | Okay, now we look down through the
list, and we still see some Android ones I
| | 04:52 | need to take care of. We see Google
misspelled. In fact, we even see Google
| | 04:57 | up here in Cyrillic.
| | 04:58 | So one thing to not is that this is not
going to do any type of translation, or
| | 05:01 | cover languages. If you specifically
want to include foreign language versions
| | 05:05 | of this, then you're going to
have to address those individually.
| | 05:07 | I can come up here, and I can
continue to add and ones down here.
| | 05:11 | I can also change this to do something else;
| | 05:13 | for those of you that are familiar
with regular expressions, I can use the
| | 05:17 | vertical pipe bar here to add
additional words that I want to exclude.
| | 05:20 | So in this case, I want tube, and gogle,
and android, and we are going to apply that.
| | 05:25 | I am going to increase the number of
rows to 50, and we can look down through
| | 05:29 | list, and look for a few other
variations of ones with more Os, ones
| | 05:33 | without the O; there's plenty
of misspellings in here. We can look for
| | 05:36 | Chrome, and other ones through here.
| | 05:38 | I can continue to add these through
here, and build up my list of branded
| | 05:42 | keywords that I want to exclude.
| | 05:43 | Now, one important piece to note here
for SEO purposes is this second
| | 05:50 | result here: not provided.
| | 05:51 | In 2011, Google decided that for any
user who is logged into their Google
| | 05:56 | account -- perhaps they logged in from
Gmail, or a Google Plus account, and never
| | 05:59 | logged out -- that they will
automatically be redirected to a secure version of
| | 06:03 | the Google search engine whenever they search.
| | 06:05 | When this happens, the Google search
engine will not pass individual search
| | 06:08 | terms through to Web analyst
tools like Google Analytics.
| | 06:11 | It's an unfortunate loss of data, but
everyone faces the same challenge, as no
| | 06:15 | tools can recover the data,
even Google analytics.
| | 06:18 | So it's not a branded term,
but it may skew your data.
| | 06:20 | So if we want to filter that one out,
let's go back to edit our filter again.
| | 06:25 | We're going to add a dimension, select
Keyword, Exclude, Exactly matching, and
| | 06:33 | type in not provided.
| | 06:37 | Apply this, and we can see that our
list can update to where the not provided
| | 06:41 | keywords will not be included in our list.
| | 06:42 | Okay, so if we continue on in this
fashion, we can weed out our branded terms,
| | 06:46 | and continue to improve our list, but
let's go ahead and take this list of
| | 06:50 | non-branded search terms,
and sort them by performance.
| | 06:53 | Now, bounce rate is a good metric,
| | 06:55 | so let's look for ones
that have a low bounce rate.
| | 06:57 | We can easily find this by
sorting on the Bounce Rate column.
| | 07:00 | We click on the Bounce Rate column;
first it's going to show us the largest
| | 07:04 | bounce rate. Click it again, and we'll
see bounce rate sorted by the least.
| | 07:09 | So this is what we want;
terms that don't bounce at all.
| | 07:12 | Now, what we've got here is a list of non
-branded terms that have a great bounce
| | 07:16 | rate, and so we all know that they're
very valuable; we should pour all of our
| | 07:20 | marketing efforts into this
list, right? Well, not quite.
| | 07:23 | As you astute viewers have noticed,
this is actually not valuable data at all.
| | 07:26 | In fact, this is nearly junk data.
| | 07:28 | Just because one person typed product.asp
into here doesn't mean that this is
| | 07:32 | the hot new keyword, and I should run
out and tell my CPC folks to go nuts on
| | 07:36 | that as a new keyword.
| | 07:37 | When we talk about performance, we're
really talking about performance in the
| | 07:40 | context of a reasonable amount of visitors.
| | 07:43 | So let's use filters to turn that
English, a reasonable amount of visitors, into
| | 07:47 | data that we can put into Google Analytics.
| | 07:49 | What I really want to see are just the
ones that brought in, let's say, five or
| | 07:53 | more visits; not these onesy,
twosy ones that are simply anomalies.
| | 07:57 | We are going to go back
into our Advanced filter;
| | 07:59 | we're going to add another row.
| | 08:01 | We're going to select a metric, and
we're going to select Visits, and we're going
| | 08:05 | to say Greater than 4, so that will
give us 5 or more visits. Click Apply, and
| | 08:10 | now we can see this is going to update
to show us ones that still have a good
| | 08:14 | bounce rate, but have a
reasonable amount of visits.
| | 08:17 | This list looks much better.
| | 08:19 | You see we've now got some real
contenders, and with a bit more massaging, we'll
| | 08:22 | have a true list of our best keywords.
| | 08:24 | This type of data filtering is
essential when you're dealing with any kind of
| | 08:27 | rate based metric, such as e-commerce
conversion rate, goal conversion rate,
| | 08:31 | bounce rate, etcetera.
| | 08:33 | Another tip for this type of analysis
is to expand your date range to get as
| | 08:36 | much keyword data as possible.
| | 08:38 | Advanced filters and inline filters
are incredibly powerful tools you'll use
| | 08:41 | over and over again in your analysis.
| | 08:43 | You'll find advanced filters
particularly powerful anytime you're sorting by a
| | 08:46 | rate to strip out those cases
with just a handful of visits.
| | Collapse this transcript |
|
|
6. Intro to SegmentationUnderstanding the importance of segmentation in data analysis| 00:00 | We said that the segmentation is the
key in the first step to any analysis.
| | 00:03 | Google Analytics is brimming with
segmentation options for us to isolate
| | 00:07 | certain groups our traffic.
| | 00:08 | One that is the most common
examples would be segmenting our visitors by
| | 00:11 | region, and from here we can
further segment our segments.
| | 00:15 | So for example, we can isolate a
single country, and perhaps we want to break
| | 00:19 | that down into individual states. But we don't
have to limit ourselves on how we drill down.
| | 00:23 | For example, when we do advertising such
as AdWords, we can target our campaigns
| | 00:27 | by country, and so it be very common
that we want to isolate traffic from
| | 00:31 | particular country, such as US, and
do analysis on just that country.
| | 00:35 | Maybe looking at just the AdWords PPC
traffic, so we can understand how those
| | 00:38 | particular campaigns are performing,
how we want to optimize them, all based on
| | 00:43 | isolating only traffic from
that country looking at AdWords.
| | 00:46 | Or, maybe perhaps we want to see what
web sites are popular for referring US
| | 00:50 | visitors to our site.
| | 00:51 | We can also look at a complete
different way of segmenting, such as by search
| | 00:56 | engine. We can isolate traffic
from just one of those segments--
| | 00:59 | in this case the Ask.com traffic--look at
different aspects of visitors from that one.
| | 01:04 | Now when we think about search engines,
what are the most important things when
| | 01:06 | we think about that?
| | 01:07 | Well, certainly one of those
things might be the landing pages.
| | 01:10 | What pages are ranking on Ask.com for
my site? Or maybe we want to think about
| | 01:14 | the different keywords that
people are typing into Ask.com
| | 01:16 | that sends traffic to my site.
| | 01:19 | Okay, looks good theoretically. How does
this actually work on Google Analytics?
| | 01:22 | Let's switch over to the
account and take a look.
| | 01:24 | We first talked about segmenting by
region, so let's click on Map Overlay
| | 01:27 | under the Visitors tab.
| | 01:28 | You can see lots of information about
how visitors from different countries
| | 01:31 | interact with our site.
| | 01:34 | From here, we can drill down to a
different country either using the map or
| | 01:37 | using the data table.
| | 01:40 | Here we can see all the traffic
that's visiting the country, and we can see
| | 01:43 | it broken down broken down by state,
with more visits to the darker states, less
| | 01:46 | visit to lighter states.
| | 01:47 | We can also see the exact information
down here in the data table, broken down by
| | 01:51 | Visits, Pages Per Visit, Time On Site,
% New Visit, Bounce Rate, et cetera.
| | 01:55 | Now when evaluating traffic, don't
forget to move over to the other tabs.
| | 01:59 | Our Goals that we have set up, and if
you have Ecommerce, that's certainly
| | 02:02 | very valuable information to know. Again, we
see this broken down by the individual states.
| | 02:06 | Well, of course, initially it will be
sorted by Visits as always, but we can
| | 02:10 | decide to sort by Revenue or perhaps
Average Value, and some things jump out at us.
| | 02:17 | Although California brought us the
most revenue, it's interesting to see
| | 02:19 | that North Carolina has a very high average
value, as well as Mississippi and Washington DC.
| | 02:24 | Now let's say we want to
see something different.
| | 02:26 | Let's say we want to see which city in
California has our most loyal clientele--
| | 02:31 | in other words, the highest percentage of
return visits. Let's drill down into California.
| | 02:38 | Here we see the graphical representation of
visits from different cities in California.
| | 02:41 | You can see a high concentration around
San Francisco Bay Area, Los Angeles area,
| | 02:46 | as well as San Diego.
| | 02:48 | Now, we don't have a report that gives
us a high percentage of return visits,
| | 02:51 | but we do have the percentage of new
visits, and we know that the opposite of
| | 02:54 | that would be return visit.
| | 02:56 | So we can sort by New Visits and
we'll see all the people with the 100% New
| | 03:00 | Visits, but we want the opposite; we
want to get the low percentage of visits.
| | 03:04 | Now I want to see cases that at least had
a few. I'm looking for my loyal clientele.
| | 03:10 | Let's go ahead and set an advanced
filter to say cities that have at least 10
| | 03:15 | visits, say number 10 has to
be greater than or equal to 10.
| | 03:21 | Apply the filter, and here you see a list of
cities with a fairly low percentage of
| | 03:29 | new visits, meaning that they have a
high percentage of return visitors. In this
| | 03:32 | case, Fairfax sends 60s visit, of
which 95% were returning visitors.
| | 03:37 | As you can see, we start to see some
cities here that don't normally pop up if
| | 03:41 | we were just doing things like sorting
by Visits or sorting by Total Revenue.
| | 03:45 | By adjusting our segments and
adjusting our metrics, we can see some insights
| | 03:48 | here that wouldn't otherwise necessarily pop up.
| | 03:50 | Let's take a look at the
different example with a different data set.
| | 03:53 | In the first part of this movie, we
also discussed segmenting by source,
| | 03:57 | specifically by search engines.
| | 03:59 | So if we click on the Traffic Sources tab,
| | 04:01 | we have an entire report
dedicated to search engines.
| | 04:03 | As we see here, far and away the
most searches are derived from Google.
| | 04:06 | Let's segment our reports further to
view only the segment of traffic sourced
| | 04:10 | from Google by clicking
and drilling down into Google.
| | 04:14 | Here we have further canned
pre-segmentation options, such as right now we're
| | 04:18 | seeing all of visits, but we know that
search engines have both organic, or free,
| | 04:21 | traffic, as well paid traffic, so let's
go ahead and just look at the non-paid
| | 04:26 | traffic, otherwise known as free,
natural, or organic search results.
| | 04:30 | These are just a few of your
segmentation and sub-segmentation options.
| | 04:34 | These options are nearly endless and
will depend greatly on what analysis
| | 04:37 | you're performing and what
questions you are trying to answer.
| | Collapse this transcript |
| Slicing data with dimensions| 00:00 | Dimensions are built-in segments, and
that's one of the primary ways that we will
| | 00:04 | segment data in Google Analytics.
| | 00:06 | Let's talk about organic
search engine traffic for a second.
| | 00:09 | Naturally, keywords are always a part
of that conversation, and in fact Google
| | 00:13 | Analytics makes it the default
dimension for the organic search report.
| | 00:16 | But for now, let's look at this
report using a different dimension.
| | 00:19 | At the top of the table we can see a
few other popular dimensions for this
| | 00:23 | report, such as Source and Landing
pages, and also a dropdown here where we can
| | 00:27 | take and choose any dimension that we like.
| | 00:29 | Let's talk about Landing pages.
| | 00:31 | The interesting thing about organic
search is that we don't get to choose where
| | 00:35 | in our site that traffic gets sent to.
| | 00:37 | The search engine does that by ranking
different pages that they feel are the
| | 00:40 | most relevant to the search term.
| | 00:41 | And maybe that's the homepage, maybe
it's a blog post, maybe it's an old page
| | 00:45 | that I didn't even realize
was still accessible on my site.
| | 00:47 | This is information we want to know.
So whatever page on our site ranks for
| | 00:51 | those keywords is going to be the landing page.
| | 00:53 | So we can change the Dimension
from Keywords to Landing page.
| | 00:57 | This way we can see which of
those are the most popular.
| | 01:00 | In this case, the home page is our most
popular organic landing page, followed by
| | 01:03 | the blog, and who we are page
listing all of the partners and employees.
| | 01:06 | But does this really answer
the question we're looking for?
| | 01:08 | This just shows us the most popular
landing pages from Google's organic
| | 01:11 | searches, but in the aggregate.
| | 01:13 | We're really trying to figure out the
most popular keyword and landing-page
| | 01:17 | combinations, and we've lost that
connection by switching from one dimension
| | 01:20 | to another entirely.
| | 01:21 | For this, we can use secondary
dimensions to show the most popular combinations
| | 01:25 | of the two dimensions we really need to know--
| | 01:27 | search term and the corresponding landing page.
| | 01:29 | To do that, we change our primary
dimension here back to keywords and we select
| | 01:33 | our secondary dimension to be the landing page.
| | 01:37 | Now we can see a list of the different
keywords that people searched on, as well
| | 01:40 | as the landing page that they
landed on our site from that keyword.
| | 01:43 | Let's take a look at our non-branded keywords.
| | 01:46 | So we'll apply an advanced filter to
exclude the keywords that matches our
| | 01:50 | branded searches. In this case,
we're going to click on advanced filter,
| | 01:54 | Exclude > Keyword. In this case we're
going to use a regular expression so that
| | 01:58 | we can put multiple keywords in a single box.
| | 02:00 | In our case we're going to put cardinal,
and then that vertical pipe is going to
| | 02:04 | mean and/or, and path. That should take
care of most of our brand of keywords.
| | 02:08 | For the most part, it looks like Google
has done a pretty good job of matching
| | 02:11 | keywords to the relevant page, although
I am a little intrigued when I see this
| | 02:15 | one here, loss aversion,
that goes to our homepage.
| | 02:18 | This might be something
I need to take action on.
| | 02:19 | I need to do some work on my SEO to
make sure Google, Bing, and the rest aren't
| | 02:23 | confused about what my pages are really about.
| | 02:25 | Let's take a look at another real-life example.
| | 02:27 | Perhaps I am in the process of
deciding which cities to hold our next
| | 02:31 | Seminar for Success Google Analytics
Live Trainings, and I want to see where
| | 02:34 | there is the most interest.
| | 02:36 | One thing I could do is find
keywords that match that topic.
| | 02:39 | I am going to set a secondary
dimension of city, and I am going to use
| | 02:42 | inline filters to restrict the keyword set to
just the things that have to do with training.
| | 02:46 | So keeping primary and this is
the keyword. I am going to change my
| | 02:50 | secondary dimension here
| | 02:51 | based on Visitors to City.
| | 02:52 | I am going to change my advanced
filter to restrict the keywords set by
| | 02:57 | including keywords that
are just related to seminars.
| | 03:01 | So in this case we'll obviously have
words like seminar, but I also want to
| | 03:04 | capture some other words around training,
| | 03:05 | so I am going to again use a regular
expression with that vertical pipe bar
| | 03:09 | to include more than one word inside the box.
Click Apply and we'll update our report.
| | 03:15 | What we see here is quite interesting.
| | 03:16 | Keywords that have to do with training
are centered around these cities, and so
| | 03:20 | we see Vancouver, Rockville, Franksville
all have a significant amount of visits
| | 03:24 | of people looking for that type of training.
| | 03:25 | These might be potential candidates
for us to hold future seminars. And we've
| | 03:29 | looked how to isolate traffic,
modify our dimensions, and add secondary
| | 03:32 | dimensions, but we can go even further.
| | 03:34 | Pivot tables allow us to take
slicing and dicing to the extreme.
| | 03:37 | Let's say I want to look at which
keywords are being searched on via the
| | 03:41 | search engines and see if there
are any differences in countries.
| | 03:44 | I want to evaluate the quality of
traffic as well as the quantity, to see if
| | 03:48 | perhaps traffic from other parts of
the world are more or perhaps less likely
| | 03:52 | to stay on our site.
| | 03:53 | So we're going to choose
Bounce Rate as our secondary metric.
| | 03:55 | Let's go ahead and do that.
| | 03:56 | First thing I need to do is clear my advanced
filter and select Pivot from the View dropdown.
| | 04:02 | Okay, now I see a list of keywords for my site.
| | 04:04 | I am going to set the
Secondary dimension here to be Country.
| | 04:07 | Now remember, I wanted to see Visits,
but I also want to see performance, so I
| | 04:13 | am going to set the secondary here to
be Bounce Rate. And lastly, I already have
| | 04:18 | Keywords as my primary dimension,
so I am going to pivot by source.
| | 04:21 | So what this shows me is people who
searched on these keywords in these
| | 04:26 | countries from these sources have these results.
| | 04:29 | From visits in the US who searched for
cardinal path, I see 1,600 visits in total,
| | 04:33 | with an average of Bounce Rate of 35%.
| | 04:36 | When I break it down by individual
sources here, I can see Google sent a vast
| | 04:39 | majority of the visits and had a 35%
bounce rate. Bing sent less visits but had
| | 04:43 | a better Bounce Rate, et cetera.
| | 04:45 | I can see that for one of these
different keywords, broken down by each
| | 04:48 | individual source and country. If I
want to break this down even further, I can
| | 04:53 | come back to my advanced filter and I
can remove those branded keywords. And I am
| | 04:57 | going to Exclude > Keywords > Matching
Regular Expression, and I am going to put in
| | 05:02 | my branded keywords.
| | 05:04 | In other words, these are words where
it's going to indicate that someone is
| | 05:06 | already familiar with my
brand, products, services.
| | 05:09 | As you can see, we've got an
incredible amount of data and we've sliced and
| | 05:14 | diced that down to get
very granular information.
| | 05:16 | One thing I want to point out:
| | 05:18 | when you start slicing and dicing
data down this finely, you do need a
| | 05:21 | fair amount of data to actually
populate this report and create segments
| | 05:24 | with meaningful data.
| | 05:25 | So while it may be interesting to look
at the number of users who converted on
| | 05:28 | a goal that came in on a specific
organic keyword on an iPhone from Minneapolis
| | 05:32 | in the last two days,
| | 05:33 | you're going to need to have visitors
who actually fall into that segment in order
| | 05:36 | for it to be meaningful.
| | 05:38 | Combining segmentation and dimensioning
allows us to quickly and deeply segment
| | 05:42 | our data to get answers to even the
most difficult analysis questions.
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|
|
7. Sharing OptionsWhy share data?| 00:01 | We talk a lot about evaluating the
value of things in analytics: which traffic
| | 00:05 | sources are more valuable, which
keywords are more valuable, and so on.
| | 00:09 | We also talk about the value
of finding actionable insights.
| | 00:12 | As an analogy, we can think about
the value of an acre of land. Now when
| | 00:16 | compared to big cities or oceanfront
property, an acre of this desolate patch
| | 00:20 | of land in West Texas may not be
valuable, relatively speaking, unless we find a
| | 00:25 | treasure buried deep below it.
| | 00:27 | Now in this case we're talking about the
buried treasure of oil. In our web sites
| | 00:30 | we talk about those valuable
insights buried deep our data.
| | 00:34 | In both cases there is value there, but
it's not enough that it merely exists;
| | 00:38 | we need to get it into the hands of
someone who can actually do something with
| | 00:41 | it so we can reap the rewards of uncovering it.
| | 00:45 | In the case of oil, it's getting it
to retailers who can turn it into cash.
| | 00:49 | In the case of our analytics insights,
it's getting that information distributed
| | 00:52 | and in use within our organization.
| | 00:55 | Collecting the data is obviously
important, and uncovering insights is even
| | 00:58 | better, but we need to complete the entire
cycle to really take it to the next level.
| | 01:02 | Actionable analytics is the goal. In
order to take action, we need to get it out
| | 01:06 | of the database and into
the hands of the decision makers.
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| Managing user accounts and profiles| 00:00 | Getting data into the hands of the
right people is critical, and it may be as
| | 00:04 | simple as giving those people who
need the information access to the right
| | 00:07 | profiles here in Google Analytics.
| | 00:08 | We can do that from the User
Management section in the Profile settings.
| | 00:12 | We can use these users areas to
grant access, but we can also use it to
| | 00:15 | restrict access in the case that certain
profiles should only be seen by certain users.
| | 00:20 | Let's take a look.
| | 00:21 | From any page within the reporting
interface, we can click on this little gear
| | 00:24 | icon in the top-right corner of the
interface and it will bring us to the
| | 00:27 | settings for that profile.
| | 00:28 | But now that we are in this screen,
let's actually use these breadcrumbs up here
| | 00:31 | to back up a step or two.
| | 00:33 | We want to be able to see all the
Google Analytics accounts that are available
| | 00:36 | to this particular login.
| | 00:37 | So here in this table we see the list
of Google Analytics accounts that I have
| | 00:40 | access to, and the column on the right
is going to indicate whether my role is
| | 00:44 | that of an administrator or
user for that particular account.
| | 00:47 | Let's click on Cardinal Path, which is
one where this user has admin access.
| | 00:51 | Here I'll see a list of all the web
properties as well as the roles for those
| | 00:54 | individual web properties.
| | 00:56 | Let's click up here on the Users
tab and see the different users that
| | 00:58 | are available here.
| | 00:59 | Here we see a mix of users and
administrators, and we have the ability over here
| | 01:03 | to edit the settings for each of these
different logins to change the type of
| | 01:06 | access that they have. And since I am
an administrator, I also have the ability
| | 01:10 | to delete any of these accounts.
| | 01:12 | We can use the Settings button up here
to show us which profiles a user-level
| | 01:15 | account has access to.
| | 01:16 | Let's take a look at the
settings CP reporting in Cardinal Path.
| | 01:20 | At the top of the page, we see the
Email address, First name, and Last name of
| | 01:24 | the user, if we have entered that in,
and two radio buttons which will allow us
| | 01:27 | to quickly change between
administrative access and user access.
| | 01:30 | If I want to change this account to
have admin access, I can just click this
| | 01:33 | radio button here and hit Save.
| | 01:35 | But let's take a look at
the user-level options first.
| | 01:37 | On the left-hand side, we see all the
web site profiles that are available but
| | 01:41 | not yet selected for this user.
| | 01:43 | On the right side, we see the profiles
that the user currently has access to.
| | 01:47 | In this case we see that this user, CP
reporting, has access to our BTOS profile, a
| | 01:52 | profile for the engage subdomain,
a profile for training subdomain, the raw/un-
| | 01:56 | filtered profile that we
use as a backup, et cetera.
| | 01:58 | If we wanted to, we can add another
profile by selecting that profile on the
| | 02:02 | left-hand side, click the Add button, click
Save, and now they have access to that profile.
| | 02:07 | We also have the option of
adding a new user entirely.
| | 02:09 | In this case we simply click on
the +New User button at the top.
| | 02:12 | We put in the email
address of the Google account.
| | 02:14 | Now remember, this has to be a Google
account, but it doesn't necessarily have
| | 02:18 | to be a Gmail account.
| | 02:20 | These are in the admin radio boxes are
the same as before, and we can add and remove
| | 02:24 | profiles for a user-level
account just like before.
| | 02:26 | Before we click on Administrator here, I
want to say, be very careful of how many
| | 02:30 | account administrators you have, and
make sure that a person really needs to be
| | 02:34 | an account administrator before
selecting them to be so, because they have
| | 02:37 | almost deity-like powers in Google Analytics.
| | 02:40 | They can delete profiles, delete users;
| | 02:42 | in fact, they can delete
the entire account itself.
| | 02:44 | Worse yet, at this point there is no
audit trail or any other way to figure out
| | 02:48 | how those accounts or profiles were deleted,
| | 02:50 | so you want to be very careful with
who you allow to be an administrator.
| | 02:53 | To add the account, we go ahead
and click the button down here.
| | 02:57 | One thing to note: if I would have
tried to add a user that does not have a
| | 03:00 | Google account, let's just say, or
let's try some fake user, @cardinalpath,
| | 03:03 | as soon as I try to save the changes,
it will tell me that user has an unknown
| | 03:09 | email address, which means
they do not have a Google account.
| | 03:11 | I also highly suggest you to avoid
the situation of Gmail when possible.
| | 03:15 | You can see if I cancel
this and go back to user list
| | 03:17 | that I do have a Gmail account in here.
| | 03:20 | It is possible to have a Gmail account,
but in this case I have a Gmail account
| | 03:23 | here that is claiming to be me, and
anyone who looks at this list would
| | 03:27 | probably assume that I registered that
Gmail account, but it may be the case
| | 03:30 | that I actually didn't.
| | 03:31 | Anyone can actually go to Gmail,
register an account with my name and if they
| | 03:35 | were able to get that on this list--
perhaps they were a previous employee,
| | 03:38 | perhaps there were an intern--after
they left the company it wouldn't look odd
| | 03:42 | to see my name in the list like this,
| | 03:43 | but you don't actually know if the person
behind that Gmail account is me or someone else.
| | 03:48 | It's much safer to use your corporate
email addresses in this, and having that
| | 03:51 | policy across your
organization is usually a good idea.
| | 03:55 | The changes that we have made here
take effect immediately, so as soon as you
| | 03:58 | add this user, that they will be able to
log in and see all the reports you have
| | 04:00 | give them access to.
| | 04:01 | Adding users allows us to share the
benefits of Google Analytics, but put some
| | 04:05 | thought into who you invite
and which permissions you grant.
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| Emailing reports| 00:00 | Let's face it.
| | 00:01 | You and I may think that pouring over this
data and pulling out insights is fantastic,
| | 00:05 | but not everyone who needs this
data will have the skills, interest, or
| | 00:08 | diligence to log in, retrace our steps through the
interface and get those same detailed reports.
| | 00:13 | But this doesn't mean they need
the information any less, and maybe
| | 00:16 | it's your job to get it to them, or
maybe you have a staff meeting every Monday
| | 00:19 | afternoon and it's in your best
interest to make sure everyone has the updated
| | 00:23 | data in their inbox come Monday morning.
| | 00:25 | In the exporting video, we saw how
you can accomplish this by exporting and
| | 00:28 | attaching it to your email, but we
could also let Google do that for us.
| | 00:31 | In the previous movie, we saw how to
build a list of non-branded keywords that
| | 00:35 | have low bounce rates.
| | 00:37 | That's an interesting list, and
it's a good candidate to be emailed.
| | 00:39 | The great thing about this is that the
advanced filters we apply in the sort
| | 00:43 | order that sorted to show keywords
with low bounce rates will be maintained
| | 00:46 | in the email version.
| | 00:47 | If we want to do this, we
simply first configure the report.
| | 00:49 | So let's go ahead and do that.
| | 00:51 | We set up some advanced filters here
to Exclude branded Keywords. We are
| | 00:54 | also going to set this is Visits >
Greater than 4. Click apply.
| | 01:00 | Okay, we have got some
non-branded keywords here.
| | 01:04 | I am going to sort this in term of
Bounce Rate to show me the keywords that
| | 01:07 | have a low bounce rate.
| | 01:08 | This is an interesting list.
I want to go ahead and email this.
| | 01:10 | Scroll up to the top. Click the Email button.
| | 01:13 | I am going to see a dialog box here that gives
us some options on how we want to set this email up.
| | 01:17 | First we are going to enter the
email address we want to send it to.
| | 01:19 | We can edit the subject line if we'd like.
| | 01:21 | Next to the Attachments we see our
options for file types--CSV, TSV, PDF--and
| | 01:26 | then the name of the report
that we are actually sending here.
| | 01:28 | We can also select the frequency of the report.
| | 01:31 | In this case, we want it to be Weekly.
| | 01:32 | Under the Advanced Options, we see a dropdown.
| | 01:34 | We can define how long we want these
reports to continue to send every week.
| | 01:38 | We will set this to be active for 6
months, and we can include some text
| | 01:41 | explaining what needs to be done with
this report, what we hope to get out of
| | 01:44 | it, or anything else that you like to
put in here in the body of the email.
| | 01:48 | Click Send and you are done.
| | 01:48 | And one thing I want to point out here
is that if we decide later on we want to
| | 01:51 | add another report to that email, we
find that report, click the Email button,
| | 01:55 | and at the bottom of this Email
Report dialog box, we have this option down
| | 01:59 | here to Add to existing email.
| | 02:01 | And this sounds like a minor
thing, but it can be a big help.
| | 02:03 | When your boss comes and asks about
the numbers for that new social media
| | 02:06 | campaign, instead of having to
recreate the entire email all over again, we
| | 02:10 | simply add this report onto the
already existing email, which is already
| | 02:14 | scheduled to send out.
| | 02:15 | Email is one of the most
underutilized but useful features in
| | 02:18 | Google Analytics.
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|
|
8. Visitor ReportsUnderstanding who is visiting a site| 00:01 | The reports under the Visitors tab
give us the most sought-after information
| | 00:04 | about the number and type of visitors,
as well as some basic information about
| | 00:07 | the nature of their visit.
| | 00:09 | Reports here are found under the left nav
under Visitors, starting with the Overview.
| | 00:12 | The overview gives us the 10,000-foot high-level
metrics that are contained in this section.
| | 00:17 | At the top, we will start with the Data
Over Time graph, which can default to
| | 00:20 | Visitors. We can see lots of things
here: Visitors, Page View, Bounce Rate.
| | 00:23 | As we scroll down, we see some of the
mini-reports here that are some of the
| | 00:27 | more detailed reports found on the
left-hand nav, as we dig into the
| | 00:30 | different Visitors reports.
| | 00:31 | There are some insights to be gained here in
the Overview by using date-range comparisons.
| | 00:35 | For example, in this case I will
compare one month to the previous month.
| | 00:38 | Click Compare to past.
| | 00:39 | I want to select the month of June.
| | 00:40 | Click Apply. So as we'll see over on the right-hand
side here, these months are incredibly
| | 00:47 | similar here, for the most part.
| | 00:49 | In fact, the lines are on top of each
other until you we go all the way back
| | 00:51 | here towards beginning of the month,
| | 00:53 | we can see there is severe divergence,
and one month has considerably more visits
| | 00:56 | along this couple-day period than the others.
| | 00:59 | And these large
departures are immediately obvious,
| | 01:01 | but for the most part, this won't be
the report that you spend a lot of time
| | 01:05 | doing real or deep analysis.
| | 01:07 | The real insight from the visitors come
from these individual reports over here,
| | 01:10 | inside the navigation.
| | 01:12 | This overview really exists just for
that high-level health check and gives us a
| | 01:16 | starting point to begin our analysis.
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| Analyzing location data| 00:00 | One of the most illuminating segments
to consider about our visitors is simply
| | 00:03 | where they are from, geographically
speaking, when they visit our site.
| | 00:06 | This segment we almost do in the
real world without thinking about it.
| | 00:10 | Where the web is accessible to the
whole world with ease, we know that
| | 00:13 | the market segments of Europe or
Asia may interact with our site
| | 00:16 | differently, especially if we ship
products to certain places or if we
| | 00:19 | have language dependencies.
| | 00:21 | Google Analytics uses some
sophisticated technology to determine, as accurately
| | 00:25 | as possible, where the geographic
location of your IP address is and records that
| | 00:29 | data as part of your visit.
| | 00:30 | We navigate to the Location report
in the Audience, sometimes known as
| | 00:34 | the Visitor section.
| | 00:35 | Here we can see a graphical
representation of the map of the world, where the
| | 00:38 | darker areas represent more visits.
| | 00:41 | We start with a world view, and we
can drill down into the country.
| | 00:44 | From there, we can drill into the states
or provinces, which are known as Regions
| | 00:48 | in Google Analytics.
| | 00:50 | And from here we can even drill
into individual cities as well.
| | 00:54 | Below each of these maps will be a full
table that gives us all the information
| | 00:57 | that's represented in the map above,
down here in the table format, along with
| | 01:01 | all the standard metrics, including
usage, ecommerce, goals, et cetera.
| | 01:05 | For example, I can see the number of
visits from each individual city and if
| | 01:09 | we zoom out, I can see that
California brings twice as many visits than the
| | 01:14 | next nearest state.
| | 01:15 | The interactivity of this
report makes it very popular.
| | 01:18 | But as analysts looking for
actionable metrics, we need to consider what this
| | 01:21 | map is really telling us and how we
can get to the really interesting stuff.
| | 01:25 | For example, unless you have a web
site that targets visitors from a very
| | 01:29 | specific region, your map is going
to look very similar to this. Why?
| | 01:33 | Well, what other map does this remind you of?
| | 01:35 | What we see here is that very often
our visitor map overlay in the country
| | 01:39 | level will look almost exactly like
this census map of population density.
| | 01:43 | Now this makes sense
| | 01:44 | because where there are more people
living, those are the places where they're
| | 01:47 | more likely to visit our site from.
| | 01:48 | So it's no surprise that our darkest
states--California, New York, Texas,
| | 01:53 | Florida, Illinois--where the largest
populations live are also the darkest ones on our map.
| | 01:58 | There's very little actionable about a
map that looks largely the same from one
| | 02:01 | web site to the next web site,
| | 02:03 | and almost all of our web sites
are going to look like this map.
| | 02:07 | One way to get actionable analytics out of this
report is to take advantage of a feature
| | 02:11 | here that many folks don't even
realize it's there, the ability to use other
| | 02:14 | metrics to populate this map than visits.
| | 02:16 | For example, if you are interested in
engagement, you can change this map from
| | 02:20 | Visits to Time on Site or Pages/Visit.
| | 02:24 | Here we get an entirely different map,
which gives us an idea of our ability to
| | 02:27 | retain and engage visitors who
come from this particular area.
| | 02:31 | Here we see that California is
still somewhat dark, but now we see some
| | 02:34 | newcomers on here, such as Montana and
Vermont. Or we can switch this over to a
| | 02:39 | conversion rate metric and see that
the states with the most visitors don't
| | 02:42 | necessarily convert the most. Or if
we looked at something like a value per
| | 02:45 | visit, we could be looking at places
that would be very profitable for us to do
| | 02:49 | cost-per-click advertising, since the
value per visit may be higher. Overall
| | 02:53 | there may be less population,
| | 02:54 | there may be less visitors, but from a
profitability point of view, it may be higher.
| | 02:58 | One very useful way to use geographical
reports is to get more information about
| | 03:03 | a direct visit which usually
doesn't have very much information.
| | 03:06 | In this case, someone who has just
typed in our URL directly, perhaps after
| | 03:09 | seeing a newspaper advertisement.
| | 03:11 | Hypothetically, let's say we
weren't getting much of any visits from
| | 03:13 | Indianapolis and St. Louis regions until we
advertised in two local newspapers.
| | 03:17 | Now while we have no absolute way of knowing
these visits came from those who saw the
| | 03:22 | ads, comparing the increase in traffic
with the ad publication dates, we can get
| | 03:26 | a pretty good idea that this traffic
was likely due to viewing these ads.
| | 03:29 | There is one more very important
piece of insight we can gain from this.
| | 03:32 | Not only do we see where there are bit
more than a thousand visits from each of
| | 03:37 | the cities; we can look beyond the
visits to the value of those visits.
| | 03:42 | By looking what happened during those
visits, we can see how much each was worth.
| | 03:45 | And we can see now that in this example
the number of visits was approximately
| | 03:49 | the same, but the Indianapolis ad was
far more successful at getting the right
| | 03:53 | people--in other words, those who
spent money to come with the site.
| | 03:56 | As potentially illuminating is this
report is, one limitation we need to be
| | 04:00 | aware of with this report is that
it is by no means 100% accurate.
| | 04:02 | If you are connecting through a network,
such as a corporate VPN, the IP address
| | 04:06 | that Google sees may actually be
located in another city, such as the
| | 04:10 | headquarters of your company, rather
than where you're located when you created
| | 04:13 | the visit, so that can
result in a false location.
| | 04:16 | It's also possible Google simply was
unable to determine where the visitor
| | 04:19 | was located, and therefore unable
to put that visitor in a particular
| | 04:22 | geographic segment.
| | 04:24 | Now the visits still occurred.
| | 04:25 | And that needs to be accounted for, so
let's simply put into a category called not set.
| | 04:30 | In this case, as we look at the data
table, if we increase the number of rows,
| | 04:34 | we can scroll down here, and we see that
the number 25 state here is actually not
| | 04:38 | set. 2300 of these visitors could not
be put in a particular state category.
| | 04:43 | The map overly report can be a
fantastic source of information once we learn to
| | 04:47 | get pass the less useful aggregate
metrics and into the details, where the
| | 04:50 | true insights lie.
| | Collapse this transcript |
| Using language identification to segment users| 00:00 | Another important clue about the
segments of visitors visiting our sites is
| | 00:03 | found in the Languages
report within the Visitors tab.
| | 00:06 | Google Analytics is able to determine
the primary language set in the browser
| | 00:10 | being used by the visitor, and
everything those users do during that visit is
| | 00:13 | going to be tallied as
belonging to that language setting.
| | 00:16 | Language is determined by your
computer's setting, not by your IP address, your
| | 00:20 | geographical location, or settings
in your Google Analytics account.
| | 00:23 | This is why you'll often see variations
of the same language in reports, because
| | 00:27 | different operating systems and
browsers report the same language in different
| | 00:30 | ways, such as en-us, en, en-gb, are
all different forms of English language.
| | 00:37 | Google Analytics will report it as it's given.
| | 00:39 | So if your browser reports that your
computer's primary language is Klingon,
| | 00:42 | that's what's going to
appear here in the report.
| | 00:45 | One interesting way we can take action
from this report is to determine if there
| | 00:48 | are significant segments of users who
may benefit from a version of the site
| | 00:52 | localized to their language.
| | 00:53 | For example, a client of ours was
trying to determine whether it be worth
| | 00:56 | the investment to create a Spanish-
language version of their existing site.
| | 00:59 | What we could see was that while
the majority of visitors to the current
| | 01:03 | English-language version of the
site did have US English as the primary
| | 01:06 | language, when we flip over to the Goal
Conversion tab, we see that even on the
| | 01:10 | English-language site, visitors that
was Spanish set as their primary language
| | 01:14 | were converting at nearly a rate in
double the English-speaking segment.
| | 01:17 | The client took this as strong
evidence that our site appeals to that
| | 01:20 | segment and would likely benefit from
making a Spanish-language version of
| | 01:24 | the site available.
| | 01:25 | Like many reports, the Languages
report isn't one you'll visit daily,
| | 01:28 | but when you're trying to answer some
specific questions, it can provide valuable
| | 01:31 | insights and segmentation
capability not found elsewhere.
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| Differentiating new users from returning users| 00:00 | One important clue we can utilize as an
analyst is whether a visitor is familiar
| | 00:04 | with your site through previous
interactions or whether this is the very first
| | 00:07 | time they've visited the site.
| | 00:08 | Through the use of browser cookies,
which are just little bits of information
| | 00:11 | your browser keeps on your computer to
remember your previous visits, Google
| | 00:14 | Analytics is able to determine whether this
person has previously been to your site before.
| | 00:19 | That information is held in the New vs.
Returning report in the Visitors tab.
| | 00:23 | Here we see the visitors broken into
their respective segments: New and Returning.
| | 00:27 | As we can easily see in both this
table and the corresponding pie chart, the
| | 00:31 | overwhelming majority of visitors to
the site are first-time, or new, visitors.
| | 00:34 | I am looking at this data and you may
be tempted to think returning visitors
| | 00:37 | here in green are approaching
becoming an insignificant segment of traffic.
| | 00:41 | Once again, I would caution that we
have not revealed the whole story because
| | 00:45 | we're just examining a single
metric in isolation, visitors.
| | 00:48 | When we change to the Ecommerce tab
and use Revenue as our metric, the
| | 00:52 | chart updates from being based on
visitors to show how much revenue each
| | 00:56 | segment accounted for.
| | 00:57 | You can see that despite making up just
a sliver of the visitors, the Returning
| | 01:00 | visitors actually account for the
majority of our revenue, which sheds an
| | 01:04 | entirely new light on that segment.
| | 01:05 | It's important to peak under the
hood a bit and see when Google Analytics
| | 01:08 | considers someone a new visitor.
| | 01:10 | It essentially boils down to whether
or not they have an existing set of
| | 01:14 | cookies from a previous
visit still in their browser.
| | 01:17 | This means that if someone uses a
different browser, they will create a whole
| | 01:20 | new set of cookies and be considered
a new visitor in the eyes of Google
| | 01:23 | Analytics, even if they've
already visited the site.
| | 01:26 | Also, if you delete your cookies, Google
Analytics will have no way to know that
| | 01:29 | you where their prior.
| | 01:30 | Also, if you would have visit the site
from your work computer, then go home and
| | 01:34 | visit again and then visit again from
a different computer, even in the same
| | 01:37 | house, such as your kid's computer,
those would all have their own fresh new set
| | 01:42 | of cookies and therefore a single
visitor will be counted as three new visits.
| | 01:46 | While you can't assume new or returned
visitors are necessarily more valuable to
| | 01:50 | your site than the other--
| | 01:51 | that depends on your individual site--
| | 01:52 | it is critical to understand the
breakdown of these two very different segments:
| | 01:56 | how many there are of each, how
valuable they are, and how they may be
| | 01:59 | changing over time.
| | Collapse this transcript |
| Understanding visitor loyalty vs. recency| 00:00 | In the previous few reports we've
introduced the idea of tracking whether users
| | 00:03 | are coming back to the site and how
engaged they are during that visit.
| | 00:07 | Now we'll look to segment that data
into histogram-styled buckets to try and
| | 00:10 | gain more insight into what's actually
happening when those visitors come to our site.
| | 00:15 | The first report here is called the
Frequency & Recency report, which gives us an
| | 00:18 | idea of how many times users are
coming back to the site, using the Count of
| | 00:22 | Visits metric, as you can see on here
on the horizontal bar. And we previously
| | 00:26 | talked about New vs. Returning, but this
attempts to shed a little more light on
| | 00:29 | not just if they're
returning, but how many times.
| | 00:32 | As we saw in the New vs.
Returning report, only 10% in total are
| | 00:36 | returning visitors.
| | 00:37 | What we see here is that half of them, 5%,
have been to the site one or more times.
| | 00:41 | That quickly drops off and as we see
for this site, folks don't tend to come
| | 00:45 | back multiple times.
| | 00:46 | So what this tell us is that we better
make sure we do everything we can to keep
| | 00:49 | them on the site once we get them here.
| | 00:51 | This also gives us info about our sales cycle.
| | 00:53 | While people rarely buy a car on
impulse, they may research for weeks.
| | 00:57 | This graph tells us that for this site
we need to make the sale now because the
| | 01:01 | odds are, they aren't coming back.
| | 01:03 | Now, because this all depends on the
nature of your business, this report will
| | 01:06 | vary widely, and yours may not look like that.
| | 01:09 | We can see a slightly different report
here if we switch to another site that's
| | 01:12 | going to look a little bit
different. Let's do that.
| | 01:15 | We still see the majority of visits
coming in the first and second row, but we
| | 01:19 | also see a bump down here, a
little bit more than the halfway through.
| | 01:22 | It's important to note that this is not
necessarily that more the visitors are
| | 01:25 | coming back around this time; it's that
this is the part of the histogram where it
| | 01:29 | starts to group larger and
larger quantities of time together.
| | 01:32 | We've got 5 here, 10 here. We get
down here, visits 100 through 200 are all
| | 01:36 | grouped into one row.
| | 01:37 | Now if you see large numbers clustered
down here all the way at the bottom, be
| | 01:41 | sure to check in if you're
counting internal traffic.
| | 01:44 | In other words, people within your own
organizations, they may be skewing your
| | 01:47 | stats by hitting the site on a daily basis.
| | 01:49 | This is especially true if you have an
organization where the homepage of the
| | 01:52 | browser is set to load the company homepage.
| | 01:54 | A similar but slightly different
metric that gives us a window into our sales
| | 01:57 | cycle is to examine days since last visit.
| | 02:00 | So we move from Count of Visits over
here to Days Since Last Visit, which shows
| | 02:04 | us how long ago the previous visit was.
| | 02:06 | So instead of the number of returned
visitors, we're looking at the time of
| | 02:09 | those visits and how
close or far apart they were.
| | 02:11 | As we see the bump in traffic halfway
down where the bucket start to grow into
| | 02:15 | larger numbers, also keep in mind
for Google Analytics to track this
| | 02:19 | accurately means the cookies must be intact.
| | 02:22 | The chance that a person deletes their
cookies within a day or two is relatively low.
| | 02:25 | But as we start to approach a year
or even longer, the chances that they
| | 02:29 | haven't changed computers, location, or
cleared their cookies in any other way
| | 02:33 | is substantially less.
| | 02:35 | The next report section down here is
the Engagement reports, and the Visitor
| | 02:38 | Duration metric is a highly
illuminating report because it exposes just how
| | 02:42 | misleading these averages could be.
| | 02:44 | It's well known within user experience
circles that most people spend less than
| | 02:47 | 10 seconds on a page, which
is backed up by this data.
| | 02:50 | We can see that by far the biggest
bucket of visitors spend very little time
| | 02:54 | on the site, and they're probably
contributing towards the bounce rate.
| | 02:57 | But if you recall from the overview
reports, which we'll glance at back here,
| | 03:01 | the average time on site down here was
a minute and 51 seconds, because there
| | 03:05 | are just a few people that spend a
great deal of time on the site, so this
| | 03:09 | average is misleading.
| | 03:10 | However, with their analytics we're
really trying to tell a story about our
| | 03:13 | users through the data.
| | 03:14 | So going back to the Engagement report,
we see it would be very misleading to
| | 03:18 | believe that the majority of people
actually do spend a minute and 51 seconds on
| | 03:22 | the site due to that average. And we
can see here that the majority are in the
| | 03:26 | less-than-10-second bucket.
| | 03:28 | Worse yet, those folks that spend a
great deal of time on their site and distort
| | 03:31 | the average, oftentimes they're
internal to the organization, or they're folks
| | 03:35 | looking from home, or other noncustomer,
non-external visitors, and we generally
| | 03:39 | aren't focused on them in
our web analytics analysis.
| | 03:41 | The Page Depth metric also in this
engagement report is similar and that
| | 03:45 | examines the engagement of the
visits, but rather than focus on time,
| | 03:49 | it focuses on the number of page views.
| | 03:51 | Again, this highlights the problem with
averages. If we look up at the Google store profile,
| | 03:56 | in the Overview section we can see the
average number of page views for this site
| | 03:59 | as a whole was over 3 pages per visit.
| | 04:01 | However, if we look at the engagement
report, we see here that three-quarters of
| | 04:06 | the visitors see two pages or less,
the majority of those just one.
| | 04:09 | So assuming that most people see over
three pages per visit because that's what
| | 04:13 | the average is would be a
major mistake in our analysis.
| | 04:16 | Visitor behavior reports can shed a
great deal of insight into how visitors are
| | 04:20 | using our site, both over the course
of time and within a specific session.
| | Collapse this transcript |
| Comparing data according to visits, visitors, and page views| 00:00 | Throughout this section we talked
about several different concepts in web
| | 00:03 | analytics measurements, such as
visitors, visits, and page views.
| | 00:06 | These concepts are used throughout many
of the reports in Google Analytics, and
| | 00:09 | it's worth taking a moment to talk
about what they really mean and put some
| | 00:13 | formal definition around them.
| | 00:14 | At the highest level, we have an actual
human being who sits down to use a computer.
| | 00:19 | This is known as a visitor, and
through cookies and other technologies,
| | 00:22 | Google Analytics tries to keep track
of how many unique visitors see the
| | 00:26 | site in any given time period.
| | 00:28 | But each person or visitor might come
to the site more than once, which would
| | 00:31 | result in multiple visits by that one visitor.
| | 00:34 | For example, if this person came to the
site three times throughout the month, as
| | 00:38 | shown here, the reports would show one
unique visitor but three unique visits.
| | 00:43 | A new visit starts anytime you either
open a new browser or let more than 30
| | 00:48 | minutes lapse since the
last page you saw on that site.
| | 00:50 | Additionally, a new visit can
start any time you come back through a
| | 00:53 | different campaign.
| | 00:54 | For example, if a visitor were to click
on two different AdWords ads, those would
| | 00:58 | be two different visits.
| | 01:00 | So if you go have lunch and then come
back to the site afterwards and continue
| | 01:03 | browsing, that will look like a new
visit to the site, since it's been more than
| | 01:07 | 30 minutes since your last page view.
| | 01:10 | Visits are also known as sessions.
And finally, each of those visits to the
| | 01:13 | site can browse through multiple pages
or page views during that particular visit.
| | 01:19 | It is important to note that these
metrics are estimates and based largely
| | 01:22 | on browser cookies.
| | 01:23 | This means that if you close your
browser or even turn off your computer and
| | 01:26 | come back weeks later, the cookies will
still be there and Google Analytics will
| | 01:30 | recognize you as that
unique visitor from before.
| | 01:33 | But if you move to your iPad or
visited from your smartphone or even from
| | 01:37 | another browser on the same computer,
you would be seen as a new visitor with a
| | 01:41 | fresh set of cookies.
| | 01:43 | Each of these metrics has value
and will be used in different ways.
| | 01:46 | For example, visitors can
give us an idea of our reach.
| | 01:49 | Visits will tell us about loyalty and
so-called stickiness of the site, which
| | 01:52 | means how often do people come back,
and how willing are they to come back to
| | 01:55 | the site over and over,
| | 01:57 | while page views tells us about how
engaged visitors are during that visit.
| | 02:02 | As you look through the reports in
Google Analytics, think about what these
| | 02:05 | metrics mean and which is most
appropriate for the question you're
| | 02:08 | currently trying to answer.
| | Collapse this transcript |
| Sorting data by browser capabilities| 00:00 | When making design considerations and
trade-offs on how we build our sites,
| | 00:04 | it's useful to be armed with
information about the type of computers and
| | 00:07 | browsers that our users have.
| | 00:08 | This next set of reports can offer up a
great deal of information to help us design
| | 00:12 | and build an optimal site.
| | 00:13 | We are located here in the Visitors >
Technology > Browser & OS reports.
| | 00:18 | The individual reports and metrics are
differentiated here with a horizontal bar,
| | 00:22 | beginning with Browsers.
| | 00:24 | It tells us the different browsers
that are used in our site, and one of the
| | 00:26 | most common mistakes in site design is to
create a site that has problems working
| | 00:30 | in all the different browsers.
| | 00:31 | But even if your site doesn't work at
all on a particular browser, users of that
| | 00:35 | browsers won't know it until
they actually visit the site.
| | 00:37 | So you can't rely on the Visitors
column to tip you off, particularly if you've
| | 00:41 | a high percentage of new users, because
while a visitor may not choose to return
| | 00:45 | to a site that doesn't display properly,
a new user would have no knowledge of
| | 00:49 | that issue, never having
visited your site before.
| | 00:51 | However, we might see evidence of
that problem by shifting over here to the
| | 00:55 | Goals or Ecommerce tab
| | 00:57 | and looking for any particular
browsers with suspiciously low values.
| | 01:00 | Scrolling down here, we see the table of
browsers, and I can change my metrics
| | 01:04 | here to include Revenue.
| | 01:06 | Now if we do see a particular browser
that's having trouble, it may mean that
| | 01:09 | they have problems viewing the site, which
prevents them from converting on our goals.
| | 01:13 | In our case, we see some
here that may be suspicious.
| | 01:16 | We have Android browser that has a
large number of visits, but absolutely no
| | 01:20 | revenue, and we do see some
interesting things in addition to that.
| | 01:23 | This being the Google Store, it may not
surprise you to see the high penetration
| | 01:26 | of Chrome, but as we look down this list,
and if we switch back and forth from
| | 01:30 | Visits to Revenue, we see a few other things.
| | 01:32 | First is that Chrome converts less
then its market share in terms of revenue.
| | 01:36 | It's 40% of our visits, yet in terms
of revenue, it was only 36%. And we see
| | 01:41 | that actually IE and Firefox do better
than their share, converting here at 32
| | 01:45 | and 25, although when it come to
visits, we've only got 29 and 17.
| | 01:48 | Now as we pointed out earlier, mobile
is all the rage, but when we look at the
| | 01:52 | revenue, we see things like Android
Browser and Opera Mini have absolutely no
| | 01:57 | revenue to their name here.
| | 01:58 | We'll note this mobile performance
and we'll remember to dig in deeper with
| | 02:01 | specific reports around those
devices in a different video.
| | 02:04 | Now along those lines, we can see
operating systems as well as the browsers by
| | 02:07 | clicking on the next link, Operating System.
| | 02:09 | Here we see that Windows users make up
the majority of visits to the site, and
| | 02:13 | it's not even close.
| | 02:14 | Now although, when we switch to Visits, we see
that Mac users make up about 8% of the visits,
| | 02:19 | they account for 22% of the revenue.
| | 02:22 | Screen colors and Resolutions are also
available, and while no one pays too much
| | 02:26 | attention to color depth, resolutions
are becoming a huge deal again with the
| | 02:30 | advent of smartphones, tablets, netbooks,
and other nontraditional form factors.
| | 02:34 | Along these same lines, there are a few
things that will stop a visitor in their
| | 02:37 | tracks as quickly as forcing them to
install or upgrade a plug-in such as Flash.
| | 02:41 | In some devices, such as pretty much any iOS
device, can't even run Flash if they wanted to.
| | 02:46 | So before you let your designer talk
you into a page that requires the latest
| | 02:49 | and greatest Flash or Java plug-in, so
be sure to check your reports here first
| | 02:53 | and see just how many users
you'll be leaving out in the cold.
| | 02:56 | One interesting insight to note is that
the most common Flash version is still
| | 03:00 | the old version, Version 10,
| | 03:02 | so that may influence our design a bit.
| | 03:03 | Also, when we are doing analysis of
these reports, don't forget to utilize
| | 03:07 | secondary dimensions.
| | 03:08 | For example, I am curious about the
Safari users, are they MacBook or iPads?
| | 03:12 | So let's drill down over here to
the browsers and click on Safari.
| | 03:16 | We see the specific browser version,
but I am not interested in that, so I am
| | 03:18 | going to click my primary
dimension here instead to be resolution.
| | 03:22 | Now I am pretty sure that this second one here,
the 768, is the iPad, but I am not 100% sure.
| | 03:28 | So I am going to take my secondary
dimension over here, drill down to
| | 03:32 | Technology, select Operating System,
and we clearly see here, the second one
| | 03:36 | was in fact the iPad.
| | 03:37 | Here we have my favorite kind of
data, indisputable and actionable.
| | 03:40 | By knowing the screen resolutions of
each particular iPhone, for example, we can
| | 03:44 | see which version of it they have when
they're visiting our site, which version
| | 03:47 | of the iPad, et cetera.
| | 03:48 | Utilizing these reports can help us
build sites that are optimally designed for
| | 03:51 | user's environments and
significantly improve the success of our sites.
| | Collapse this transcript |
| Analyzing data from mobile browsers| 00:01 | Mobile devices are becoming
increasingly important in the web analytics world,
| | 00:04 | and these two pre-segmented reports can
help us analyze these users more efficiently.
| | 00:09 | In Google Analytics, we have two
reports to help us analyze mobile traffic.
| | 00:12 | The first of these is
the Mobile Overview report.
| | 00:15 | Now, this is a simple breakdown in mobile visits:
| | 00:17 | Yes, or non-mobile visits, which are No.
| | 00:20 | This report allows us to quickly
isolate mobile traffic and evaluate that
| | 00:23 | traffic against things like bounce
rate, unit conversion rate, revenue
| | 00:27 | generated, et cetera.
| | 00:28 | Now if we use the Plot Rows
functionality by checking these check boxes,
| | 00:32 | clicking Plot Rows, we can even see how
mobile traffic versus non can be plotted
| | 00:36 | up against each other, versus
all of the traffic on our site.
| | 00:39 | Now in this case, it doesn't look like
mobile traffic contributed much to these
| | 00:42 | increases and decreases in traffic.
And if we click this over to the
| | 00:45 | Ecommerce reports and look at Revenue
generated, we're not seeing a single
| | 00:49 | penny for mobile traffic.
| | 00:50 | Now this is despite over
90,000 visits, not a single dime.
| | 00:54 | Now this might be an indicator that
we need to work on making our site
| | 00:57 | more mobile-friendly,
that something is very wrong.
| | 00:59 | This is actionable data without a doubt.
| | 01:01 | Now that's good to know, but we can
get even more detail about the mobile
| | 01:04 | traffic from this other
interesting report, the Devices report.
| | 01:08 | Here we can evaluate mobile
traffic by the device used.
| | 01:11 | We can also look at the
brand of the phone manufacturer:
| | 01:14 | Apple, Samsung, Nokia, et cetera.
| | 01:16 | We can look at the different service providers.
| | 01:18 | We can look at the Verizon,
we can look at Comcast, Sprint, Nextel, et cetera.
| | 01:22 | We can even look at things like
whether or not the device has a touch screen.
| | 01:26 | So like an iPhone, or if it's a stylus-
based phone like a Palm Pre or even a
| | 01:30 | clickwheel phone like the BlackBerrys.
| | 01:31 | The last dimension here is the operating system.
| | 01:33 | Now using this function allows us to
group together things like all the Android
| | 01:37 | phones and tablets, all the
iPads, BlackBerrys, et cetera.
| | 01:40 | Now it's probably no surprise when we
are looking at the analytics for the
| | 01:43 | Google Store site that Android
ranks at the top of the list.
| | 01:46 | However, if we just looked back at the
Mobile Device segment here, we wouldn't know
| | 01:50 | that. Apple device is ranked at the top
of the list here, but that's probably
| | 01:53 | because there are dozens of
different Android devices.
| | 01:55 | So those visits could split out
between all those individual devices,
| | 01:58 | whereas there are a fewer variations
of Apple devices, and all iPhones are going
| | 02:01 | to get grouped together.
| | 02:03 | These reports are changing rapidly as
the measurement industry attempts to keep
| | 02:06 | pace with the fast-moving mobile industry.
| | 02:08 | So there's a good chance that
these may be slightly different or have
| | 02:11 | additional features by the time you
see this, and I encourage you to explore
| | 02:14 | each link and report.
| | 02:15 | One clever recent addition is the links
to pictures of each device in the Mobile
| | 02:19 | Device Info report.
| | 02:20 | So if you can't remember if the
Motorola Xoom was a smartphone or a tablet,
| | 02:23 | just click on this little camera icon here to
launch a picture of the device and you can see.
| | 02:27 | The mobile web is increasingly
important and these reports are going to help us
| | 02:30 | understand how more mobile
users are interacting with our site.
| | Collapse this transcript |
| Using flow visualization to see common paths| 00:00 | Earlier I said that the All Traffic
Sources report was my go-to report, but this
| | 00:04 | brand-new report might just take
the crown as my absolute favorite.
| | 00:07 | We've had dozens of discussions over
the years with Google engineers about how
| | 00:10 | pathing reports that show the click stream
progression of a user are generally useless.
| | 00:15 | The problem is you have 10,000 visitors
in your analysis and 9,800 different paths.
| | 00:19 | But this report is different. And
while there's still a lot of development
| | 00:22 | going on to evolve this report
further, the earlier indication is that they've
| | 00:25 | knocked it out of the park.
| | 00:26 | As analysts, we often say we want to
understand how different groups are
| | 00:29 | interacting with our site differently:
| | 00:31 | where they go, what they do,
where they drop off, how they come in.
| | 00:35 | We're interested in questions like
how do our new users use this site
| | 00:38 | compared to our returners?
| | 00:39 | How do users in China use the
site compared to those in US?
| | 00:42 | How about people who hear about us via
the social media channels versus good
| | 00:46 | old-fashioned organic search?
| | 00:47 | So many questions and lots of reports
available, but many are indirect and unintuitive.
| | 00:52 | But now we have a report that
combines the content navigation, entrance and
| | 00:56 | exit reports, and funnels all in one
and at the same time leverages all our
| | 01:00 | campaign data and custom segmentation.
| | 01:03 | This is Visitor Flow Visualization,
and I am big fan. Let's take a look.
| | 01:06 | Come here to the Audience or
Visitors tab and click Visitors Flow.
| | 01:10 | Initially the report opens by
demonstrating the relative number of
| | 01:13 | visitors from each country.
| | 01:15 | We can see the trends and how they
navigate from one page to the next.
| | 01:18 | We can, of course, select which
dimension we want to see here.
| | 01:20 | We can group by things like source,
medium, keywords, et cetera, all the
| | 01:24 | dimensions that are available to us.
| | 01:26 | We don't have to choose Country,
although that's an interesting one to start with.
| | 01:29 | Here, the pages of your site are
represented by these boxes that are known as nodes.
| | 01:33 | Visitors and the paths and trends
that they take through the site are
| | 01:36 | represented by these individual lines.
| | 01:38 | So as we look at this, these individual
nodes over here represent the different
| | 01:42 | countries, and the paths that they
connect to represent the landing pages.
| | 01:46 | These are the first pages on the site
that those individual visitors came to.
| | 01:49 | From there, we can see how many
visitors moved on from their landing pages to
| | 01:52 | another page on the site and which
page, and we can also see how many left the
| | 01:56 | site and dropped off after
visiting a particular page.
| | 01:59 | The number of visitors that left the
web site after reviewing that page are
| | 02:02 | represented by this red
bar on the side of each node.
| | 02:05 | This is a visual representation of the
percentage of visitors that abandon the
| | 02:08 | site and didn't move on further.
| | 02:10 | Above the red bar, for the visitors
that didn't drop off, we can see on these
| | 02:13 | lines where they moved on to.
| | 02:15 | We can click on any of these
lines to highlight traffic through
| | 02:17 | those individual paths.
| | 02:19 | As we click on any of these individual
lines, it shows the previous visitors and
| | 02:22 | where they came from, who went
through that particular path.
| | 02:25 | I'll show a couple of examples
here as I highlight different lines.
| | 02:29 | If we click on one of the nodes in this
first line over here, the countries, we
| | 02:33 | can highlight the traffic through
that particular country and follow that
| | 02:36 | traffic through the site.
| | 02:37 | So in this case, I am looking at
where all the traffic in the US goes, but maybe
| | 02:40 | I want to come down here and look at
India and highlight traffic through this
| | 02:43 | particular country and see where do
people in India come on my site, where do
| | 02:46 | they go, how do they
progress, how do they drop off?
| | 02:49 | I can do the same for
Australia or any other site through it.
| | 02:52 | To unhighlight that particular one,
I can click again and click Clear
| | 02:55 | Highlighting and we'll go
back to the original screen.
| | 02:58 | If we have a particular interest in a
node, we can click the second option up
| | 03:02 | here to view only this segment.
| | 03:04 | At that point, it's going to clear
away all the other ones and just show us a
| | 03:06 | segment that we're currently interested in.
| | 03:08 | To go back, I simply come up here to
the breadcrumbs and click on the original
| | 03:11 | Visitor Flow and I'm right back where I started.
| | 03:13 | Another good tip is that on any individual
page node here, when I click on here, I
| | 03:18 | can select the group details.
| | 03:19 | From here, we see a table with
different metrics about the pages that
| | 03:22 | are included there.
| | 03:23 | We can also see things like Top pages,
Traffic breakdown, and even things like
| | 03:26 | Outgoing traffic of where they went next.
| | 03:28 | This view was great if we are trying
to understand marketing things like where
| | 03:32 | did things from a particular source
come from, or things about a visitor like
| | 03:35 | what particular people that came from a
country did when they came through here,
| | 03:39 | such as United Kingdom, or India versus the US.
| | 03:41 | But what if we are looking at this from
a content point of view, and we want to
| | 03:44 | examine an individual piece of content?
| | 03:46 | In that case, we can scroll over here,
click on an individual page node.
| | 03:50 | We can click Explore traffic through here.
| | 03:52 | This is kind of an amazing view that's
going to show us a report that will make
| | 03:55 | this particular page the center of
our analysis world, focusing only on the
| | 03:59 | traffic through this particular page.
| | 04:01 | On the left-hand side, we can see all
the pages that led up to that page, with the
| | 04:05 | green here being people who
entered the site via that page.
| | 04:08 | What I like about this
report is it's interactive.
| | 04:09 | I can click on the +Step here to go back
even further, and I can understand where
| | 04:14 | people came from prior to that page
and prior to that page all the way back.
| | 04:17 | As we see the progression through here
into the page that we're interested in,
| | 04:20 | I can do the same thing over on the
right where we'll see people went after.
| | 04:23 | In this case, I can see the number of
people who dropped off, but I can also
| | 04:26 | come over here and click through
each step and see where they went page
| | 04:30 | after page after that.
| | 04:32 | I can of course continue to highlight
traffic through individual pages and see
| | 04:36 | the progressions through each one.
| | 04:37 | As we examine this, there are a few
other things we should keep in mind.
| | 04:41 | One is over here I can change the way
this looks if there are too many lines
| | 04:44 | through here by clicking on the plus
button and allow us to see each of these
| | 04:47 | paths more clearly by elongating the
space in between each individual node.
| | 04:52 | I can click the Home button at any
time to come back to my primary node, and I
| | 04:55 | can change the number of steps here by
clicking the x to reduce that step there
| | 05:00 | and get back to our original one here.
| | 05:01 | One of the most powerful
features is somewhat hidden.
| | 05:05 | Let's say that you are looking
through this list and you're looking at this
| | 05:07 | particular blog post here, and you think
to yourself that you're interested in
| | 05:10 | not just the traffic that came through this
blog post, but all the different blog posts here.
| | 05:14 | What we want to do is click this
little gear up here on the top and we've got
| | 05:18 | these Match Type option.
| | 05:19 | So one of the things we can say is
begins with, and clear this out so it's not
| | 05:22 | this particular blog post, but
anything that starts with /blog.
| | 05:26 | What it's going to do is allow us to
analyze the traffic through our content as
| | 05:30 | if everything through this
/blog was one individual page.
| | 05:33 | Here we can see all the traffic that
flowed through the blog, where it came in,
| | 05:36 | how much of it dropped off,
and where it went after that.
| | 05:39 | If I click here on Group details, I
can see all the different pages that
| | 05:42 | actually are grouped under this
particular one, so everything that starts with
| | 05:46 | /blog here, all the individual visits,
percentage of traffic drop-off rates,
| | 05:50 | and individual metrics that I need to
see here. Or I can come back here to my
| | 05:53 | Flow Visualization and see how traffic
flowed through that particular set of
| | 05:57 | content on the site.
| | 05:59 | This is incredibly powerful as we are
trying to understand how people are using
| | 06:02 | groups of content rather
than just individual pages.
| | 06:05 | The data in these reports can
be used by your entire web team:
| | 06:08 | marketers and advertisers to
designers to conversion optimizers. They
| | 06:12 | represent the best kind of reports,
easy and intuitive, yet powerful, and it's
| | 06:16 | easy to dig deeper.
| | Collapse this transcript |
|
|
9. Advertising ReportsLinking an AdWords account to Google Analytics| 00:01 | In this chapter we're going to discuss
integrating Google analytics with Google
| | 00:04 | AdWords, which is a very powerful feature.
| | 00:07 | Our goal here is to give you
knowledge to organize, optimize, and tweak your
| | 00:10 | campaigns, but in no way should
this be considered a course in the
| | 00:12 | fundamentals of AdWords.
| | 00:14 | For now we'll assume some
understanding of the basic AdWords concepts.
| | 00:18 | I do, however, suggest that before
you begin advertising with the AdWords
| | 00:20 | for the first time,
| | 00:21 | you seek out some basic AdWords
training, such as the AdWords Essentials
| | 00:25 | Course here on lynda.com.
| | 00:26 | At the minimum, you want to get to the
point where you understand the basics of
| | 00:29 | the Ad Auction, the different types of
ad networks, placement versus keyword
| | 00:32 | targeting, and bid terms versus
search queries, just to name a few.
| | 00:36 | However, if you're already an AdWords
advertiser, it's no surprise why you are
| | 00:39 | watching this chapter.
| | 00:40 | One of the primary motivations to use
Google Analytics is to track and maximize
| | 00:44 | your online advertising spent.
| | 00:46 | This is particular compelling if you
are a Google AdWords advertiser, because
| | 00:49 | the optional integration between these
two Google products and the fact that
| | 00:53 | they utilize the same backend database
enables information that simply cannot be
| | 00:57 | found anywhere else, in any
other product, from any other vendor.
| | 01:00 | Normally when we think of Google
Analytics we think of recording what happens
| | 01:03 | after they click onto our site.
| | 01:04 | But here, we'll also be able to pull
down pre-click data, such as the number of
| | 01:08 | times the ad was shown,
clickthrough rate, et cetera.
| | 01:11 | We can also get cost data, so we
can create return-on-ad-spent type ROI
| | 01:15 | reporting and much more.
| | 01:16 | As we've seen before and we'll see over
and over, having access to information
| | 01:20 | others don't can make your advertising
far more efficient than your competitors
| | 01:23 | and maximize your own budget.
| | 01:25 | In fact, many of the clients we have
worked with that pay literally hundreds of
| | 01:28 | thousands of dollars for analytics
packages will run Google Analytics in
| | 01:31 | addition to those other packages just
to get access to these AdWords reports.
| | 01:35 | Before we jump into the Google
Analytics reports, let's take a look at a
| | 01:38 | typical report from within the AdWords reporting
interface that has conversion tracking enabled.
| | 01:43 | Here we see things like Clicks,
Impressions, Clickthrough Rate, Average CPC,
| | 01:47 | Average position, and all the
usual pre-click data from AdWords.
| | 01:51 | When we link the two together and
enable auto-tagging, AdWords passes this data
| | 01:55 | automatically over to Google Analytics,
and the easiest way to link these two
| | 01:58 | together is to start by
logging into the AdWords interface.
| | 02:01 | Go ahead and choose the Tools and Analysis
tab and drop down here to Google Analytics.
| | 02:06 | If you haven't yet linked your Google
Analytics and AdWords accounts, click on
| | 02:10 | the gear in the upper right-
hand corner of the window.
| | 02:12 | This will bring you to your
Analytics Profile settings page.
| | 02:14 | Here we are looking at the profile
settings, but we don't want to link just one
| | 02:17 | profile, we want the entire account.
| | 02:19 | So click on All Accounts and then
select the Google Analytics account that you
| | 02:23 | want to link to this AdWords account.
| | 02:25 | Click on the Data Sources tab and this
AdWords page should appear, allowing you
| | 02:29 | to link your AdWords and Analytics accounts.
| | 02:31 | Now you can see the Google Analytics
interface from right here inside of AdWords.
| | 02:35 | If we wanted to, we could do
everything we can do with Google Analytics right
| | 02:38 | here, but the real value is that the
accounts are now linked and integrated,
| | 02:42 | which will enable the full power of
the reports in the rest of this chapter.
| | Collapse this transcript |
| Identifying campaigns and segmentation options| 00:00 | There are three reasons the AdWords
reports are some of the most powerful in all
| | 00:03 | of Google Analytics:
| | 00:04 | One, because they have the ability to
present data that can't be found elsewhere;
| | 00:08 | two, they are extremely actionable; and
three, they are directly related to the
| | 00:11 | amount of cash that goes out the door.
| | 00:13 | Improvements based on this analysis
could be directly attributed to the bottom
| | 00:16 | line, which always makes for a popular
report and usually a popular analyst.
| | 00:20 | We navigate here via the advertising section.
| | 00:22 | You'll see that the AdWords reports
have their own section as well, under the
| | 00:25 | main one, and we'll start
out in the Campaigns report.
| | 00:29 | By clicking on Clicks in the top
navigation, we can see an important mash-up
| | 00:33 | of two key databases:
| | 00:34 | the data about the visits from your
Google Analytics data sets and then the
| | 00:37 | rest of the top line matrix
associated with AdWords, such as how many
| | 00:41 | impressions, how much revenue, ROI etc
that have been pulled from the AdWords
| | 00:45 | database and correlated here.
| | 00:47 | After all, Google Analytics
has no concept of impressions.
| | 00:49 | That happens before you even hit the
site and Google Analytics would not have
| | 00:52 | had a chance to run.
| | 00:54 | As we discussed earlier in the
chapter, when you turn on auto-tagging and
| | 00:57 | link your Google Analytics accounts with
your AdWords, this as all happens automatically.
| | 01:01 | And comparing even these top-line
metrics, it can be very illuminating to use
| | 01:05 | the Compare to Past feature.
| | 01:06 | Here we can quickly see that our visits
are down only slightly, but our revenue
| | 01:10 | per click has dropped
dramatically, so what happened?
| | 01:13 | We certainly want to investigate
that in a hurry, especially if you are
| | 01:16 | paying for every one of those clicks
that are apparently not helping as much as they used to.
| | 01:19 | Here in the Campaigns reports, it follows
the same hierarchy as if you were in AdWords.
| | 01:24 | Starting at Campaigns and then if you
click to view the Ad Groups heading or if
| | 01:28 | you click down into a given campaign, you
will see your campaign data broken down
| | 01:32 | into the Ad Groups that belong to that campaign.
| | 01:35 | Since we clicked and drilled down into
the Google Store campaign, we're going to
| | 01:38 | see the associated Ad Groups
beneath that as the default segment.
| | 01:41 | We're initially sorted by the Visits
column, but it's interesting to evaluate
| | 01:44 | the other performance metrics.
| | 01:46 | Since the system knows what we paid
an AdWords for the ad click and Google
| | 01:49 | Analytics knows if it brought any
revenue in the associated visit, we can
| | 01:53 | calculate ROI statistics, including
Margin, which is our net revenue divided
| | 01:57 | by our total revenue--in other words total
revenue minus cost divided by the revenue.
| | 02:03 | In the Margin column here we'll see some
things have practically jump off the page at us.
| | 02:06 | 44% isn't too shabby, but -11,000%?
We'll certainly want to take a closer look
| | 02:12 | at those Ad Groups and figure
out exactly what is going on there.
| | 02:15 | One thing we can see right
away is this particular disparity.
| | 02:18 | Every time somebody clicks on that ad,
I am paying a cost per click of about a
| | 02:21 | buck 18, but we're only receiving
one cent back in revenue per click.
| | 02:26 | So do we want to keep doing that? Not likely.
| | 02:28 | This is highly, highly actionable analytics.
| | 02:30 | These new AdWords reports bring us a ton
of segmentation options to really dig into.
| | 02:35 | Some even get their own dedicated
report, as we see over here in the
| | 02:38 | left-hand navigation.
| | 02:39 | Well, look at those in depth, but for now
let's look at some of the lesser used ones
| | 02:42 | that still provide a lot of value.
| | 02:43 | There are so many of these available
that they actually scroll off our screen,
| | 02:47 | but I want to bring your attention to
three in particular that I think we'll
| | 02:49 | want to highlight: Ad Content, Ad
Distribution Network, and Match Type.
| | 02:54 | The first of these, Ad Content,
shows us how each version of an ad was
| | 02:57 | performing, and it's useful for split
testing which, by the way, you should all be doing.
| | 03:01 | In this case, we see we were
putting an insertion operator to use.
| | 03:04 | If you are unfamiliar, the insertion
operator allows the ad to reflect the exact
| | 03:09 | text of the search query, which can make
your ad appear to be highly specific to
| | 03:13 | the search or search phrase.
| | 03:14 | Now some people speculate that this is
good for enticing users to click on the
| | 03:17 | ads because they see the ultra-specific
ad text reflected back to them, and they
| | 03:21 | think that your site has exactly what they need.
| | 03:24 | But the suspicion is that the
performance of those visits is not necessarily so
| | 03:27 | great once they get to your site, and
they realize it's not as perfect a match
| | 03:30 | as they thought based on that ad text.
| | 03:32 | So how can we evaluate that easily?
| | 03:34 | Let's take a look at the metrics we've got here.
| | 03:37 | Assuming the ad is displayed in
equal number of times, we just compare
| | 03:40 | Visits and Bounce Rate.
| | 03:41 | Here we see the ad was good at
generating clicks, but as we suspected, it has a
| | 03:45 | high Bounce Rate, more than twice
the ad without the insertion text.
| | 03:49 | Sometimes that insertion operator works great.
| | 03:51 | I am not saying it doesn't. But you
need to use your analytics to evaluate it
| | 03:55 | carefully for your site.
| | 03:56 | There's also a great deal of
discussion about which of Google's ad
| | 03:59 | networks works the best.
| | 04:01 | Well, best is a vague word and there
is a lot of ways we can analyze this in
| | 04:04 | Google Analytics, using the Ad Distribution
Network as a secondary dimension of our campaign.
| | 04:09 | In this case we can see that in terms of
Ecommerce Conversion Rate and Per Visit
| | 04:12 | Value, Google Search greatly
outperforms the Search partners.
| | 04:16 | The next segment is Match Type, which
shows the performance when the keywords
| | 04:19 | were broad matched, phrase matched, or
exact matched, and it's another hotly
| | 04:23 | debated item and you'll need data to
back up your own decisions and strategy.
| | 04:27 | We can do that using the Match Type
segment here that shows us the performance
| | 04:30 | of each type, and in this particular
account it proves the critics right and shows
| | 04:33 | us why Broad matches often bemoaned.
Though it gets far more visitors than
| | 04:36 | the other match type, its conversion rate and
revenue are much lower than the exact match.
| | 04:41 | This is not always the case. Check your own
stats and do the analysis on your account.
| | 04:45 | The next one is Placement Domain.
| | 04:46 | Here we are seeing the domains in the
Google Display Network where the ads have
| | 04:49 | been placed, and we can see
how each site is performing.
| | 04:52 | Some sites will have the type of
traffic that that is ideal for your business
| | 04:55 | and converts like mad,
| | 04:56 | and some sites may never
convert a single visitor.
| | 04:59 | Here are the Ad Groups from our T-
shirt and Jersey campaign, and it's pretty
| | 05:02 | clear that the folks on Google.com
are more interested in our jerseys and
| | 05:04 | T-shirts than the other domains.
| | 05:06 | If you're interested in the actual URL,
not just the overall domains, perhaps
| | 05:10 | because we're running ads on different
parts of the site, we can see that with
| | 05:13 | this Placement URL report we can
evaluate the results in order to adjust our
| | 05:17 | advertising strategy accordingly.
| | 05:19 | Next we'll look at the Content
Targeting option, which will indicate whether
| | 05:22 | we are targeting keyword searches or
specific ad placements on the Google
| | 05:25 | Display Network sites.
| | 05:27 | You don't know the performance
until you have the data, and in this case
| | 05:30 | automatic placements are
responsible for a lot more visits.
| | 05:33 | We can continue on with all kinds of
combinations of dimensions, secondary
| | 05:36 | dimension, goal metrics, site usage
metrics, et cetera, so that we can tweak and
| | 05:40 | optimize our campaign towards specific targets.
| | 05:43 | You can't manage what you can't
measure and those who want to manage AdWords
| | 05:46 | will find plenty to do in these reports
as they consolidate, isolate, and segment
| | 05:50 | your AdWords data so you can make
informed decisions about your ads spent.
| | Collapse this transcript |
| Using keyword reports| 00:00 | Keywords are obviously an important
thing to analyze when we are talking about
| | 00:03 | AdWords strategy, and we have a
dedicated report here in the Advertising
| | 00:06 | section under AdWords.
| | 00:08 | Depending on the question we are
trying to answer, there are a number of
| | 00:10 | legitimate ways to analyze this.
| | 00:12 | Certainly I want to see which keywords
I am bidding on that are triggering the
| | 00:15 | most ads and bring in the most visits.
| | 00:17 | Now keep in mind what we're seeing
here is the bid term, not necessarily the
| | 00:21 | actual keywords typed in by the searcher.
| | 00:23 | One of the most important questions to
answer in regarding our bidding strategy
| | 00:26 | is, how much should I be
bidding on a particular term?
| | 00:30 | Obviously much that will depend on
how much the term is worth to us and Per
| | 00:33 | Visit Value is designed to answer just
that question of how much is an individual
| | 00:37 | visit worth to us in terms of value.
| | 00:40 | You can find that here under the Ecommerce tab.
| | 00:42 | Now google store is one word, and
android notebook are two obviously
| | 00:48 | very different terms.
| | 00:49 | They both bring about the same amount of visits.
| | 00:51 | So if we were simply ranking these on
visits, we might think that they are similar.
| | 00:55 | But look at the difference in swing
between values that they represent. And this
| | 00:59 | is certainly not an AdWords course,
| | 01:01 | but suffice it to say that no two
keywords will perform exactly alike and the
| | 01:04 | subtle difference is in connotation
and motivation can have wildly different
| | 01:08 | values to me as a web site owner.
| | 01:10 | In this case, we know that the person
looking for a specific product is far more
| | 01:14 | likely to make that high-value purchase.
| | 01:16 | There's actionability
everywhere in these reports:
| | 01:19 | adjusting individual bid prices, adjusting
ad text, negative keywords, the list goes on.
| | 01:24 | Utilize this report and the other
reports in the section to see if you have any
| | 01:28 | bid terms that are
underperforming or overperforming.
| | Collapse this transcript |
| Fine-tuning your match type with the Matched Search Queries report| 00:01 | The Matched Search Queries report,
located in the AdWords reports, underneath the
| | 00:04 | Advertising section, has some of the
most requested and actionable data in all
| | 00:08 | of Google Analytics.
| | 00:09 | This used to be a dimension very deep
in the AdWords reports, but now in the new
| | 00:12 | version, it has been promoted to its
own spot in left-hand navigation.
| | 00:16 | Let's talk terminology for a second, because
subtleties make a big difference in this report.
| | 00:20 | In AdWords you have the Keywords, which
are your bid terms, and then you have the
| | 00:24 | search query, which is what the user
actually typed into the search bar.
| | 00:27 | That search query triggered an ad
impression based on a keyword that you bid on,
| | 00:31 | and as a result, your ad was
triggered to be displayed.
| | 00:34 | The searcher then clicked on the ad and
they came to your web site as a visitor.
| | 00:37 | The key here is that the search
query is not necessarily the same as the
| | 00:40 | keyword that you bid on.
| | 00:41 | In fact, there might be some large
variations between the keywords and the
| | 00:45 | search queries that cause
that corresponding ad to be shown.
| | 00:48 | You might recall that there are three main
match types in AdWords: Broad, Phrase, and Exact.
| | 00:52 | Just like the name implies, Broad
match is meant to cast a wide net to match
| | 00:56 | search queries of keywords.
| | 00:57 | As a result, you might see search
queries that have none of the same words as
| | 01:01 | your broad-matched keywords that
you bid on in your AdWords account.
| | 01:03 | In Google Analytics, you can see both
the search query and the keyword that
| | 01:07 | caused the ad to show if we use the
secondary dimension of keyword while we are
| | 01:11 | in this Matched Search Query report.
| | 01:13 | Let's go take a look at that.
| | 01:14 | Here we see the Matched Search Queries
and what people actually typed in. And as
| | 01:18 | a secondary dimension, we can come
here down to AdWords and we can add the
| | 01:22 | keyword that contains the bid term.
| | 01:24 | This can give us a great perspective for
comparing and contrasting the terms and
| | 01:27 | the keywords that brought
these visitors to your site.
| | 01:30 | Here in this case, we can see that
both google shopping and google store were
| | 01:34 | searches that triggered ads that
included the bid term of google store.
| | 01:38 | So on left, I see google shopping and
google store were Matched Search Queries.
| | 01:41 | Both of those were for the
keyword that was bid on, google store.
| | 01:44 | In this case, it was an exact match.
google store was the Matched Search Query
| | 01:48 | and google store is the keyword.
| | 01:49 | But up here we had google shopping,
which was considered to be close enough to
| | 01:53 | google store to trigger the ad.
| | 01:55 | Okay, so we see the same keyword here
but different Matched Search Query.
| | 01:58 | Was the result the same? Not even close.
| | 02:01 | They both had a similar number of visitors
two thousand something, but look at the revenue.
| | 02:05 | One got 1500 while the
other got zero. So why is that?
| | 02:09 | Well, let's think from the
searcher's point of view.
| | 02:11 | People who are searching on the word
"google store" might actually be looking
| | 02:15 | for the google store, which sells Google
merchandise like T-shirts and pens and things.
| | 02:18 | But if someone types in google
shopping, it's very possible what they are
| | 02:22 | actually looking for is the google
shopping comparison engine, what used to be
| | 02:26 | known as Froogle, so they can buy
other things from other stores.
| | 02:30 | So in that case they are looking to
buy a new flatscreen TV, not actually a
| | 02:33 | T-shirt with the word Google written
across the front of it, and the just want to
| | 02:37 | use Google as the search engine.
| | 02:38 | These completely different motivations
and intentions can lead to completely
| | 02:42 | different amounts of revenue for our
Google store, and we need to understand
| | 02:45 | those subtle differences between
those exact queries typed in, so we can
| | 02:49 | understand how to bid for those.
| | 02:51 | This data is absolutely critical for
informing your entire AdWords strategy.
| | 02:55 | For example, if a search is not
relevant, I may want to use those as negative
| | 02:59 | keywords, so that our ad won't
be triggered for the searches.
| | 03:02 | If the search that I see is relevant
but just not performing well, I may want
| | 03:06 | to create an entirely separate ad group and ads.
| | 03:08 | If it's performing very well and I have
a search that's just pure gold for me,
| | 03:12 | I may want to increase my bid to make sure
I get as much of that traffic as possible.
| | 03:16 | I may also create landing pages and ads
that speak directly to that valuable searcher.
| | 03:20 | Seeing the search queries can be
beneficial in two ways: for creating the
| | 03:23 | negative keyword lists for generating
new keyword ideas and adjusting my bids
| | 03:28 | and landing pages to match those.
| | 03:29 | As you can see, this report is
extremely actionable and insightful.
| | 03:33 | If you spend any money in AdWords, I can
nearly guarantee that there is money to
| | 03:37 | be made or saved by spending
some time analyzing this data.
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| Optimizing traffic by time of day| 00:02 | One very useful feature of AdWords is
the ability to show your ads during the
| | 00:05 | time of day when they're
most likely to bring you value.
| | 00:08 | For example, say you're a local flower
shop and if you know that you're much
| | 00:11 | more likely to secure a sale during
business hours when you're there to answer
| | 00:14 | the phone, rather than say at 3 a.m.
when they may have decided to go with an
| | 00:18 | online shop, you can choose to concentrate
all of your ad budget during the daytime hours.
| | 00:23 | But what if it's not so simple?
| | 00:24 | What if you don't know exactly when you
should be running your ads or if there's
| | 00:28 | even a difference at all?
| | 00:29 | Google Analytics can help you answer
this kind of question with the Day Parting
| | 00:32 | report inside of the AdWords reports.
| | 00:35 | Here we see visits via AdWords
plotted out against the hours of the day.
| | 00:39 | Here we can clearly see that your
most active time of day is in the early
| | 00:41 | morning and that during the wee
hours here in the middle of the night,
| | 00:45 | things aren't so hot. Okay, good, right?
| | 00:47 | We should adjust our AdWords? Not so fast.
| | 00:50 | We're not in the business of getting visits.
| | 00:52 | We're in the business of making money,
and we want to see when the most valuable
| | 00:55 | traffic comes by, as well as the most volume.
| | 00:57 | So we need to adjust our
metrics to reflect that.
| | 01:00 | Go up here and click the dropdown.
| | 01:02 | We want to select the option
here to compare two metrics:
| | 01:05 | we want both Visits and we want Per Visit value.
| | 01:07 | When we do that, what we see is that
while volume does drop off as the day
| | 01:12 | rolls on, the value of those visits remains
extremely high; in fact, it's at its highest point.
| | 01:17 | So the last thing we want to do is
drop off this highly valuable traffic that
| | 01:21 | comes here in the late
afternoon and early evening.
| | 01:23 | Ultimately, day parting can do a lot to
maximize and squeeze every drop of value
| | 01:27 | from your advertising dollar,
especially certain types of businesses.
| | 01:30 | But make sure you fully understand what
these reports are telling you before you
| | 01:34 | take action that could end up
hurting rather than helping.
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| Using the Destination URL report to identify landing pages| 00:00 | When it comes to actionable
analytics, we're looking for insights--
| | 00:03 | in other words, ones where we can
easily turn around and do something about it
| | 00:06 | that will improve our site.
| | 00:07 | There are few things that can have
a quicker more impactful change than
| | 00:10 | improving our landing pages,
especially when it's landing pages that we're
| | 00:14 | paying for people to visit.
| | 00:15 | When we're talking about AdWords, it's
the destination URL that determines the
| | 00:19 | landing pages, and they have
their own dedicated set of reports.
| | 00:22 | We navigate here, under the Advertising
section, into the AdWords reports and from
| | 00:27 | here we see the destination URLs.
| | 00:29 | Here in the Ecommerce tab we
can see the associated key metrics:
| | 00:32 | Visits, Revenue, Transactions, Average
Value, Ecommerce Conversion Rate, Per
| | 00:36 | Visit Value, et cetera.
| | 00:38 | The default report shows me
the most popular landing pages.
| | 00:41 | Well, what can I do with that?
| | 00:43 | In some ways, this really shouldn't be
too much of a surprise, since you get to
| | 00:47 | determine where you send
the traffic from AdWords.
| | 00:50 | So perhaps Visits isn't the best indicator.
| | 00:53 | To make this report immediately
actionable, keep the Ecommerce tab selected and
| | 00:57 | move to the Comparison view.
| | 00:59 | Also change the metric to
Ecommerce Conversion Rate.
| | 01:03 | Here we see there is a huge difference
in how my landing pages are performing.
| | 01:08 | Clearly, we need to understand what's
happening with that second page there and
| | 01:11 | why its performance is so
different from the others.
| | 01:13 | Our first steps might be checking
keywords that are driving traffic to the page,
| | 01:17 | and checking ad text, checking
internal site search reports, which we'll also
| | 01:21 | discuss in an upcoming video.
| | 01:23 | Once we have some theories about what
may be wrong with that page, we need to
| | 01:27 | make those changes and test it.
| | 01:29 | To do that, we can employ a landing
page and optimization software set, such as
| | 01:33 | Google's free web site Optimizer,
which also has a course here on lynda.com.
| | 01:37 | Landing pages are a critical part of
optimizing your site, and the destination
| | 01:40 | URL report is a great first step
towards evaluating your AdWords' landing pages.
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| Identifying the best placement options for ads| 00:00 | Although we generally think of
AdWords as the ads that appear next to
| | 00:03 | Google searches, in reality, the
network is far more sophisticated and has
| | 00:07 | far more reach than that.
| | 00:08 | With the ability to display our ads
and promote our site on millions of
| | 00:11 | different sites across the web, big
sites like The New York Times or even
| | 00:14 | some small niche sites,
| | 00:15 | some sites' placements are going
to work very well, and some don't.
| | 00:19 | We need to be able to measure this
so we can evaluate that performance
| | 00:22 | and adjust our ads.
| | 00:23 | The domains and URLs where our ads are
shown in display network have their own
| | 00:26 | report, which we can use to give us
insight into how the content targeted sites
| | 00:30 | are performing in the display network,
which is formally known as the Google
| | 00:33 | Content Network. And we navigate here
to the Placements report here under the
| | 00:36 | Advertising section, with its own
dedicated AdWords reports.
| | 00:39 | And first, we're going to see
the Content Targeting Option here.
| | 00:42 | In this case, our ads are going to
be divided into Automatic or Managed
| | 00:45 | placements, so we can see which variety
of ad targeting is performing better or
| | 00:49 | worse for Visits and Conversion Rate.
| | 00:51 | Then we can click into the
Placement domains or Placement URL to see
| | 00:54 | specifically where our ads are being displayed.
| | 00:57 | Although it's interesting to see
which sites have been generating the most
| | 00:59 | traffic for us, this is AdWords, and I
am paying for this traffic, so I want
| | 01:02 | to see how it's performing, so I can
either target a site specifically or cut
| | 01:07 | off those that aren't.
| | 01:08 | Down here I have selected Ecommerce
Conversion Rate to compare one metric versus
| | 01:11 | the other and see which of these
different places is performing better.
| | 01:14 | However, we could also look at
things like Per Visit Value, Revenue,
| | 01:18 | Average Value, et cetera.
| | 01:20 | Now, there are lots of ways that we
could evaluate these different place and
| | 01:22 | sources depending on your goals and
metrics, but by and large, it looks like
| | 01:26 | this traffic isn't doing too well compared
to the other sources of traffic on my site.
| | 01:30 | But there are a few bright spots down here,
particularly number 6 and number 9 down here.
| | 01:34 | Now, in the past, some advertisers have
avoided content targeting on the Google
| | 01:38 | Display Network entirely.
| | 01:39 | I can tell you, it is possible to be
tremendously successful with Content
| | 01:43 | Targeting, but generally speaking,
it requires you to be much more
| | 01:45 | diligent about managing which sites
are showing your ads and how you're
| | 01:49 | managing your budget.
| | 01:50 | You need analytics to be able to
manage this type of ad network.
| | 01:53 | Remember, you can't
manage what you can't measure.
| | 01:55 | This dedicated report is a great
resource to help you do just that.
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| Keyword positions| 00:00 | The AdWords Keyword Position report
here under the Traffic Sources tab is one
| | 00:05 | of the most under-utilized but actionable
AdWords reports in all of Google Analytics.
| | 00:09 | It's also a fantastic example of how
we can use Google Analytics and AdWords
| | 00:14 | integration to gain a distinct
advantage over our competition.
| | 00:17 | After all, your ad's position on the
page is all relative to the competition's
| | 00:21 | ads on the search results page.
| | 00:23 | Unfortunately, this report is often
used incorrectly, which can have disastrous
| | 00:28 | effects in your AdWords campaign.
| | 00:30 | So here we'll show you how to
correctly interpret the results. And one of the
| | 00:33 | most critical questions to answer when
running an AdWords campaign is, where do
| | 00:37 | I want my ads to appear on the page?
| | 00:40 | As you can see in this mockup of the
search results page, our ads can appear
| | 00:44 | over here on the right in the
traditional location, inside positions 1 through 8,
| | 00:48 | or above the organic results, in
the top positions 1 through 3.
| | 00:51 | Now, AdWords actually gives you the
option of stating a preference on which
| | 00:55 | position you prefer, but how do
we know which position we prefer?
| | 00:59 | To answer that question, we'll use this
Google Analytics report to examine the
| | 01:02 | performance of each keyword when it
appeared in those different locations.
| | 01:06 | And you can see here on the left,
keywords in my account that has
| | 01:10 | brought traffic to my site:
| | 01:12 | Google Store, Google Logo,
Google Stores plural, et cetera.
| | 01:15 | Now, as I select the keyword, it will
be highlighted and the right side will
| | 01:18 | automatically update to show
the results for just that keyword.
| | 01:22 | So I can view how each keyword is
performing individually, which is important,
| | 01:25 | since no two keywords will perform the same.
| | 01:28 | Now the default is just to
show the number of visits.
| | 01:31 | So in this case when I click on the
keyword google store, I can see the number
| | 01:34 | of visits that were generated when
the ad appeared in each position.
| | 01:38 | Here you can see that when my ad
appeared in the top position on the left, over
| | 01:41 | the natural search results,
it generated the most visits:
| | 01:44 | 952 compared with 640, 612, 380, 412, et cetera.
| | 01:51 | Now, if your goal is to drive the most
amount of traffic to your site, then you're done.
| | 01:55 | There's no question that the primo
spot for your ad to appear is the number
| | 01:58 | one spot above the natural results.
| | 02:01 | But hold on. Before we close the
book on this, remember, this only takes
| | 02:05 | into account visits.
| | 02:07 | Most businesses do not
show ads just to get visits;
| | 02:10 | they show ads to generate revenue.
| | 02:12 | Now fortunately, we have a
metric that shows just that.
| | 02:16 | I simply change my metric using this
dropdown box to show how much revenue was
| | 02:20 | generated when the ad appeared in each position.
| | 02:25 | Now here you can see a very different story.
| | 02:27 | The top-left position that performed so
well before generates only $250, while
| | 02:31 | the third position over on
the right generated over 620.
| | 02:37 | So how could it be that we got so many
more clicks and visits here, but it added
| | 02:41 | up to significantly less revenue?
| | 02:43 | Well, if you're a veteran
AdWords user, you're aware
| | 02:45 | there are plenty of theories out there
about why you might see this behavior.
| | 02:49 | For example, there's a tendency for non-
discriminating users to get a bit click
| | 02:53 | happy and simply click on
the first thing that they see.
| | 02:56 | But since they're just clicking on the
first thing they saw, rather than because
| | 02:59 | your ad had exactly what they needed,
they aren't particularly likely to
| | 03:03 | actually buy from you.
| | 03:04 | But over here, it's a different story.
| | 03:06 | Here they've gone through all the
different options and settled on your ad
| | 03:09 | buried over here in the middle.
| | 03:11 | So it's highly likely there was
something about it that matched their specific
| | 03:14 | needs and therefore the likelihood that
they buy after clicking is much greater.
| | 03:20 | So all this brings up another good point:
| | 03:22 | Why do we want to pay for bad traffic?
| | 03:25 | And while revenue has been very
insightful, it doesn't tell the entire story
| | 03:28 | either, because it says nothing about our costs.
| | 03:31 | We're not just looking for
total revenue, but profitability.
| | 03:35 | Since this is pay per click, we have to
pay for every one of these visits and we
| | 03:39 | want to see how much revenue are we
getting back each time we get one of those
| | 03:43 | clicks that we're paying for.
| | 03:45 | We have a metric that tells us
exactly that, per visit value.
| | 03:49 | We select this metric and we can
see a very different story unfold.
| | 03:53 | While the positions on the right are
generating up to $3.63 per click, the
| | 03:58 | top-left position that faired so
well in the Visits report generated just
| | 04:01 | $00.67 per click. And this discrepancy
is even more disturbing when you consider
| | 04:07 | that this top position often commands
a heavy price premium and is much more
| | 04:11 | expensive location in the positions over
here on the right that are performing so well.
| | 04:16 | So as you can see, which position is
best can have very different answers,
| | 04:21 | depending on what you see here in your
report and what your business goals are.
| | 04:25 | This is a very powerful report, but we
need to choose our metrics wisely, so
| | 04:29 | we're not optimizing for the wrong thing.
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|
|
10. Traffic Source ReportsUnderstanding where site visitors come from| 00:00 | If I could pick only one tab to do all
my work, it would be the Traffic Sources
| | 00:04 | tab. Whenever I examine a
client's account for the first time,
| | 00:07 | this is the first place I go.
| | 00:09 | To really understand the difference
these reports can make, it's worth stepping
| | 00:12 | back and looking things in perspective.
| | 00:13 | There is a very famous person in the
business world many of may know by the name
| | 00:18 | of John Wanamaker, a very successful
retail magnate, and one of the things he is
| | 00:22 | famous for is the phrase, "I fully
believe half of the money I spend on
| | 00:25 | advertising is wasted;
| | 00:27 | the trouble is I don't know which half."
| | 00:29 | And back in his day, this was just sort
of accepted as a cost of doing business.
| | 00:32 | It wasn't really a problem
because the playing field was level.
| | 00:35 | Everybody had the same problem, and
what were you going to do, not advertise?
| | 00:38 | But these days, that doesn't necessarily apply.
| | 00:42 | With accurate analytics on my site, I
can evaluate my marketing programs and
| | 00:45 | understand exactly which ones
are working and which ones aren't.
| | 00:47 | I know exactly where the money I am
spending on marketing campaigns is being
| | 00:51 | effective and where it's not.
| | 00:52 | So I don't have to waste half of the
money on advertising because I simply
| | 00:55 | can't track which ones are
working and which ones aren't.
| | 00:58 | In other words, accurate analytics is a
major competitive advantage. Now it's funny.
| | 01:02 | When I talked to clients, sometimes I see
the reverse psychology is more motivating.
| | 01:05 | Some people are mildly excited when they
see what's possible, but it's only when
| | 01:08 | they realize that in a very short time
all of their competitors will have this
| | 01:12 | information and they'll still be the
only ones wasting 50% of their marketing
| | 01:15 | budget, fear and panic start to take over.
| | 01:18 | In some ways, it is an arms race.
| | 01:19 | Let's make sure you've
got the adequate firepower.
| | 01:23 | Analytics is going to split all of
our traffic up into three main buckets.
| | 01:26 | In this case, we've got referring sites,
search engines, and direct traffic.
| | 01:32 | Referring sites are just a fancy
way of saying that these are links.
| | 01:35 | These are links where some of the web
site has referred traffic over to yours.
| | 01:40 | Search engines are just special cases of
this where Google Analytics recognizes
| | 01:43 | that these aren't just any web sites;
| | 01:45 | these are search engines, and it's able
to pull out keywords from the URL as well.
| | 01:50 | Direct traffic in many ways
is the absence of information.
| | 01:53 | Google Analytics wasn't able to figure
out any other way of understanding where
| | 01:56 | this traffic came from,
| | 01:57 | either automatically, like it does in
the referring sites, or manually tagging,
| | 02:01 | which we'll take a look at later.
| | 02:03 | So direct traffic, we realize that a
visit came and people did things
| | 02:06 | but it wasn't able to apply any other
information to it or tag it in any other
| | 02:10 | bucket and therefore it
becomes the direct none tag.
| | 02:13 | As we will see, this direct referring
sites and search engines report, they are
| | 02:18 | reflected here in the Direct Traffic overview.
| | 02:21 | In this chapter, we'll go in depth in
each one of these reports, and I think
| | 02:23 | you'll agree that a few minutes of
browsing these reports in this section can be
| | 02:26 | invaluable for understanding where
your traffic is coming from, which is a
| | 02:29 | fundamental pillar of analysis.
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| Analyzing the All Traffic Sources report| 00:01 | The All Traffic report, found under the
Traffic Sources section, is in the top
| | 00:04 | three most important reports of all
of Google Analytics because it quickly
| | 00:08 | allows you to evaluate all of your
sources of traffic and instantly separates
| | 00:12 | the good, the bad, and the ugly.
| | 00:14 | The ability to distinguish high-quality
traffic from poor-performing traffic is critical.
| | 00:18 | Anyone who is serious about their
web site should be able to immediately
| | 00:21 | answer questions such as what is your best
performing source of traffic, what is your worst;
| | 00:26 | which medium sends you the most traffic,
which medium gets you the best bang
| | 00:29 | for your buck, and even questions like
how is your offline marketing compare to
| | 00:33 | your online sources?
| | 00:34 | All those are easy once you get the
hang of making this report work for you.
| | 00:37 | Traffic Sources are lined up
in the table with Source/Medium.
| | 00:39 | These are the default dimension,
although that's easily modified.
| | 00:43 | If we switch over to the Goals or
Ecommerce tab, we can see how each of those
| | 00:47 | Source/Medium combos is performing,
beyond just the number of visits.
| | 00:51 | Let's take a closer look at two of my sources.
| | 00:53 | The newsletter I sent out via email
brought nearly 14,000 visitors, each worth
| | 00:58 | nearly three bucks,
totaling over 40,000 in revenue.
| | 01:01 | On the other hand, the banner ad I've
been running has only brought in 2,500
| | 01:04 | visitors, and each are worth a mere $0.9.
| | 01:08 | In total, I've collected just a
couple of hundred bucks from banner.
| | 01:11 | On top of that, I had to pay per
click for each one of those banner ads,
| | 01:14 | so I need to evaluate whether I am
getting enough money per visit to justify
| | 01:18 | how much I'm paying per
click or potentially CPM.
| | 01:22 | Now that's an astronomical difference
between these two that I need to be fully
| | 01:26 | aware of. Especially when you're running
campaigns that you are paying for, such
| | 01:29 | as these banner ads, it is critical
that you are able to account for that spend
| | 01:33 | and perform analysis on the performance.
| | 01:36 | Both of those things are relatively easy
here in the All Traffic sources report.
| | 01:40 | We'll talk later about campaign tagging,
how you can ensure Google Analytics
| | 01:43 | is able to populate these reports
correctly, and is aware of how to track your
| | 01:47 | marketing activities, including how to
properly identify each one of these mediums.
| | 01:51 | In general, the All Traffic report
provides some of the most fundamental
| | 01:54 | information a web analytics package
can offer in a simple, easy-to-adjust
| | 01:58 | report: which resources, which mediums,
and which campaigns are performing on
| | 02:02 | your site, and which aren't.
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| Identifying direct traffic| 00:00 | Direct traffic is traffic that comes
to your site without any other source of
| | 00:03 | information accompanying it.
| | 00:04 | In some ways, it is the absence of information.
| | 00:07 | It can come when someone types in a
URL directly or perhaps if they saw an ad
| | 00:12 | with the URL on it and came and typed
that in, if they know it by heart because
| | 00:15 | they frequent your site often, or even
if you have a very easily guessed URL,
| | 00:20 | perhaps microsoft.com, pepsi.com,
ford.com, that kind of thing.
| | 00:26 | It can also come from untagged links
where tracking info has been stripped out,
| | 00:29 | for example, JavaScript-created links,
redirects, et cetera. One side note:
| | 00:35 | it's generally a myth that bookmarks
are responsible for direct traffic.
| | 00:38 | If the visitor does return to your site
in six months or less, the cookie will
| | 00:42 | be maintained and it will be tracked
under the previous site's source, not
| | 00:45 | necessarily piled into the direct bucket.
| | 00:48 | The lack of info about direct can make it a
challenge do to analysis, but all is not lost.
| | 00:52 | There are some things we can detect
about the visit that doesn't depend on the
| | 00:55 | source tagging, for example, geo-
detection of the IP address to tell us what
| | 01:00 | geographical area that visit came from.
| | 01:01 | So let's take a look at this.
| | 01:04 | Let's dimension by city and figure
out which cities were the sources of
| | 01:07 | our direct traffic.
| | 01:08 | Now let's say, for example, that we
had sent our catalog where the URL was
| | 01:11 | prominently displayed, and we think
that a number of people may have typed in
| | 01:15 | the URL directly after
receiving this print campaign.
| | 01:18 | In this case, we can switch on over to
the Ecommerce tab and take a look at the
| | 01:23 | amount of revenue that came from each
of the cities based on direct traffic.
| | 01:26 | Another thing that points out at us here
is that we see in Houston, Minneapolis,
| | 01:31 | and Dallas, even though Houston had
the largest number of visits, it had the
| | 01:35 | lowest amount of value, meaning
from all the visits that came there, I
| | 01:38 | collected the least
amount based on direct traffic.
| | 01:41 | Now of course, I don't know for sure
that this traffic came from this particular
| | 01:44 | mailer, but if I'm willing to make that
assumption, either based on the fact that
| | 01:47 | I got little to no traffic from them
before or I saw a spike right around the
| | 01:51 | time that those mailers were sent out--
granted, this is not necessarily a
| | 01:55 | precise data, but it's a lot
information that I have before when we were just
| | 01:58 | looking at one big lump some of direct traffic.
| | 02:01 | However, it's obviously less than ideal,
and we want to avoid this if possible by
| | 02:05 | the use of proper tagging.
| | 02:06 | We'll take a look later at some campaign
tagging videos to show how we can avoid
| | 02:10 | this problem and get much more precise metrics.
| | 02:13 | Direct traffic should be avoided
whenever possible, but it's sometimes
| | 02:16 | inevitable. With a little digging there
are some insights that can be salvaged.
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| Identifying users who were referred to your site | 00:00 | Referring sites is simply a fancy way
to describe sites that link to you, or in
| | 00:03 | other words, sites that refer traffic to you.
| | 00:06 | Google Analytics has the ability to
detect an incoming visit from a link and
| | 00:09 | then records that link
down as the referring site.
| | 00:12 | From then on, anything that is done
during that visit, including a goal or a
| | 00:15 | transaction, will be credited or
attributed to that referring source.
| | 00:19 | Now one caveat is that this does
depend on these being, generally speaking,
| | 00:23 | plain-vanilla HTML links
from one site to the other.
| | 00:25 | If you have fancy redirect
scripts or JavaScript-built links,
| | 00:29 | sometimes that can break the tracking.
We'll lose the ability to grab that refer.
| | 00:32 | In that case, it would be in the
Direct Traffic bucket, but most of the time
| | 00:35 | from one link to the other will be
appropriately tracked in the Referring Sites report.
| | 00:39 | As we look through this list of sites
that have referred traffic to our site,
| | 00:43 | one thing that can be interesting is to
use a secondary dimension to see, I know
| | 00:46 | that this is the site it came from,
but where did it come to on my site?
| | 00:50 | If we use secondary dimension of Landing
Page, we'll get the combination of this two.
| | 00:54 | I can see that in this case, the
twitter.com link came here to our blog in this
| | 00:59 | particular blog post.
| | 01:01 | As we can see from Google.com, there
were several different landing pages on
| | 01:05 | our site where Google.com sent traffic over.
| | 01:07 | Now one thing to point out: this is not
Google.com's search engine, if someone
| | 01:11 | is actually performing a Google search;
| | 01:12 | this is Google.com's Analytics partner page.
| | 01:15 | In this case, if we want to
investigate that a little bit closer, we can
| | 01:18 | actually click on the Google.com
link and it will show us what's called
| | 01:21 | the referring path.
| | 01:22 | Referring Path or Referral Path is the
path or the address of the page on the
| | 01:26 | referring site itself.
| | 01:27 | So in this case, all of these pages are on
Google.com, referring traffic over to our site.
| | 01:32 | Now again, we can do a Landing Page
second dimension here, which will show all of
| | 01:40 | the places on Google.com that sent
traffic over and where it's sent to on our
| | 01:43 | site. So we can see here that there
are several different pages on Google.com
| | 01:47 | that send traffic to our
Google consulting services page.
| | 01:50 | Let's take a look at a
example that's not a search engine.
| | 01:54 | If we look down here, we
see another one, seomoz.
| | 01:57 | If we click into this link, we can see
that these are all the different pages on
| | 02:01 | SEOmoz that refer traffic to our site.
| | 02:03 | If I click on the little box here with
an arrow pointing out, it will actually
| | 02:06 | take me to that particular page.
| | 02:08 | In this case, I can see there was an
article on Google Analytics, and if we
| | 02:11 | scroll about halfway down, we can see
that there is a link over to webShare's
| | 02:14 | site, halfway through the page.
| | 02:16 | If I click on this link, Google
Analytics will now record the fact that I just
| | 02:19 | clicked on that link, and I will
get a referring site of SEOmoz.
| | 02:25 | So of course, one of the things we
want to do here is evaluate the different
| | 02:27 | sources of traffic to our site, and
one of the most important ways we can
| | 02:31 | evaluate traffic is by our goals.
| | 02:33 | So if were to click here on the
Referring Sites report and click on the Goal Set
| | 02:36 | 1 tab, I may want to do
something like sort by one of my goals.
| | 02:40 | In this case, I have a generic contact
us form, and I can quickly see that the
| | 02:44 | joinazima.org was one of the most
successful sources of traffic to us.
| | 02:48 | Now I also want to keep in context
the number of visits that were sent and
| | 02:52 | between these two, I can determine
which was the most successful or important
| | 02:56 | sources of traffic to me, as well as
others that perhaps weren't performing so well.
| | 03:00 | With this report, you can determine
not only who is sending you traffic, but
| | 03:03 | just as importantly, whose sends
quality traffic that converts on my goals, be
| | 03:07 | it Ecommerce or otherwise.
| | Collapse this transcript |
| Viewing search engine reports (overview, organic, and paid)| 00:00 | Search engines are one of the most
important traffic sources for many sites, and
| | 00:04 | there is a wealth of information we
can gain about how our visitors are using
| | 00:07 | them to find our sites.
| | 00:09 | Google Analytics has three
reports dedicated to search:
| | 00:11 | the Search Overview report, the Organic
Search report, and the Paid Search report.
| | 00:15 | Let's start with the Overview.
| | 00:17 | Here under Traffic Sources, we see
Sources and then Search and then the Overview.
| | 00:20 | This report quickly lets us compare
organic search and paid search traffic
| | 00:24 | across all of our site usage,
goals, and ecommerce metrics.
| | 00:28 | Much like the All Traffic Resources,
report, we can also change the dimension up
| | 00:32 | here to view the data by Source,
Keyword, Campaign, or any of the other
| | 00:36 | dimensions that are available to us.
| | 00:38 | But the key difference here is in this report
| | 00:40 | it will only show us data about
paid and organic search traffic, no
| | 00:44 | other traffic sources.
| | 00:45 | The organic and paid traffic get their own
dedicated reports, which function much the same way.
| | 00:50 | Starting here with the Organic, we see
the organic medium isolated so we have
| | 00:54 | all the data in this report
coming just from the organic searches.
| | 00:57 | When we load the report, it starts with
a default dimension here of keyword, or
| | 01:01 | we can change to any number of other
dimensions available to us. And of course,
| | 01:05 | we can see all of our usual metrics
by which we can evaluate each keyword.
| | 01:08 | Keywords are listed individually here--well,
except for these not-provided keywords.
| | 01:12 | These occur when a user is logged in to
their Google account and does a Google search.
| | 01:16 | In that case, Google uses the
Secure Search which doesn't transmit the
| | 01:19 | exact keyword that the user typed into the
web Analytics tools such as Google Analytics.
| | 01:24 | They submit "not provided" instead.
| | 01:26 | However, we can get some information
about the types of searches that are being
| | 01:30 | done by the landing pages.
| | 01:32 | So here if we click on not provided to
drill down into there, I am going to see
| | 01:35 | a report that's just about not provided.
| | 01:37 | Now the keyword here, not provided,
which is what we've drilled down into, and
| | 01:41 | we can see here on the top in the
breadcrumbs that we went from All to just the
| | 01:45 | Keyword of (not provided), and so rather
than repeating that here, we can switch
| | 01:48 | to a dimension that's more useful.
| | 01:50 | In this case, let's take a
look at the Landing pages.
| | 01:52 | You won't get the exact keywords that
the person did a search on, but by looking
| | 01:56 | at the landing pages that were derived
from the (not provided), we can get some
| | 02:00 | insight into the types of things people
were searching for and where they went
| | 02:03 | on our page when there were logged into
their Google account and thus directed
| | 02:06 | to the Secure Search.
| | 02:07 | It's certainly not the same as having a
real keyword, but it's certainly better
| | 02:11 | than no data at all.
| | 02:12 | Let's go back out to our primary
keywords report, click on the Organic, and we
| | 02:17 | have our list of keywords again.
| | 02:18 | One thing is don't forget to take
advantage of secondary dimensions here, such
| | 02:21 | as a landing page to see which
landing pages the search engines are sending
| | 02:25 | traffic to you for any of these given keywords.
| | 02:27 | To do that, we click on
Secondary dimension here.
| | 02:30 | I can just go ahead and type
in landing page, click on that.
| | 02:32 | What we are going to see is on the left-
hand side, we'll see all of the keywords
| | 02:36 | that are there. We are going to
see them broken out into the individual
| | 02:38 | landing pages in which there's sending it to.
| | 02:40 | You can see that people who typed in
Google Store to their search engines were
| | 02:44 | being sent to this landing page,
which is essentially the homepage.
| | 02:47 | A homepage is the landing page
for a lot of our keywords here.
| | 02:50 | One thing to notice though is that
these will be repeated. Google Store is
| | 02:53 | actually sending people to
do different landing pages.
| | 02:56 | Some of the people who typed in Google
Store got sent to the shopping homepage.
| | 02:59 | Some people got sent to the original homepage.
| | 03:01 | Of course scrolling up here to the top,
don't forget about your performance
| | 03:05 | metrics, such as your Ecommerce and
your Goal tabs, so we can see the actual
| | 03:09 | value of each of these keywords.
| | 03:10 | Here in let's site usage tab, we can
sort by Pages/Visit and that will help us
| | 03:14 | understand which keywords bring
visitors that conduct in-depth shopping
| | 03:18 | expeditions, or we can switch over to
this Goal tab and see the performance
| | 03:21 | based on the completed orders goal, and
we can put in an advance filter to show
| | 03:25 | five or more searches and see which
keywords convert the best in that respect.
| | 03:29 | Let's go ahead and do that. I am going to click
on Completed Order here to sort by that particular column.
| | 03:35 | In this case, I can see that there are
several keywords here and landing pages
| | 03:38 | that have 100% conversion rates, but
there's only one visit, so those aren't
| | 03:42 | quite as useful to me.
| | 03:44 | For these purposes I am going to go
ahead and take the Landing Page secondary
| | 03:47 | dimension off, so I can see my keywords here.
| | 03:50 | I am going to click on Advanced, and I'm
going to make sure that we have at least
| | 03:54 | five visits, actually more than
five visits, in each of these.
| | 03:58 | Click Apply and we'll see that same list,
keywords here sorted by the Completed
| | 04:02 | Order column, with at least six or more visits.
| | 04:07 | This is going to give us a pretty good
idea of which of these keywords have a
| | 04:09 | high conversion rate.
| | 04:10 | We see that most of these are branded,
so we can take this one step further and
| | 04:13 | strip out the branded as we've shown earlier.
| | 04:16 | On the Paid report, we see all the
search engine traffic that was paid for.
| | 04:20 | Now keep in mind this isn't just
Google, but all search engines.
| | 04:23 | We also get an extra
dimension here of Matched Search Query.
| | 04:26 | This is the actual search term that the
visitor typed in resulting in our ad being shown.
| | 04:31 | This is highly useful data, but if
you are looking specifically for AdWords
| | 04:35 | data, you will get even richer data,
such as cost and impressions, in the
| | 04:39 | advertiser set of reports
which are covered in another video.
| | 04:42 | Utilizing these reports properly can
not only give you tremendous insights into
| | 04:45 | paid and organic search, but make an
incredible impact on any paid search engine
| | 04:49 | campaigns you might be running as well.
| | Collapse this transcript |
| Introducing campaign tracking| 00:00 | So in the intro to campaign tracking
video we defined campaign information and
| | 00:04 | talked about how to divvy up our
visits into these buckets, so to speak.
| | 00:07 | But how will this be used by Google
Analytics, and how do we get it in there?
| | 00:10 | Well, lots of reports use campaign
tracking, but the two we most commonly think
| | 00:13 | of are the Campaigns and
there's All Traffic Sources report.
| | 00:17 | Here we can see our
dimensions are Source/Medium.
| | 00:21 | So the first word here is going to be
all the different sources that we've got:
| | 00:24 | google, yahoo, doubleclick, bing, et cetera.
| | 00:28 | On the right side, we're going to see all
the different mediums that those brought:
| | 00:30 | cpc, organic, referrals.
| | 00:33 | So in this case, we can see that
the google was a source twice, but the
| | 00:36 | mediums were different.
| | 00:37 | On the organic side, it brought us
21,000 visits, but on the cpc medium, we
| | 00:42 | have 4,600 visits, both from the same
source, just two different mediums broken up.
| | 00:46 | We also see up here we have
our (direct)/(none) bucket.
| | 00:49 | Remember, when the source is direct
and there's no medium, this means Google
| | 00:52 | wasn't able to determine
any information about that.
| | 00:54 | It doesn't know what the source was, and
it doesn't know what the medium is, but
| | 00:57 | we still have to account for that visit.
| | 00:58 | So it gets under the bucket of
(direct)/(none), which is pretty much the absence
| | 01:01 | of any other information.
| | 01:03 | We also have a dedicated report
just like campaigns. We click here.
| | 01:06 | We're going to see all the different
campaigns listed out and all the visits
| | 01:09 | associated with those campaigns.
| | 01:10 | Remember, this is the overall umbrella
of campaigns, meaning the sources and
| | 01:13 | mediums are all going to be rolled under there.
| | 01:15 | In this case, we have a campaign
that was targeting the Google Store that
| | 01:18 | targeted the English and the Americas.
| | 01:20 | We also have one that was
targeted towards coffee shops.
| | 01:23 | And it's going to be the aggregate of
all the different things underneath it, so
| | 01:26 | all the sources, all the mediums, all
the ad versions are all going to get
| | 01:29 | rolled up into those larger buckets.
| | 01:30 | And as far as how Google populates these
reports, it does its best to figure out
| | 01:34 | as much as it can on its own.
| | 01:36 | So things like organic search engines
are going to automatically detect when a visit
| | 01:40 | comes from a search engine.
| | 01:41 | It's even able to pull out the keyword
from the referring source, anytime we
| | 01:45 | link from one web site to another as long as
we're using sort of a plain-vanilla HTML link.
| | 01:50 | And it's able to figure that out. It
can actually look inside the request and
| | 01:54 | see where it came from and make sure
that it gets into the appropriate bucket.
| | 01:57 | Of course, direct visits are also able
to be detected because direct visits are
| | 02:02 | the absence of information.
| | 02:03 | So if we can't figure anything else out
then it's going to go into the direct bucket.
| | 02:06 | And I put Google AdWords here as being
auto-detected, and this is true only if
| | 02:10 | you have Auto-Tagging turned on.
| | 02:12 | If you turn on Auto-Tagging in Google
AdWords, it's going to automatically apply
| | 02:15 | all these campaign tags so that Google
Analytics understands exactly where it
| | 02:18 | came from, which campaign it was a part
of, which keywords were used, which ad
| | 02:22 | version was shown, et cetera.
| | 02:24 | All the other things you're going to
have to manually tag in order for Google to
| | 02:27 | understand which buckets those
should go into. So most cost per click.
| | 02:32 | We said Google AdWords has the ability
to do auto-tagging, but if you're using
| | 02:35 | other networks, you're going to have to
actually manually tag each one of those
| | 02:38 | destination URLs so that Google
Analytics can understand the campaign
| | 02:42 | information associated with those.
| | 02:44 | Same thing with emails.
| | 02:45 | When we send those out, we need to
make sure we tag our links, banner ads,
| | 02:49 | offline ads, and pretty much everything
else other than the ones that we listed
| | 02:52 | here to be auto-detected,
especially the ones that you pay for.
| | 02:55 | It's a little bit ironic that some of
the most important information to track,
| | 02:58 | the links that we're actually paying
for, are the ones that Google Analytics is
| | 03:01 | least able to track on its own
without this extra tagging information.
| | 03:05 | So this is how this campaign data
is going to appear in the reports.
| | 03:08 | But now we need to talk about how to get
it from an idea in our head into Google
| | 03:11 | Analytics so it can
actually create these reports.
| | 03:13 | Let's walk through an example.
| | 03:15 | Let's say that you're the Acme Box
Company and you want to put out a campaign
| | 03:19 | for your small red cardboard boxes.
| | 03:21 | Part of this campaign is to put banner
ads out on different web sites across the
| | 03:24 | web that are going to point back to
your small red cardboard box landing page.
| | 03:28 | Of course, when someone is out,
visiting a web site that has this banner ad, if
| | 03:31 | they were to click on that banner ad, it
would take them to this page and Google
| | 03:35 | Analytics would run.
| | 03:36 | Now unfortunately, almost all banner ad
systems use tracking systems that will
| | 03:40 | send several redirects and strips
out all the usual tagging that Google
| | 03:44 | Analytics would be able to use to
figure out what site it came from.
| | 03:48 | Even if it didn't, if it was a direct
link from the other site onto your page,
| | 03:52 | you would be able to get that referral
information, but Google Analytics would
| | 03:55 | have no idea what kind of
campaign you want to track this under.
| | 03:58 | It wouldn't know which
version of the ad was shown.
| | 04:01 | Basically, we need to get more
information somehow into that link so that
| | 04:04 | Google Analytics understands which
campaign variables you want this particular
| | 04:08 | visit to be tagged as.
| | 04:10 | Since we'll be doing the tagging,
we get to make that decision.
| | 04:13 | So let's think about this first. Campaign name.
| | 04:16 | Well, this is my red boxes campaign so
we're simply going to call this redboxes
| | 04:20 | as the campaign name, all
one word, all lowercase.
| | 04:23 | The medium is going to be a banner ad,
so we'll just call this a banner.
| | 04:26 | The source is going to be where this
actual banner ad is displayed, in this
| | 04:29 | case boxafficianadomag.com.
| | 04:31 | And if you have different versions of this
ad that are displaying, you can put that here.
| | 04:35 | In our case, this is the one with the
red gradient, so we'll just call that
| | 04:38 | redgradient, all one word, all lowercase.
| | 04:41 | We want everything the person does in this
visit to be tagged under each of these buckets.
| | 04:45 | So we want to know if this person goes
ahead and checks out and buys lots of red
| | 04:48 | cardboard boxes, we want to know that
the campaign, redboxes, had some success.
| | 04:53 | We also want to know that it
was the banner that drove it.
| | 04:55 | We want to know that it was
boxafficianado magazine that was the source of that.
| | 04:59 | So we want all of the things for that visit
to be tracked under each one of these buckets.
| | 05:03 | The way we're going to get that
information in Google Analytics is by tagging.
| | 05:05 | So if the normal URL that someone
would go to be Acmeboxes.com/redboxes.htm,
| | 05:12 | we're still going to put that as
the link where the banner ad goes.
| | 05:14 | Go down to the end of it.
| | 05:15 | We're going to put these
campaign variables here.
| | 05:18 | So in the query string here after the
question mark, we're going to put these
| | 05:20 | query string parameters.
| | 05:22 | So under utm_campaign we're going
to put the campaign name, redboxes.
| | 05:25 | And under utm_medium, we're
going to put the medium, banner.
| | 05:28 | Under utm_source, we're going to
put the source, boxafficianadomag.com.
| | 05:33 | And under utm_region content, we're
going to put redgradient, which is the
| | 05:36 | version of the ad that we showed.
| | 05:37 | So the basic idea here is to transfer
the information we want the visitor to
| | 05:41 | be tracked under by populating these query
string parameters with the appropriate tags.
| | 05:45 | But if you're skeptical about
creating these tags on your own, there is a
| | 05:48 | tool that can help.
| | 05:49 | Let's switch over to the web and take a look.
| | 05:52 | To find this handy web-based tool, just
head on over your favorite search engine
| | 05:55 | and type in "google analytics url builder."
| | 06:02 | Click on the first link that comes up
here and we'll see a tool designed to make
| | 06:05 | this process a little easier.
| | 06:07 | The first thing it's going to ask us for
is what's the page we want to land on
| | 06:11 | when someone were to click on that link.
| | 06:12 | In our case, it was the boxes.
| | 06:18 | Okay, we've got our standard URL there.
| | 06:20 | Now we just have to populate
each one of these variables.
| | 06:22 | The Campaign Source we said
was boxafficianadomag.com.
| | 06:28 | The Campaign Medium we said was banner.
| | 06:31 | This wasn't a pay-per-click campaign.
No one was searching on keywords here, so
| | 06:34 | we're going to leave that blank.
| | 06:35 | Instead, the different version
that we saw was the redgradient one.
| | 06:38 | So under the Content,
we're going to put redgradient.
| | 06:41 | Under the Campaign Name, this was the
redboxes campaign, so we'll fill that in, redboxes.
| | 06:48 | After we filled in all of our variables,
we click Generate URL, and it's going to
| | 06:52 | generate that URL for us.
| | 06:54 | This is the actual URL we're going to
use as the destination for when someone
| | 06:57 | clicks on that banner ad. Okay.
| | 07:00 | So that's how we created this URL down
here, the tagged URL that's going to fill
| | 07:03 | in all that information and allow
Google Analytics to track this appropriately.
| | 07:07 | This was the case with the banner ad,
but let's look at another example.
| | 07:10 | What about we send out an email?
| | 07:12 | We said it was incredibly important to
track email so that it didn't pollute
| | 07:15 | the rest of our reports and so we can
properly track how successful our email marketing is.
| | 07:19 | In this case, let's say we're sending
out an email here about the seminars.
| | 07:23 | It's got a link here where you can
register, and we want to track how many people
| | 07:26 | are coming to the site and registering.
| | 07:28 | The first thing we need to do of
course is lay out our different
| | 07:31 | campaign parameters.
| | 07:32 | In this case, the campaign that I
want to track under is Seminars.
| | 07:35 | So our utm_campaign variable
is just going to be seminars.
| | 07:38 | Now the source is a little trickier
on email because it's not a source
| | 07:42 | coming from a web site. Generally speaking it's a
good idea to put the source here or something that
| | 07:46 | will make sense to you, such as a
particular database of emails that you're using
| | 07:50 | to populate this email or something
else that's going to indicate how this
| | 07:53 | customer is associated with your site.
| | 07:56 | In our case, we're going to call this the
newsletters4s and the medium is going to be email.
| | 08:00 | I do suggest you don't differ too much here.
| | 08:02 | Generally speaking, we want to
keep the medium of email as email.
| | 08:05 | So under the actual link itself, when
you click on the Register Now button, we
| | 08:10 | want you to go to this page here,
websharedesign.com/GoogleSeminars, but I need
| | 08:13 | to populate all those different query
string parameters with the information up
| | 08:16 | here so that Google Analytics
knows to track it under these buckets.
| | 08:19 | So the actual tagged link address would be this,
| | 08:21 | websharedesign.com/GoogleSeminars,
and then we fill in all of those query
| | 08:25 | string parameters here.
| | 08:26 | Now optionally, we still have this
Content field that we didn't fill in.
| | 08:30 | Remember, we used that to show which
version of the banner ad we showed, the
| | 08:33 | redgradient versus the non?
| | 08:35 | We can use that here to actually figure
out things like did they click on this
| | 08:39 | Register Now link or did they
click on the Register Now button?
| | 08:42 | Everything else is going to remain the
same, but I could fill in one link where
| | 08:45 | utm_content would be button and the
other link would say utm_content=link.
| | 08:50 | And then I can figure out from my
Google Analytics tracking which version of
| | 08:54 | that was clicked more often, the
button or the link. Or if I send out two
| | 08:57 | different versions of this email,
maybe one with a black header up here and
| | 09:01 | maybe other one has a white header up here,
| | 09:03 | I can use this to figure out which of
those versions was more successful getting
| | 09:06 | people to click and sign up.
| | 09:08 | Okay, let's go look at how we
would use that tool to help build this URL.
| | 09:12 | Switching back to the tool, we're going to
delete our old entries and fill in the new ones.
| | 09:20 | Here we had the page
that we were going to visit,
| | 09:25 | websharedesign.com/GoogleSeminars, and
we're going to fill in each one of these here.
| | 09:30 | For the Source, we said this was our
newsletters4s database, so we just type
| | 09:33 | in newsletters4s here.
| | 09:36 | For the Medium, we said this was email.
| | 09:38 | We're not using a term.
| | 09:40 | The Content, for this particular one, we're
going to use this for the link for the button,
| | 09:43 | so I'm just going to type in button.
| | 09:44 | And the Campaign Name we said was
tracked under the seminars campaign.
| | 09:48 | We use this, click Generate URL, and
now I've got the URL that I'm going to put
| | 09:52 | as the link that happens
when you click on a button.
| | 09:55 | Now for the link that happens when you
click on the link version, I just changed
| | 09:59 | this up here to link, regenerate the URL,
and now I've got the link that we're
| | 10:03 | going to use on the link.
| | 10:04 | Let's take a look at an example, not an
email or banner ad, but an actual CPC campaign.
| | 10:09 | So if we were doing pay per
click, let's say in this case
| | 10:12 | we're doing one on Yahoo! Remember, Yahoo!
| | 10:14 | doesn't use auto-tagging per se, so
we're going to have to tag these manually.
| | 10:18 | Our campaign here is still seminars,
except now the Source, instead of being the
| | 10:22 | newsletter database, is going to be yahoo.
| | 10:25 | And the Medium, instead of
email, is going to be cpc.
| | 10:27 | We're still going to use the same URL here;
| | 10:30 | we're just going to change the
destination URL to be the tagged link.
| | 10:34 | So we go back to the tool and we
change our Source from newsletter here to be
| | 10:37 | yahoo, our Medium from email to cpc,
and our Content, we can put if we're
| | 10:42 | running different ads.
| | 10:43 | So maybe in this case, we're going to
say learnfromtheexperts because that was
| | 10:48 | the name of this particular ad.
| | 10:50 | You don't have to fill
this in if you don't have it.
| | 10:53 | Campaign Name remains seminar, we
click Generate URL, and now we've got the
| | 10:56 | actual URL that we're going to
use as our destination tagged URL.
| | 11:01 | So we'll follow the same process for
any links that we put out on the web
| | 11:05 | pointing back to our site that we
want to track under these different
| | 11:08 | campaign variables.
| | 11:09 | Campaign tracking is critical to using
Google Analytics effectively, and we'll
| | 11:13 | use these basic concepts introduced
here throughout the rest of the course.
| | Collapse this transcript |
| Planning, creating, and logging a tracking strategy| 00:00 | As we've seen, there's a fair
amount of work and thought that goes into
| | 00:02 | planning campaign tagging strategy
for your organization before you ever
| | 00:06 | actually tag up a link.
| | 00:07 | And because once you tag those links
and get that data into Google, it can't
| | 00:10 | ever be changed, ever.
| | 00:12 | So it's worth taking a video to pass
along some best practices and tools you can
| | 00:15 | use to avoid some of the common pitfalls,
especially in this case, where we have
| | 00:19 | teams in North America, South America,
different product teams, et cetera, all
| | 00:23 | piling data into the same reports.
| | 00:25 | Now Campaign Tagging is great because
it lets us get all of our email blasts,
| | 00:29 | banner ads, and more
tracked in Google Analytics.
| | 00:31 | However, whatever tag you happen to type
for your campaigns in that link will be
| | 00:35 | displayed exactly by
Google Analytics in the reports,
| | 00:39 | so it's important that everyone
tag campaigns in the same way.
| | 00:42 | For example, even capitalization
differences can cause Google Analytics to
| | 00:45 | create duplicate campaigns in your reports.
| | 00:48 | Even worse is that in this case it's
easy to do the mental math and add them up
| | 00:51 | because I've put them here
right next to each other,
| | 00:53 | but usually they won't be
right next to each other.
| | 00:55 | They'll be five pages down and the
other one will be seven pages down and you
| | 00:59 | have 18 different variables that
you're trying to add up in your head.
| | 01:01 | It defeats the entire point of a
tracking system, and it's well worth it to
| | 01:05 | lay out some exact standards ahead of time,
including little things like capitalization.
| | 01:09 | So to avoid this problem, a
centralized tracking sheet can help keep track of
| | 01:13 | which campaigns correspond
to which business initiatives.
| | 01:16 | It's a good idea to plan out what kinds
of Source/Medium tags you'll be using, to
| | 01:20 | avoid confusion ahead of time.
| | 01:22 | You can even use a shared Google
spreadsheet or a traditional spreadsheet file.
| | 01:26 | In fact, if you'd like to use this one
I've already created, I'm happy to share
| | 01:29 | with you all at the link here
at the bottom of the screen.
| | 01:31 | Now to get you started, I'll share
some ideas of common tracking schemes.
| | 01:36 | For example, here are some ideas
for how to track email campaigns.
| | 01:39 | The source can be the various email lists.
| | 01:41 | The content denotes the
messaging used in the email.
| | 01:44 | So in this case, we have some that
offer 20% off, some that offer free
| | 01:47 | shipping, et cetera.
| | 01:49 | Now you can obviously
customize these however you'd like.
| | 01:53 | Remember, the only one that's
somewhat sacred is the medium.
| | 01:57 | You want to see how all your email is
doing against, say, CPC or all your organic,
| | 02:01 | so don't necessarily start
inserting other things in the Email column.
| | 02:04 | Pretty much want to leave that to just email.
| | 02:06 | Here's how you might track banner ads.
| | 02:08 | The source would be the publisher, and
the content can denote the size of the ad
| | 02:11 | or something else interesting about the ad.
| | 02:14 | This is a great way to test and see
what kind of banner ads work for your site.
| | 02:18 | We hear a lot about social media.
| | 02:19 | Does it live up to the hype?
| | 02:20 | Well, here's a look at how
we can track social media.
| | 02:23 | The source can be the social network
itself, while you can use content to describe
| | 02:27 | what type of social content was seen,
i.e. is this the news feed update or
| | 02:31 | is this a link on your fan
page, that kind of thing.
| | 02:34 | By tagging your campaigns consistently,
you'll make it very easy to analyze your
| | 02:38 | various sources, mediums, and content.
| | 02:40 | Here we can see how accurate tagging has
benefited this particular organization.
| | 02:44 | All these mediums are tracked
directly in Google Analytics.
| | 02:47 | Again, a basic system like a
shared spreadsheet can help you avoid
| | 02:50 | inconsistent messy data.
| | 02:52 | Trust me on this one.
| | 02:53 | Taking the time to put some standards
in place will pay dividends in the long run.
| | Collapse this transcript |
| Tracking offline campaigns| 00:01 | Link tagging is great for banner ads and
email and all the online marketing, but
| | 00:05 | did you know you can use the same
principles for your offline marketing as well?
| | 00:08 | All you have to do is make sure that
any link you publish has all the campaign
| | 00:11 | variables and it will work the exact
same way, just like you see here. All right!
| | 00:16 | I could see it now. Somebody is going to have
to explain to their boss.
| | 00:19 | They'll be saying, "I'm telling you, I
watched this course and the instructor got
| | 00:22 | all bent out of shapes,
saying we have to tag our links."
| | 00:24 | And obviously, this isn't going to work.
| | 00:26 | So what are we going to do then? Not tag?
| | 00:28 | Nope, we're still going to tag.
| | 00:28 | We're just going to do it smarter,
so we don't kill the usability of our
| | 00:32 | advertising in the meantime.
| | 00:34 | Let's say you're advertising your site,
explorecalifornia.org, with some rack
| | 00:37 | cards placed at local hotels,
and you want to see how they do.
| | 00:40 | The card is advertising your tours
and it makes reference to a spa special.
| | 00:45 | If we put the regular web address of
explorecalifornia.org, any visitors to
| | 00:48 | that will show up as direct traffic in our
analytics, and we'll lose the ability to track.
| | 00:53 | But if we put that full, super-long
tagged URL, we'll lose, well, our customers.
| | 00:58 | Instead, we're going to put a
vanity URL with a special subdirectory.
| | 01:02 | In this case, we're going to
use explorecalifornia.org/tourmap.
| | 01:05 | Now that subdirectory page doesn't have
to actually even exist as an HTML page.
| | 01:09 | Instead, it's just going to map
to that really long-tagged URL.
| | 01:13 | Rather me explain in theory, let's just
go to the site and actually check it out.
| | 01:16 | So rather than a long-
tagged URL, we're going to say
| | 01:19 | explorecalifornia.org/tourmap.
| | 01:24 | That's our published vanity URL.
| | 01:26 | When I hit Enter, it's going to
redirect me over to this page.
| | 01:29 | It's my standard tours page, but I've
got all my source, medium, content, and
| | 01:35 | campaign names here.
| | 01:36 | So my source here was these are the
hotel rack cards; my medium was a print.
| | 01:40 | My content was this is the one I have
with the California flag in the subdirectory.
| | 01:44 | And the campaign that I'm trying to
advertise here is my tours campaign.
| | 01:48 | And the beauty of setting up all these
vanity URLs is I'm not limited to just one.
| | 01:52 | I can do lots of different versions,
depending on what exactly I want to track.
| | 01:55 | For example, I've got my tour map one
here that went to the place we just saw,
| | 01:58 | but what if I want to test a
different version of the creative?
| | 02:01 | In this case, I'm replacing the
flag with this iconic image instead.
| | 02:05 | Everything else is the same.
| | 02:06 | So we know we can use that utm_
content variable to indicate the different
| | 02:09 | versions of the creative.
| | 02:11 | So instead of the /tourmap vanity URL,
in this case, I'm using the /touring on
| | 02:15 | these particular cards, and I'm
going to adjust the map long version to
| | 02:18 | indicate just that.
| | 02:19 | Let's go back and take a look.
| | 02:21 | And the old one here, this was the
/tourmap that redirected here and said this
| | 02:25 | was the California flag_s.
| | 02:28 | But in this new version of the vanity
URL, which I call touring, everything is
| | 02:32 | going to be the same, except I've
changed this utm_content variable to indicate
| | 02:36 | that this is the one with the bike icon.
| | 02:37 | Now again, these are both
pointing to the same landing page;
| | 02:41 | it's just that the information we tell
Google Analytics is slightly different to
| | 02:44 | indicate what was the original source.
In other words, which rack card did this
| | 02:48 | particular person see?
| | 02:50 | Now if I were to go into Google
Analytics, I could very easily compare these
| | 02:53 | two ad versions to see one worked
better by looking at the Ad Versions Report
| | 02:56 | and simply looking for bikeicon versus
California flag and seeing which one had more conversions.
| | 03:02 | One important thing about vanity URLs in
the associated landing pages is to make
| | 03:05 | them specific to the original ad,
in this case our rack card.
| | 03:08 | The first question anyone asks
themselves when they land on the page is, am I
| | 03:12 | in the right place?
| | 03:13 | We want to keep that same look and feel
and bring in graphical elements, text,
| | 03:17 | and other offers that let us know,
yes, we're in the right place.
| | 03:20 | This is exactly what you were looking for.
| | 03:22 | We also need some call to action here.
| | 03:24 | We don't just want to see
which creative brought more people;
| | 03:26 | we want to see which one
signed up for more tours.
| | 03:29 | So we need to make sure we have that
goal configured in our analytics and that
| | 03:32 | way we can see which ad version
converted on that goal better.
| | 03:35 | So in this case, we want to see which
people clicked on the learn more! button.
| | 03:39 | And there is a potential issue here.
| | 03:41 | What if someone didn't type in the
full vanity part of the domain and instead
| | 03:45 | they just typed in explorecalifornia.org?
| | 03:48 | Well, that's certainly a possibility
and we want to try to avoid that whenever
| | 03:51 | possible because we lose the
ability to track this particular source.
| | 03:54 | It would just get tracked as direct traffic.
| | 03:56 | So the first thing we want to do is have some
compelling reason for them to type it all out.
| | 03:59 | In this case, if you specifically want
to see tour info, then you need to type
| | 04:03 | it in or you're just going
to get the generic homepage.
| | 04:06 | And the second is it helps to give them
some more motivation: a special price, a
| | 04:09 | demo, a free T-shirt, whatever--
| | 04:12 | just something that they won't
get if they truncate that URL.
| | 04:16 | But what could we do to force them to type
in their vanity URL, nonviolently, of course?
| | 04:21 | Well, we could make the
entire thing a vanity URL.
| | 04:24 | In this case, the entire domain is
the vanity URL, so there's no other
| | 04:28 | option but to type it in.
| | 04:29 | So in this case, we're using catours.org.
Let's check out how that would work.
| | 04:37 | If I just type in catours.org,
| | 04:39 | I still get redirected back to the same page.
| | 04:43 | The only issue here is you actually
have to go out and register those domains.
| | 04:46 | So if we're talking about lots and
lots of vanity URLs, it can be somewhat
| | 04:49 | tedious to register and set up lots of
different domains, much more so than it
| | 04:53 | would be just to set up a
simple subdirectory redirect.
| | 04:55 | Let's take a look at a
different one that we're using.
| | 04:57 | Instead of catours, let's
look explorecatours.org.
| | 05:02 | Where do you suppose
we're using this vanity URL?
| | 05:05 | As you may have guessed up here,
we're using these in radio ads because
| | 05:09 | our medium is radio.
| | 05:10 | When it comes to split testing this
physical media, there's a lot we can learn
| | 05:14 | from the direct mail folks;
they've been doing it for years.
| | 05:17 | One of the most creative examples I've
seen of motivating folks not to drop the
| | 05:20 | vanity subdirectory is this one.
| | 05:21 | Now you and I probably know that if we
receive this in the mail with a web site
| | 05:25 | published with our name in the URL,
it's probably just a database-driven page
| | 05:28 | with some customizations, maybe it
has my first name in the greeting, maybe
| | 05:31 | there are some things that are related
to what I've purchased before, et cetera.
| | 05:34 | But there's lots of people out there
for who this would be quite compelling.
| | 05:38 | They would be quite curious to know
just exactly what is going on with this web
| | 05:42 | page that's published on some
web site with their name on it.
| | 05:45 | And if I'm honest, I probably check it out too.
| | 05:47 | Just out of curiosity, even though I
have a pretty good idea of what's going on,
| | 05:50 | it's a pretty compelling thing.
| | 05:51 | Certainly we're not just going to
type in acmeboxcompany.com without
| | 05:57 | putting your name on it.
We don't want to see the homepage.
| | 05:59 | I want to see what's that
page with my name on it.
| | 06:02 | So how do we actually do this mapping?
| | 06:03 | Well, what we'll need to do is set
up a 301 Redirect on our server that
| | 06:07 | redirects from our vanity URL to the tagged
address via this 301 Redirect on the server side.
| | 06:14 | Now I promised I'd avoid code as much as
possible, but this one is just too easy not to show.
| | 06:19 | If you're using the Apache server, it's
as simple as creating an htacess file in
| | 06:23 | your root directory with these four things.
| | 06:26 | You simply have redirect 301, then
the /tourmap is going to be our vanity
| | 06:32 | subdirectory, and then the full tagged link.
| | 06:35 | So I'll just literally type "redirect
301," your vanity URL, and then the fully
| | 06:41 | tagged link, and that's it.
| | 06:42 | This will redirect from here
to here just as we've seen here.
| | 06:45 | Now the only trick to all this is
that you have to remember to always use a
| | 06:49 | vanity URL that's unique
per different tagged address.
| | 06:53 | Otherwise, if you're using the same
vanity URL and your rack card is your
| | 06:56 | radio spot, you won't be able to tell which
one caused the traffic and which one worked.
| | 07:00 | Tracking offline traffic right alongside
your online traffic is usually valuable
| | 07:03 | and usually quite illuminating, and it
works just like online traffic, with one
| | 07:08 | more step in between of a vanity URL.
| | Collapse this transcript |
| Finding data in a Campaign report| 00:00 | At this point, we've planned out
our campaign tracking strategy;
| | 00:03 | we've tagged our links in our ads,
emails, and other online advertising;
| | 00:07 | we put vanity URLs in our offline media; and
we are ready to see our results in our reports.
| | 00:12 | Now if we come down here and we look
at the Campaigns report, under Traffic
| | 00:16 | Sources, this report functions very
similarly to the All Traffic report, in that
| | 00:21 | we can change our primary dimension up
here to Source, Medium, Source/Medium
| | 00:25 | combination, or any of these
other dimensions that are available.
| | 00:29 | We can view all the same groups
of metrics that we've seen before:
| | 00:33 | Site Usage, Goals Sets, Ecommerce, et cetera.
| | 00:36 | So let's dive in and look
at a couple of campaigns.
| | 00:38 | As I look down through here and see my
list, the vast majority of my visits seem
| | 00:43 | to come from these two campaigns.
| | 00:45 | As I look through here, similar time on site,
both seem to get majority of new visits.
| | 00:51 | So far, they've seem very similar.
| | 00:53 | But of course, we want to look a little deeper;
| | 00:54 | we want to look at the
performance of these guys.
| | 00:56 | So let's come up here.
| | 00:57 | This is a shop, so I am going to
choose the Ecommerce tab, and now we see a
| | 01:01 | completely different story.
| | 01:03 | Despite the fact that they've got
28,000 and 24,000 visits, this coffee shop
| | 01:07 | has generated only $17 in total, while the
California Campaign seems to generate almost $6,000.
| | 01:12 | So what could be going on here?
| | 01:15 | Well, we need to dig a little deeper.
| | 01:17 | We know the campaign names, but we don't
know much about what's inside the campaign.
| | 01:21 | Now our campaign is the overall
umbrella that includes different
| | 01:24 | sources, different mediums,
| | 01:25 | so let's start by there.
| | 01:27 | Let's check a secondary
dimension for these campaigns of source.
| | 01:32 | Both of these campaigns
seem to be coming from Google.
| | 01:34 | I wonder if this is organic
or if this is paid search,
| | 01:38 | so let's go ahead and
change our source here to Medium.
| | 01:42 | Both of these campaigns are coming
from Google cpc, and we see all the visits
| | 01:46 | being generated from this,
| | 01:48 | so we need to dig a little bit deeper.
| | 01:49 | Let's try coming down here and
check in the next one, Keyword.
| | 01:52 | We are starting to see a little bit more here.
| | 01:55 | Despite the fact that there were a ton
of visits here, 17,000 and 10,000, there
| | 02:00 | is very little revenue generated by
either of these under the content targeting.
| | 02:04 | We do see some actual keywords that
were targeted here, which of the 5,000
| | 02:07 | some generated 3,400. Let's keep going.
| | 02:10 | Since I am starting to suspect content
targeting versus search targeting, I can
| | 02:16 | actually go down here to the
AdWords ones in my secondary dimension.
| | 02:20 | We can set up that as the
ad distribution network.
| | 02:23 | This will point out which ad
distribution network is responsible for which ones.
| | 02:27 | Here again, we see lots and lots of visits
here that results in very little revenue.
| | 02:32 | As for Google, search has a lot of visits,
but also results in more of the revenue.
| | 02:37 | I can actually sort this by
revenue and it becomes pretty clear.
| | 02:40 | Between Google search and the search
partners, there were a lot of visits and
| | 02:43 | also a lot of revenue.
| | 02:44 | Here in the California side, we had a
lot of visits on the content network with
| | 02:48 | very little revenue, and on the Coffee
Shops campaign on the content network,
| | 02:51 | we had tons of visits and absolutely no revenue.
| | 02:54 | So at this point, we have a pretty good
understanding of exactly what's going
| | 02:57 | on with our campaigns.
| | 02:59 | So through the campaigns report, we
can evaluate our campaigns overall, or we
| | 03:03 | can really dig into the performance of
our campaigns across different mediums,
| | 03:06 | sources, keywords, ad
network, cities, you name it.
| | 03:10 | You may recall in the very beginning
of this course we showed an example of
| | 03:13 | how we found out exactly how much revenue was
generated via different versions of an email blast.
| | 03:18 | Now my hope is at this point, you
know exactly how that was tracked.
| | 03:22 | We tagged each email with campaign
variables and Google Analytics did the rest.
| | 03:26 | So hopefully now you'll never
send out another untagged email,
| | 03:29 | you will never launch another untagged
banner ad, and as a rule, you'll never
| | 03:33 | launch another marketing initiative
without thinking yourself, now how are
| | 03:36 | we going to identify these visitors
so we can track the performance of this
| | 03:39 | campaign?
| | Collapse this transcript |
|
|
11. Content ReportsAnalyzing top content by metrics and the navigation summary| 00:00 | Ultimately, publishing a web
site is about publishing content, and
| | 00:03 | understanding how that content is used and
consumed is a principal goal of any analytics package.
| | 00:08 | Before we launch into the most
important content report, the Pages report,
| | 00:12 | it's worth discussing the terminology used in
Google Analytics to refer to the parts of the URL.
| | 00:16 | Now most old-school Internet folks may
take issue with this naming convention
| | 00:20 | not being 100% accurate, but for the
purposes of using Google Analytics, it's
| | 00:23 | important to know what they're referring to
when they use these terms, so let's take a look.
| | 00:27 | First, we have this
beginning part here, the protocol.
| | 00:30 | The protocol is going to be your
http:// that we see at the beginning of a URL.
| | 00:35 | By and large, a protocol does
not matter to Google Analytics.
| | 00:38 | Google Analytics takes care of all
this for you, and we don't care at all what
| | 00:41 | the protocol is so http, https,
whatever. It doesn't matter.
| | 00:45 | The next part here is the hostname.
| | 00:46 | The hostname here is anything that
starts with a www, essentially right after
| | 00:50 | the protocol, all the way through to
the end of the .com, .net, .org, whatever
| | 00:55 | this final top-level domain is.
| | 00:57 | All this in between here is going to
be referred to as the hostname, and it's
| | 01:00 | what's you are going to find in the
hostname dimension of the network report, in
| | 01:03 | the visitor's reports.
| | 01:04 | But as far as the content reports go,
we don't care about this either;
| | 01:08 | the only thing we care about in the
content reports is this last part, starting
| | 01:12 | with a slash and including a
slash all the way to the end.
| | 01:15 | While, the URL is this whole thing,
| | 01:17 | we are calling this last part the URI
or the request URI, and this is what's
| | 01:20 | going to populate all of our content reports.
| | 01:22 | This is what's going to be used for
our page, our directories, and everything
| | 01:25 | that we see, as far as the URI is concerned.
| | 01:27 | Now switch into the Pages report
we have here in Content > Site Content > Pages.
| | 01:34 | We can see just that.
| | 01:35 | We see those URIs starting with
a slash and everything after.
| | 01:39 | The Pages report orders these based on
the most popular in terms of the number
| | 01:42 | of Pagesviews by default, as we are
sorting by this metric here, and this can be
| | 01:46 | any page in the site.
| | 01:47 | It doesn't necessarily go down by
directories or pages or alphabetical or
| | 01:52 | whatever content has loaded up in the browser;
| | 01:54 | it's just about how many
times these pages are viewed.
| | 01:57 | I am going to sort it by Default.
| | 01:58 | Now I can sort these by any of these
other metrics that we have over here.
| | 02:01 | We'll look at those in just a second.
| | 02:02 | The small box here with the arrow
pointing out is a link that will allow you to
| | 02:06 | see just what this content is.
| | 02:07 | So if you are not sure what this
particular link is, I can click on this arrow
| | 02:11 | and it's going to launch a new browser
window that's going to show me that exact content.
| | 02:15 | Looking back at these metrics, we now
have a couple of new metrics in our table,
| | 02:18 | things like Unique
Pageviews and Average Time on Page.
| | 02:21 | Now what's really interesting is
when we look at things like Bounce Rate.
| | 02:24 | We've seen this metric before,
but it wasn't actionable.
| | 02:27 | If you see that your entire site has a Bounce
Rate of 90%, what are you going to do about it?
| | 02:31 | Just hope that your visitors stop
bouncing? Jut now we are getting to a point
| | 02:35 | where we can actually take some action
on that Bounce Rate because then people
| | 02:39 | come to a particular page when they
bounce, the biggest thing we can do to fix
| | 02:43 | that is to change the page itself.
| | 02:45 | And so here, I can easily identify
which pages have a high Bounce Rate, which
| | 02:49 | pages have a low Bounce Rate.
| | 02:50 | From there, I can look at the
keywords that brought people.
| | 02:53 | I can look at the sources of traffic,
I can look at what countries and cities
| | 02:56 | they are coming from, all because I
know which page causef the Bounce Rate.
| | 03:00 | Percentage of Exit is the percentage
of those who will leave the site from
| | 03:03 | this particular page.
| | 03:04 | We'll look at that metric in more detail
later when we look at that individual report.
| | 03:08 | One of the things we can do in the Pages
report is filter which of these pages get
| | 03:11 | included in the data table.
| | 03:13 | For example, if I want to see only the
pages and directories that have to do
| | 03:16 | with analytics, I simply go to this
filter, type in analytics, hit Enter--and the
| | 03:21 | Pages report is a very popular report
as it's important to know what the most
| | 03:25 | commonly viewed pages of the site are.
| | Collapse this transcript |
| Sorting top content according to page title| 00:00 | As analysts, we want to understand
what the popular content is on our site,
| | 00:04 | and that's one of the reasons
the Pages report is so popular.
| | 00:07 | The problem is, as we scroll down
through here, it's not necessarily all that
| | 00:10 | easy to get insights from here
because it's not entirely clear to me what
| | 00:13 | some of these pages are.
| | 00:14 | As I see the pages through here, these
are obviously category of pages, and each
| | 00:18 | of these numbers represents one of the
categories of products that corresponds
| | 00:21 | to my database, but I as the analyst
don't necessarily know what those are.
| | 00:24 | So what I can do, instead of using this
report with URLs, is I can come up here
| | 00:29 | and switch this over to Page Title.
| | 00:30 | This is actually going to show me the
page title of the corresponding web page.
| | 00:33 | Now what I can immediately see here are
those nasty URLs that represented the pages
| | 00:38 | for my wearables, my
accessories, my office pages here.
| | 00:41 | Well those are my product categories.
| | 00:43 | So this is clear, and it's immediately
obvious to me in terms of how my products
| | 00:46 | in the corresponding pages
on my site are performing.
| | 00:48 | I don't have to leave this page and
cross-reference any URLs in another browser,
| | 00:52 | or any other convoluted process,
because it's all clearly spelled out for me
| | 00:56 | here as I get a different line
for each single document title.
| | 00:59 | Now it is important to note, this
isn't a one-to-one ratio of URL to title.
| | 01:03 | What I mean by that, if certain
documents or certain web pages have the same
| | 01:06 | titles, then they are going to get
combined into a single line here.
| | 01:10 | For example, we see here there are
almost 170,000 pageviews of this top one here.
| | 01:14 | That's a fairly generic Google Store type title.
| | 01:17 | What this probably means is that we are
reusing the same title for different pages.
| | 01:21 | What we can do to figure this out is we
can actually click and drill down into
| | 01:25 | that particular title, and the next
report is going to show us all the
| | 01:28 | different URLs that are associated
with that title. And just as I suspected,
| | 01:32 | we see a category id of new and our
onsale and our green categories all reuse
| | 01:37 | that same document title.
| | 01:38 | This can happen a lot if we
are using template-based pages.
| | 01:40 | People will use the template and they will
forget to change the title for the new page.
| | 01:44 | And this isn't a great thing from a
usability point of view, and it can also hurt
| | 01:47 | us from an SEO point of view.
| | 01:48 | So this is something I may want to go get fixed.
| | 01:51 | The beauty of this report is I can
easily see all the different pages that
| | 01:54 | share that same title.
| | 01:55 | Now going back to the Content by Title
report, I can do some further analysis.
| | 01:58 | For example, one of the things I want
to look at is Bounce Rate. Especially now
| | 02:02 | that I have the ability to see what
these pages actually are, I can understand
| | 02:05 | what some of the trends are, what
some of the products are popular and if
| | 02:07 | anything jumps out on me.
| | 02:08 | Now when I go over here and I do the
search by Bounce Rate, I first want to see
| | 02:12 | ones that have a high Bounce Rate. The
problem is I am back to that situation
| | 02:15 | where I have got pages in one
Pageview and 100% Bounce Rate.
| | 02:19 | I can come up here to set an Advanced
filter and what I want to say is I want to
| | 02:22 | show all of the ones that have
Pageviews greater than, let's say 100.
| | 02:27 | Now something has jumped out on me right away.
| | 02:29 | I can see that, for whatever reason,
this Organic Cotton Tote Bag in the
| | 02:32 | Accessories category is not doing
too well. Of all the Pageviews that are
| | 02:35 | loaded, 100% of those people bounced right away.
| | 02:38 | Something has gone wrong here,
and it's not something that's
| | 02:41 | particularly appealing to folks.
| | 02:42 | I want to basically go figure that out,
whether it's a problem with the product,
| | 02:45 | whether it's a problem with the page.
I've got some actionable data here that I
| | 02:48 | need to follow up on.
| | 02:50 | As I scroll through this
list, we see lots of examples.
| | 02:52 | I can expand the number of rows down
here to 50, and we can see some that have
| | 02:56 | quite a few Pageviews but they are
still having very high Bounce Rates.
| | 02:59 | For example, our Google Bean Bag has
1600 different pageviews, still sitting
| | 03:02 | around a 80% Bounce Rate.
| | 03:04 | Now we don't always want
to focus on the negative;
| | 03:06 | let's always look at the flip side.
| | 03:07 | We can take this exact same report and
we can flip it around for extremely low
| | 03:11 | Bounce Rate for products that are very popular.
| | 03:13 | Notice that I have still got my advanced
filter on so these Pageviews are going
| | 03:16 | to be relatively high, and
again some things jump out on me.
| | 03:19 | For all these products here, the
Google Aluminum bottle, the Blinky Pin, my
| | 03:23 | Hemp Travel Organizer, Magnetic Game
Set, have a 0% Bounce Rate, despite getting
| | 03:27 | quite a few pageviews.
| | 03:29 | These are very popular
products and ones that do fairly well.
| | 03:31 | I might want to understand a little bit
more about the organic search terms that
| | 03:34 | brought people to the site and see how
I am really getting solid traffic here
| | 03:38 | that's performing very well.
| | 03:39 | This report is full of
valuable actionable information for me.
| | 03:42 | As analysts, much of our job is not
about getting the raw data, but rather
| | 03:46 | getting the data in an organized way so
we can easily pull out insights that we
| | 03:49 | can actually do something with. Having
human-readable titles, that can often make
| | 03:52 | it much easier for us to pull out
the data or to share it with others.
| | Collapse this transcript |
| Understanding when to use content drilldown| 00:00 | Depending on your site's architecture,
the Content Drilldown report provides a
| | 00:04 | unique and very efficient reporting
tool to analyze your site's content.
| | 00:07 | Now we've looked earlier at the Top
Content tool, and what this does is gives us
| | 00:10 | that idea of the individual items, but
it doesn't make it easy to group them, and
| | 00:13 | this can be sometimes misleading.
| | 00:15 | For example, if we look down here at
the Top Content Report for the webShare
| | 00:18 | site, we might think that the locations
subdirectory is actually one of the more
| | 00:21 | popular ones because it's
here on top of the report.
| | 00:24 | But if we look down further, we see that
this tools directory pops up a few times.
| | 00:28 | Now the problem here is that this is
showing the individual tools, which all
| | 00:32 | happen to live under the tools directory.
| | 00:34 | If I want to understand the overall
sections of the site that are going to be
| | 00:37 | the most popular, what I need to do is
find a way to figure out how to group all
| | 00:41 | those tools together and compare them
to all the other things grouped together.
| | 00:45 | If you have got it organized into
subdirectories like this, it's actually very easy to see.
| | 00:48 | We simply go up and click on the
Content Drilldown, which is going to start at
| | 00:52 | the root of my site, which is just the
slash, the thing right after my domain
| | 00:55 | name, and show us all the things that
are the most popular based right off of
| | 00:59 | the root, in other words, all the
subdirectories and pages that live right off
| | 01:03 | of that first slash.
| | 01:04 | Now what we can see in this case is
that the locations one that was up there at
| | 01:07 | the top now has gotten bumped down a couple.
| | 01:10 | Actually the /tools
subdirectory and the blog are more popular.
| | 01:13 | Now we can go further than this.
| | 01:15 | If I want to see which individual
tools are more popular, I simply click into
| | 01:18 | the tools subdirectory, which is
going to drill down into there.
| | 01:20 | What we'll see here that everything
I'm going to see from now on is based on
| | 01:25 | just things that are under the /tools directory.
| | 01:28 | So for example, we can see which of
those pieces of content underneath the
| | 01:30 | tools subdirectory, in this case,
which particular tool is the most popular.
| | 01:34 | I can see that the ad-split-testing-
tool is most popular, followed up by the
| | 01:37 | sample-size-estimation-tool.
| | 01:39 | All of the stats and metrics that I
would get for here are just going to be
| | 01:42 | based on things inside
of the tools subdirectory.
| | 01:44 | All the information that we see here in
the Content Drilldown is going to be the
| | 01:48 | exact same content that we see
inside of the Top Content report;
| | 01:52 | it's just been grouped into subdirectories.
| | 01:54 | Now, not every site can take advantage of this.
| | 01:56 | For example, if we flip over to the
Google Store example and we scroll down here
| | 02:00 | and look at the Content Drilldown, we
see that it's not easily divided into
| | 02:03 | subcategories or subdirectories. All the
content is based off the root and there
| | 02:07 | isn't any clear way to drill down or
compare sections of the site, because
| | 02:11 | that's not how their site
architecture was set up.
| | 02:13 | For sites that are organized by
subdirectories, the Content Drilldown allows you
| | 02:16 | to browse the content reports using
the similar hierarchies you would see on
| | 02:20 | your actual web site, and therefore as
an analyst, you get a much clearer picture
| | 02:23 | of how each content area is performing.
| | Collapse this transcript |
| Measuring the importance of top landing and top exit pages| 00:00 | Now we'll take a look at the Top
Landing Page and Exit Page reports.
| | 00:03 | Landing page analysis is really important.
| | 00:05 | There is a tight correlation between
the success of a site and lowering the
| | 00:09 | bounce rate and one of the surest ways
to lower your bounce rate is to improve
| | 00:11 | the pages that they land on to
be compelling enough to stay.
| | 00:14 | The Top Landing Pages, we can see
here that these are the most common
| | 00:18 | landing pages, the bounces, the bounce rate,
but it's not necessarily the most actionable.
| | 00:22 | It's hard for me to take action on
this as a simply aggregated top report.
| | 00:26 | Usually what we would do if we want to
improve the landing pages is we would do
| | 00:29 | something like look at the keywords
that brought them there and understand why
| | 00:33 | they came. Did they do a search on a
particular set of keywords that are going
| | 00:36 | to give us insight into what they
were looking for when they hit the page?
| | 00:39 | So for example, if I came here to the
Keywords report, and found that people
| | 00:42 | came to my site looking for
Google Consulting Services.
| | 00:45 | Now hopefully they landed on the google
consulting services page; otherwise, they
| | 00:49 | might be likely to bounce.
| | 00:50 | So I come in here and I set my
Segmentation option here to be the Landing Page
| | 00:54 | dimension, and then I can see all the
different pages the people landed on.
| | 00:58 | This is going to help me understand
how I can improve my landing pages much
| | 01:03 | better than the Top Landing Pages
report, which is simply aggregated across all
| | 01:06 | traffic sources and all Keywords.
| | 01:08 | Another good example of how
we can analyze landing pages.
| | 01:11 | Let's say, for example, that we're
running a banner ad campaign selling small
| | 01:15 | red cardboard boxes from Acme Industrials.
| | 01:19 | Now one option might be to send people
from the banner ad right to our homepage.
| | 01:23 | Another option for a different landing
page might be to send them directly to
| | 01:26 | the page that sells small red cardboard boxes.
| | 01:30 | Now most of you are probably guessing
that the page that sends directly to
| | 01:33 | the product is going to perform better, but
of course we don't want to rely on speculation.
| | 01:37 | We need to make sure
that the data backs up that.
| | 01:40 | What we can do is take a look
at these different campaigns.
| | 01:42 | We can see here that the landing page
for this particular campaign, Small Red
| | 01:47 | Cardboard Boxes versus the Homepage.
| | 01:49 | What the data tells us is that even
though the visits were about the same,
| | 01:53 | approximately 1100 for each one since
we did a somewhat evenly split test,
| | 01:57 | look at the difference in revenue,
$3500 for those who landed on the Small Red
| | 02:00 | Cardboard Box versus just 707
for those who hit the Homepage.
| | 02:04 | It's clear here our speculation was
correct; in this case, the detail page was
| | 02:08 | far better of a
landing page than the homepage.
| | 02:10 | Now let's take a look at Top Exit Pages.
| | 02:13 | Top Exit Pages are going to show
which pages our visitors are leaving from.
| | 02:17 | Now this isn't exactly like a bounce.
| | 02:19 | A bounce means that the page they leave
from was the page that they entered on,
| | 02:23 | and a bounce is generally a bad thing.
We want people to go ahead and click on
| | 02:26 | more pages of our site
and interact with our site.
| | 02:28 | An exit page has to happen.
| | 02:30 | You have to leave somewhere.
| | 02:32 | You can't stay on our site forever.
| | 02:33 | So it comes much more of a case of
which particular page did you leave from.
| | 02:38 | In this case, let's take a look at
this one where I have two different pages
| | 02:41 | here. The first one is the
genericGuarantee, and then I have changed it to be
| | 02:45 | 100percentBuyBackProgram.
| | 02:47 | What I'm hoping is that after you
read my guarantee, you are going to go on
| | 02:50 | and buy things from me.
| | 02:52 | So what I'm hoping in this case is
that this is not going to be the last page
| | 02:55 | that you see, and I am going to judge
the success of these two by which one gets
| | 02:59 | people to move on to the next page.
| | 03:01 | In this case, we can see the
genericGuarantee had a 58% exit rate while the
| | 03:05 | 100percentBuyBackProgram had only a 10%.
| | 03:08 | In this case, the
100percentBuyBackProgram page was far more successful at
| | 03:12 | getting people to move on and
not leave the site at that point.
| | 03:15 | Now on the other hand, if we found
that the most common exit page on the site
| | 03:19 | was my Thank You page after completing
my shopping cart, that would be fantastic.
| | 03:23 | That's where I want everyone to leave.
| | 03:25 | It's very important to understand
how your visitors reach your site and what
| | 03:28 | elements of your site are driving them away.
| | 03:30 | Whether you use the Top Landing Page
and Exit Pages or whether you view those
| | 03:34 | landing pages as evaluation criteria
for other campaigns, keywords and sources,
| | 03:38 | this is a critical piece of our analysis.
| | Collapse this transcript |
| Identifying slow-performing pages with the Site Speed report| 00:00 | The Site Speed report measures the
page load time or latency for a sample of
| | 00:04 | visits and shows us the average page
load time for visits that were sampled,
| | 00:08 | along with the total number of page views,
the number of page views Google sampled
| | 00:11 | to give us that average load time,
bounce rate and exit rate for each page.
| | 00:16 | With this report you can see which
pages loaded the fastest and which
| | 00:19 | ones loaded the slowest.
| | 00:20 | Going to this report here, under
Standard Reporting, in the Content section
| | 00:24 | here in the Site Speed report, one
of the things that we can do is sort by
| | 00:28 | this first column here.
| | 00:30 | This is going to show us the pages which
loaded the slowest or had the longest load time.
| | 00:35 | Another way to look at this is
through the Comparison view here.
| | 00:37 | We can do the Comparison view.
| | 00:39 | I like to extend my number of rows
here, and I can look down this list and
| | 00:43 | quickly get an idea of which pages are
performing well and which pages are very
| | 00:47 | concerning to me that I
might want to look at first.
| | 00:50 | With this type of data, it makes
easy for us to hit the pages that are
| | 00:53 | poor performing first.
| | 00:54 | We certainly want to look at those and
see what we can do to improve the load
| | 00:57 | time for those pages since the load
time can affect both user experience and
| | 01:01 | potentially even your SEO, if it's bad enough.
| | 01:03 | Now once we have spent the time improving
those pages, we want to come back to this report.
| | 01:07 | We can use the Compare to past
feature up here in the Date Range selector.
| | 01:13 | That way we can see if the improved
page-load time has also lead to things like
| | 01:17 | improved bounce rate, exit
rates, or even our organic traffic.
| | 01:21 | Where this report gets even more
helpful is if we look at some of the
| | 01:24 | other dimensions here.
| | 01:25 | We could use something like Landing Page or
any other dimensions available in this list.
| | 01:30 | Landing pages are a first
impression for our visitors,
| | 01:32 | so a slow page load time could have an
impact on our bounce rate or just their
| | 01:36 | overall impression of the site.
| | 01:38 | But we can use this report from more
than just content; if instead we set our
| | 01:42 | dimension here to something like
Operating System, now we can start to get some
| | 01:50 | insights on how our site performs on
desktop platforms versus some of our mobile
| | 01:54 | and tablet platforms.
| | 01:56 | If we select this back to the non-
comparison view in our regular Data format
| | 02:00 | and sort this by Average Page Load
Time, we can see here that the worst
| | 02:04 | performer is our Android operating
system, which usually indicates a mobile
| | 02:08 | platform such as a phone or a tablet.
| | 02:10 | Since this is only affecting less than
1% of our page views, so might not be a
| | 02:14 | big deal, but mobile
traffic is increasingly important,
| | 02:17 | so it might be another piece of
evidence to suggest that we need a more
| | 02:19 | mobile-friendly site.
| | 02:21 | Like so much of our analysis, this is
not to tell you what to do, but simply
| | 02:25 | inform your decisions with the right
data so you can make the right choice.
| | 02:29 | So that's part one of Site Speed report,
but there are two other tabs up here
| | 02:32 | that can show us some different data.
| | 02:34 | The Performance tab allows us to see
sampled page views grouped by varying levels
| | 02:39 | of average page-load time.
| | 02:40 | So now we can see how much of our
traffic is having problems with load time.
| | 02:44 | In the case of this site, it looks like
most of the site is doing well enough,
| | 02:48 | but there's about 10% of these sample visits
| | 02:50 | that's of page-load times of 13 seconds
and more, which is definitely a problem.
| | 02:54 | Now some of this might because the
user was on a slower connection such as a
| | 02:57 | mobile connection, but this data at
least lets us know how big of a problem site
| | 03:01 | speed is for our users. Even five
seconds can seem like a long time and deter
| | 03:05 | some users who were
looking for quick information.
| | 03:08 | Our third tab up here is the Map Overlay.
| | 03:11 | Here we can see Average Page Load
Time and other metrics by country, city,
| | 03:14 | continent, or subcontinent
region. And this is important,
| | 03:17 | since the geographic distance and
network congestion can actually impact certain
| | 03:21 | parts of the world and not others.
| | 03:23 | So it may look like your page loads
fine in certain places, but other certain
| | 03:26 | parts may be impacted much more severely.
| | 03:29 | In this particular case, we don't see
anything too terribly insightful in this.
| | 03:32 | Let's switch to another profile and see if we
can see something a little bit more drastic.
| | 03:40 | Here we see a different story.
| | 03:41 | In North America, we don't seem to
see I have too much of a problem here.
| | 03:45 | Page Load Times are relatively quickly,
but we do see some spots in Europe as
| | 03:48 | well as Asia that are having more
significant problems that we'll probably
| | 03:51 | want to investigate.
| | 03:52 | Page-load time might not be the
number one issue for your site, but these
| | 03:55 | reports can help you better
understand how much of your traffic is having a
| | 03:58 | problem with this, where you are
having the biggest problems, so that you can
| | 04:01 | take action and deliver a better,
faster user experience to your visitors.
| | Collapse this transcript |
| Understanding the Site Search and Usage report| 00:00 | We have tools in our analytics tool
belt that will tell us what happened, but
| | 00:03 | very few that will tell us why.
| | 00:05 | To us analysts, user intent is the
elusive holy grail that we are always
| | 00:08 | searching for, and I am a huge fan of
internal site search because it sheds a
| | 00:12 | little light on that fantastic combination of
not just what they did, but why they did it.
| | 00:17 | Let's back up a little bit and talk
about exactly what we mean when we say
| | 00:19 | internal site search.
| | 00:21 | We are not talking about a search engine,
such as a Google.com search that you
| | 00:24 | would access from up here in the toolbar.
| | 00:27 | Internal search engines are the search
engine that searches just the content
| | 00:30 | inside your own site.
| | 00:32 | For example, here in the Google Store,
it's here where it suggests we enter a
| | 00:35 | keyword or item number.
| | 00:36 | If I did a search here for say, android
t-shirt, it's going to search just the
| | 00:43 | Google Store for those items.
| | 00:45 | Now it does get a little confusing
because Google also powers the back end of
| | 00:48 | many internal site search engines,
either through actual hardware computers that
| | 00:53 | you put in your network or through
software known as the custom search engines,
| | 00:56 | such as the one we use
here on the webShare site.
| | 00:58 | You can see that here even that it says
Google Custom Search, this is definitely
| | 01:03 | an internal site search engine and not
a Google.com search of the overall web.
| | 01:07 | Now it's important that we make
this distinction clear, not just from a
| | 01:10 | usability point of view, but for us
as analysts, because while a Google.com
| | 01:14 | search may have brought you to this site,
what you search for once you're on the
| | 01:18 | site tells us a different story altogether.
| | 01:20 | In fact, an interesting trend is that
external searches has often had nothing to
| | 01:24 | do with internal searches, even from
the same user, because they are trying to
| | 01:27 | accomplish very different things.
| | 01:28 | An external search is about locating a
web site that will serve the general need,
| | 01:33 | if you need to buy Google-branded gear to
find the official Google merchandise store.
| | 01:37 | But once you get to the store, you are
going to type in something specific, like
| | 01:43 | "Flashing Yo-Yo" or "kickball" or perhaps
even an android bike jersey, but you are
| | 01:47 | going to be searching for things that
are only inside the store, not trying to
| | 01:50 | locate the store itself. And the same
logic applies for more generic searches.
| | 01:54 | Let's say we do a Google search
for google consulting services.
| | 02:01 | In this case, you are probably
looking for a company that offers Google
| | 02:04 | consulting services.
| | 02:05 | But once you arrive on their site,
you'll likely to look for something more
| | 02:08 | specific, such as AdWords management or
pricing or something else that relates
| | 02:12 | specifically to the product itself,
not the general company as a whole.
| | 02:17 | The Site Search reports will extract
an amazing amount of information about how
| | 02:20 | these searches affect your
site. Let's take a look.
| | 02:22 | Here in the Overview page we'll see
the key metrics about those visits that
| | 02:27 | contained a search, and it will also
help us answer important questions such as,
| | 02:31 | How do visitors who searched
compare to those who didn't?
| | 02:34 | Which search terms did visitors use?
| | 02:36 | And insightful things like, where did they
start their search and what did they find?
| | 02:40 | So what if you don't have an internal site
search engine, did you care about all this?
| | 02:43 | Absolutely, and we need to look no
further than the Usage report to see why you
| | 02:47 | might want to reconsider that.
| | 02:49 | Here we see an example of report
showing us how many visits utilized the search
| | 02:52 | functionality, and the results
are fairly typical, at around 25%.
| | 02:56 | However, as we switch away from just
visits into something more interesting,
| | 03:00 | like Goal Conversions, we see
results that are also very typical.
| | 03:04 | The majority of our goal conversions
come from people who used site search,
| | 03:08 | despite the fact that they were
much smaller percentage of traffic.
| | 03:11 | Site search is very important to
conversions, which are important to us,
| | 03:14 | so these reports we will want to pay
special attention to and if you don't have
| | 03:18 | a site search engine, you may want
to consider putting one on your site.
| | 03:20 | As you can see, internal site search is
very important to conversions, which are
| | 03:24 | very important to us,
| | 03:25 | so these are reports we'll
want to pay special attention to.
| | Collapse this transcript |
| Analyzing the Search Terms and Search Term Refinement reports| 00:00 | When analyzing site search, the most
important thing is, well, the actual terms
| | 00:04 | that were searched on.
| | 00:05 | But the search term report
offers much more than just a list.
| | 00:08 | We see several other
metrics here that offer insight.
| | 00:11 | Total Unique Searches, which is the
number of visits that included search
| | 00:14 | terms, Results Pageviews/Search, which
is the average number of search results
| | 00:19 | pages that were viewed.
| | 00:20 | We see the percentage of search exits.
| | 00:22 | This is the percentage of
searches that resulted in a site exit
| | 00:25 | immediately after the search.
| | 00:27 | In other words, people did the search,
they didn't find what they wanted, and
| | 00:30 | they left the site altogether. Not a good thing.
| | 00:32 | Percentage of search refinements, in
other words, a percentage of searches that
| | 00:35 | resulted in an additional
search beyond that initial term.
| | 00:39 | Time after Search, the time spent on
the site after they did the search. And
| | 00:42 | Search Depth, which is the number
of pages they viewed after searching.
| | 00:46 | Now when we set up site search, which
we cover in another movie in this chapter,
| | 00:50 | we have the option to set up categories as well.
| | 00:52 | For example, on the Google Store
site, next to their search box they have
| | 00:57 | a dropdown that allows you to choose which
category of items you want to see results for.
| | 01:02 | So here a user could search for
YouTube accessories rather than all of the
| | 01:06 | YouTube items, including
apparel, apps, kid's items, et cetera.
| | 01:09 | Now, when we go back to the report, if
we select Site Search Categories, we will
| | 01:13 | see a list of the categories of people searched
within and the same metrics as we saw before.
| | 01:18 | Now in the case of the Google Store the
All category includes any searches users
| | 01:23 | made within the store in All
Products categories selected.
| | 01:27 | Now the not set one here, that includes
all searches made from the front page of
| | 01:31 | Google Store where there
isn't a category dropdown.
| | 01:33 | So in the category field in that case is
not set. And then the rest of these are
| | 01:38 | categories that are available on the dropdown.
| | 01:40 | So see Wearables here.
We see wearables there.
| | 01:42 | We have got offers here.
We have got offers there.
| | 01:44 | These all match up with the
appropriate category inside the report, that is, in
| | 01:49 | the dropdown on the search box.
| | 01:50 | In this viewing report, we can
compare groups or products based on these
| | 01:54 | categories offered by
our internal search engine.
| | 01:56 | Now if we drill into this Wearables
category, it will show us the list of search
| | 02:00 | terms within that category. And then if
we drill into a search term from here,
| | 02:04 | like Android, we will see a list of the
destination pages that the searcher selected
| | 02:08 | from the search results page for that term.
| | 02:11 | We can check this list to make sure the
right pages are showing up for a given term.
| | 02:15 | If we are seeing pages that
shouldn't be there or pages missing that
| | 02:18 | definitely should be there,
| | 02:19 | we can use this to inform us when we
need to adjust that site search tool. Or
| | 02:25 | we can select Refined Keyword to
see a list of search terms that these
| | 02:29 | searchers entered after their first search
failed to produce the results they were looking for.
| | 02:33 | Here we can see a little
more of that list zoomed in.
| | 02:35 | Now at first that may seem like a very
narrow thing to track, but it's actually
| | 02:39 | becoming increasingly important, due to
the trends and search behavior, as recent
| | 02:43 | studies have shown that our
search behavior is changing.
| | 02:46 | When the web first started we would
do a search and then we'd page though
| | 02:49 | results looking for what we want.
| | 02:51 | However, search engines have gotten so
good at figuring out exactly what
| | 02:55 | we are searching for when we type a
given phrase that we come to trust them so
| | 02:58 | heavily, we expect the right results to
be there right at the top of the page.
| | 03:02 | What we are seeing is that if a
searcher doesn't find the results at the top of
| | 03:06 | the results page, rather than clicking
onto the second, third, fourth pages of
| | 03:10 | results hoping to find it, people
actually assume that they didn't properly
| | 03:14 | type in their search, so to be clearer
in their request, they will search again
| | 03:18 | and refine that search.
| | 03:20 | So understanding the relationship of
the first search along with the subsequent
| | 03:23 | refine search can give us great insight
into our patterns and the intents of our users.
| | 03:28 | In this example we can see the
users who initially search for android
| | 03:32 | within the Wearables category refine their
search to things like hat, shirt, pillow, et cetera.
| | 03:37 | This is a fantastic source of that user intent.
| | 03:40 | Here people are basically telling us outright
what kind of android apparel they want to see.
| | 03:44 | We can also see that other users
refine their search to include things like
| | 03:48 | Linux or Google, so if we have
products related to those terms, we might want
| | 03:51 | to include these types of products
and "you might also like these products"
| | 03:54 | section on our android pages.
| | 03:56 | This is fantastic information for us to
use when we update our pages, change the
| | 04:00 | way we present the user the
information, or maybe even change what items we
| | 04:04 | stock in our store entirely.
| | 04:06 | As we have seen here, the Search Term
and Refinement reports are the foundation
| | 04:09 | of the internal site search reports.
| | Collapse this transcript |
| Using the Site Search Pages report to understand how users search| 00:00 | The Site Search Pages report can help
us understand how our visitors are using
| | 00:03 | our Site Search tool to
find content on our site.
| | 00:06 | It can help us identify pages that are
confusing, vague, or otherwise frustrating
| | 00:10 | to users, because one of the most
important things we need to know when
| | 00:13 | evaluating a particular search is
where was the person on our site when they
| | 00:17 | performed that search, and what
were they seeing at the time?
| | 00:20 | After all, as site owner performing
analysis on my own site, if the visitor is
| | 00:24 | at Cardinal Paths Adwords management
page and they type in an internal search
| | 00:28 | for Google Analytics Consultant, that's okay.
| | 00:31 | You want them to move from one
section of the site to the other. But if
| | 00:33 | they're already are on our Google
Analytics Consulting page, and then they
| | 00:36 | type in Google Analytics Consulting
in the internal search box, well, then I
| | 00:40 | have a big problem.
| | 00:41 | So for good reason, the Start Page
location is the default dimension for this
| | 00:45 | Site Search Pages report, as this
view shows us where our visitors are
| | 00:48 | beginning those searches.
| | 00:50 | Let's take a look at an example
from the Google Store here.
| | 00:52 | Let's say we're doing analysis on the page
that hosts merchandise related to Google apps.
| | 00:57 | This down here is the page that I'm
interested in, with a somewhat cryptic file name.
| | 01:01 | In this case this it is our
main category page, Google apps.
| | 01:04 | We see that there are over 9700
unique searches started from that page.
| | 01:08 | We'll click down to drill down on that
page of interest, and because of some
| | 01:12 | oddities of the way that
our web site's database works,
| | 01:14 | we have some erroneous data in here.
| | 01:16 | And this isn't common, but
it's not uncommon either.
| | 01:19 | So if you see this in yours, it's easy
enough to correct via an advanced filter.
| | 01:23 | In our case I'm going to apply a
regular expression that will remove those
| | 01:26 | digits that starts with 10.
| | 01:27 | I am going to exclude this, start
with 10, and removes anything there after.
| | 01:38 | We apply that and then now one rises to the top.
| | 01:40 | This one I want to pay attention
to, Google Apps Bumper Stickers.
| | 01:43 | We see that a lot of people who are on
this page were searching not just for
| | 01:46 | Google apps items, but one in
particular, the Google Apps Bumper Stickers.
| | 01:49 | So there is a clear action we
can take away from this analytics.
| | 01:53 | People on this page who feel they
need to begin a search are overwhelmingly
| | 01:56 | looking for this product.
| | 01:57 | So we need to update that page to make
sure that these bumper stickers are clear
| | 02:01 | and center in the front, or if we
don't currently offer them, we probably
| | 02:05 | should, and feature them here.
| | 02:07 | The opposite of the Search Start Page
is the Search Destination Page report,
| | 02:11 | which rather than showing us which
pages they started the search on, these will
| | 02:14 | show us which pages did
they go to after the search.
| | 02:18 | This report shows us not just what
pages are showing up in our internal
| | 02:21 | results, but which of those pages are
ultimately selected by the user from the search results.
| | 02:25 | It can be accessed by clicking here on
the original Site Search Pages report
| | 02:30 | and then selecting the middle
bar here to Destination Page.
| | 02:33 | Determining the destination pages can
be important in showing the relevant
| | 02:36 | results or showing up for the
keywords that visitors are typing in.
| | 02:40 | Now one thing that jumps out to me here
right away is this one labeled exit.
| | 02:44 | 152,000 people searched, weren't
happy with the results, and left my site.
| | 02:51 | So let's take a look at what caused that.
| | 02:53 | We can drill down in here to that particular
page, which isn't actually a page; it's the exit.
| | 02:57 | What we're going to get is a list of all
the terms here that caused people to do
| | 03:01 | a search and become dissatisfied with the
results enough to leave the page at
| | 03:04 | that point; in fact, they
left my entire site that point.
| | 03:08 | So we get a list of all the terms
here that caused people to do a search and
| | 03:12 | become dissatisfied with the results
enough to leave the page and the
| | 03:14 | entire site at that point.
| | 03:15 | And there's a lot of action
we can take from this page.
| | 03:19 | If these are items that we don't sell,
well maybe we should consider selling them.
| | 03:22 | Worse yet, if these are items that we
do sell, we need to understand why people
| | 03:26 | weren't clicking into these results.
| | 03:28 | Were the results not presenting the proper page?
| | 03:30 | Was it not clear to the user
that this page had those results?
| | 03:33 | Whatever the case in your site, you can
use this report as the glue to connect
| | 03:37 | the dots from what the user did to the
structure of your actual site and how you can fix it.
| | 03:42 | Another way to come at
this is from the keyword side.
| | 03:45 | Instead of looking at a destination
page and then seeing the keywords, we
| | 03:49 | can look at a particular keyword and see
which destination page people chose from that.
| | 03:54 | So I can come back here to the Search
Terms report and look at the different
| | 03:58 | keywords that people were searching on.
| | 04:01 | I want to select one of these
keywords that people searched on,
| | 04:03 | let's just say in this case android.
| | 04:04 | I'm going to apply another filter here
to remove some of these erroneous search
| | 04:08 | pages and my list will become much cleaner.
| | 04:10 | I'm going to get rid of those that are
actual search pages so I can see which ones
| | 04:19 | actually got clicked on.
| | 04:20 | Now I see that a fair amount of people exited,
but I'm interested in these next two here.
| | 04:24 | In fact, I'm curious about this one
down here, the Android+Restroom+Sign.
| | 04:27 | So I'm going to copy that URL and come up
here and see what that page is all about.
| | 04:35 | This is the Android Restroom Sign T-Shirt.
| | 04:38 | Given that this is the second-most
popular page after the original one, now if I
| | 04:42 | go back here to the list, I'll see that
this was the second-most popular result
| | 04:46 | after the main android page.
| | 04:48 | So if I go to the main Android
T-Shirt and look what that says--
| | 04:51 | let's take a look at that.
| | 04:57 | One thing I noticed here is that the
most popular result for a specific T-shirt is
| | 05:01 | not even featured here on the main T-shirt page.
| | 05:05 | We can surmise that we might find
success by moving it here, given its
| | 05:08 | popularity as a search result destination page.
| | 05:10 | As you can see, there's a ton of insight
to beginning from the Start and Destination
| | 05:14 | Pages report if we take the
time to really analyze them.
| | Collapse this transcript |
| Configuring Site Search| 00:01 | After seeing the amount of information
that can be pulled from the internal site
| | 00:04 | search reports, you may assume
there's a lot of work to integrate analytics
| | 00:07 | within your own site.
| | 00:08 | But actually configuring you internal
site search couldn't be much easier.
| | 00:11 | First step, you need to actually
have a site search on your site.
| | 00:14 | If you don't, Google will of course
suggest their own, but it's not a requirement.
| | 00:18 | Google Analytics site search reports
work with all kinds of different site
| | 00:22 | search engines from lots of different vendors,
| | 00:23 | so don't think you have to have the Google one.
| | 00:25 | Now with that said, if you need one,
the Google one works very well and they
| | 00:29 | offer a free version with ads or an ad-
free one for just about 100 bucks, which
| | 00:33 | is quite reasonable considering a full-
blown custom search engine can run into
| | 00:36 | the six figures or more per year,
depending on volume and features.
| | 00:40 | So it's not a requirement, but they do
offer a free and inexpensive version if
| | 00:43 | you want to get up and started quickly,
and it does work with Google Analytics.
| | 00:47 | Okay, assuming you have one on your site,
the next step is to simply go to your
| | 00:50 | site and do a search. So let's do that.
| | 00:52 | We're going to open our browser
window here to the Google Store site, and we've
| | 01:02 | got our internal search box up here.
| | 01:04 | Now again, we're not talking
about a google.com search.
| | 01:06 | We're talking about the site
search internal to your site.
| | 01:09 | And what we're going to do is
type in something here that's easily
| | 01:11 | recognizable to you.
| | 01:13 | So in our case I'm just going to put
my name in there. That's something I can look up.
| | 01:16 | It shouldn't appear in the URL
and it's going to jump out at me.
| | 01:19 | Now what we're going to do is go up
here to the URL and we're going to look
| | 01:21 | through all this whole string here.
| | 01:23 | And I'm trying to pick out my name
there, and what I'm going to trying to do is
| | 01:26 | grab the parameter right before my
name that actually holds my name.
| | 01:29 | So in this case q is the name of the
variable the name, the name of the query
| | 01:33 | string parameter that holds the
variable that was searched on, the search
| | 01:36 | term, which in this case was my name.
| | 01:37 | What we want to do is tell Google
Analytics that anytime you see this q here,
| | 01:42 | the thing that's held there, the
thing that's immediately after it, is the
| | 01:45 | actual search term.
| | 01:46 | In this case, I would want Corey
Koberg to be the thing that's recorded in my
| | 01:49 | analytics, and so we need to tell
analytics that's going to be held up here
| | 01:52 | in this q variable.
| | 01:54 | And the important thing here is it's
not always going to be a q. It depends on
| | 01:58 | what your search engine does.
| | 01:59 | So this is why you need to go to
your site and check out what your
| | 02:02 | particular variable is.
| | 02:03 | For example, if I went over here to CNN and I
did a search here--I'm going to do the same thing,
| | 02:08 | put my name in there--no results for
my name, but when I look up into the URL
| | 02:13 | here, I see that the word query.
| | 02:14 | So in this case, it's not a q.
It's the full written-out word query.
| | 02:18 | That's going to be the
variable that holds our search term.
| | 02:21 | If I went over here to Yahoo!
| | 02:23 | and I am going to put not up here in the web
search but the actual internal site search,
| | 02:28 | if I put it here and we are on sports,
I am big Illini fans, so I put in the
| | 02:32 | word illini, and I see here that Yahoo!
| | 02:35 | is going to use a p. p is going to be
the variable name that we would have there.
| | 02:38 | So if we're configuring the internal
site search for this site, we would use p,
| | 02:42 | as the variable that holds our search value.
| | 02:45 | Now we've figured out what
the name of that variable is.
| | 02:47 | We need to go over to
Analytics and tell it what that is.
| | 02:49 | Now I come here to the profile that I
want to add site search to. I'm going to
| | 02:53 | click on the little gear icon here.
| | 02:57 | From here, I click on Profile Settings.
| | 02:59 | I'm going to scroll down here to Site
Search Settings, and I'd say that I do
| | 03:03 | want to track site search.
| | 03:04 | It's going to ask me what
that query parameter is.
| | 03:06 | In other words, what is that
variable that holds our search value?
| | 03:09 | For our case it was the q, so I just
simply type in q. Now the next question
| | 03:13 | here is whether or not we want to
strip out query string parameters or not.
| | 03:17 | What this means that it's going to look
at the information it needs to pull out
| | 03:20 | that query string, in other words, the
search value that's there, and then it's
| | 03:23 | going to strip it out of the URL for
the reports, so that we don't have to see
| | 03:26 | that cluttering those up.
| | 03:29 | This is mostly a matter of personal
preference, whether or not you will see
| | 03:31 | those search parameters in your reports or not.
| | 03:33 | I'm going to go ahead and say yes,
because at this point we've already grabbed
| | 03:37 | the information we need to pull that
into our reports, so we can just avoid some
| | 03:41 | clutter there and reduce the
overall number of URLs in our account.
| | 03:44 | The next question is, do we
use categories for site search?
| | 03:48 | This is an optional one down here.
| | 03:50 | I can also include a category parameter.
| | 03:52 | A category parameter is going to be
useful if you're the case like Barnes &
| | 03:55 | Noble here, where you've actually
have different categories of search.
| | 03:58 | So it's not just a single box, but in
this case I could actually select Music,
| | 04:02 | and if were to go do a search such as
this, I would see that my keyword is the
| | 04:09 | actual query string parameter that
holds the search term that I use.
| | 04:14 | Store though is the category parameter.
| | 04:17 | In this case I did a search for
vampire weekend, but I did a store category.
| | 04:23 | So what I need to tell Google
Analytics is that the word that comes after
| | 04:26 | store, in other words what's held in the
store variable, is what's going to be the category.
| | 04:31 | So in that case, if I were doing it
for Barnes & Noble, and I were doing
| | 04:34 | that setup here in Analytics, I would
say the category parameter is store,
| | 04:37 | and it knows that anything comes
after that denotes what the category was
| | 04:41 | that was done in the search.
| | 04:42 | Again, this is completely optional and
many of you will not have a category, but
| | 04:46 | if you do, it's easy to set that up.
| | 04:48 | Okay, we apply changes
and we're done. That's it.
| | 04:51 | That's all we need to set that up.
| | 04:52 | If we come back here, looked in our
reports, went down to Site Search, now when I
| | 05:00 | go back here and actually view the
results for this particular profile, I'm
| | 05:03 | going to come down here to Content, to
Site Search, click on Overview, and what I
| | 05:07 | see is absolutely nothing.
| | 05:08 | There's going to be no values here,
because like almost everything in Google
| | 05:11 | Analytics, it's only going to start
from the time I select it moving forward.
| | 05:15 | In other words, it's not going to go
back in history and look at the overall
| | 05:18 | different searches that took place before now;
| | 05:20 | it's going to count from right now forward.
| | 05:22 | There is only one caveat here.
| | 05:24 | In the rare case that you go to your
site and you do a search and you look up in
| | 05:27 | the URL and find that your search term
is not there, this means that your search
| | 05:31 | engine doesn't reflect
that term back into the URL.
| | 05:34 | To fix this, first talk with your
system admin and see if it's possible to
| | 05:37 | change that so that it does.
| | 05:39 | If it's not possible, as an
alternative, you can fire a virtual page view
| | 05:42 | that contains the term.
| | 05:44 | Now that virtual-page-view-based trick
is an advanced JavaScript-based technique
| | 05:47 | that's a bit outside of the scope of
this class, but any of the Google Analytics
| | 05:50 | certified professionals will be able to
help you in a very short amount of time.
| | 05:54 | Configuring site search is a
relatively easy task that opens up a world
| | 05:57 | of reports, and there are some of the best
insights we haven't actual customer intent.
| | 06:01 | I highly recommend you take the
time to do this for your profiles.
| | Collapse this transcript |
|
|
12. ConversionsUnderstanding the Goal reports| 00:00 | A food friend of mine, Avinash
Kaushik, has the interesting role of being
| | 00:03 | the Analytics Evangelist for Google,
and he said that not using goals is a
| | 00:08 | crime against humanity.
| | 00:10 | Now it's possible he's overstating the case
just a bit, but I think his point is fairly clear.
| | 00:15 | If you don't have any goals, then
what you have is a really, really fancy
| | 00:19 | hit counter, and that's kind of like
driving a Ferrari from your driveway to
| | 00:23 | the driveway next door;
| | 00:25 | it does work, but it misses the point entirely.
| | 00:28 | Now, when it comes to defining goals, only
you know what the goals for your site are.
| | 00:32 | We talked in the introduction about
choosing goals is an answer to the question of
| | 00:35 | why do you have a web site.
| | 00:37 | And we even gave several
suggestions and examples for food for thought.
| | 00:41 | We can find the dedicated goal reports
here in their own section there in the
| | 00:44 | Conversions > Goals > Overview.
| | 00:47 | We can configure up to 20 goals per
profile, and if you still need more, you
| | 00:50 | just use more profiles.
| | 00:52 | And what you see here in the rest of
the reports will depend on your site and
| | 00:55 | how you configure your goals.
| | 00:56 | Now remember, Google Analytics is just a tool.
| | 00:59 | Like any other tool, it has to be
configured to get the most out of it. Just
| | 01:02 | like we can figure a drill with a proper
sized bit to tell what size of a hole we need,
| | 01:06 | we need to configure goals in Analytics
to tell what we need measured, and what
| | 01:11 | we consider to be a success.
| | 01:12 | One important configuration is that we can
actually assign a monetary value to a goal.
| | 01:17 | This allows us to get revenue without
having a full-blown e-commerce shopping-
| | 01:20 | card type solution on our site.
| | 01:22 | And the idea here is that if you know
a particular goal is estimated to be
| | 01:25 | worth a certain amount to you, and each
time a visitor reaches that goal,
| | 01:29 | it will attribute that revenue to the
visitor and all the segments associated with it.
| | 01:33 | So perhaps you know that based on
historical sales data that your sales team
| | 01:37 | closes out one out of every ten leads
they get for 10% close rate, and the
| | 01:41 | average sale is $1000.
| | 01:43 | So divide a $1000 by 10, and we get
an average lead or goal value of $100.
| | 01:47 | So we can go in and tell
Google Analytics just that.
| | 01:51 | Once we do, Google will have actual
numbers with which to evaluate our
| | 01:54 | keywords, our landing pages, our sources,
mediums, and everything else that we want to analyze.
| | 01:59 | Now remember, the bottom line is we're
just trying to understand which segments
| | 02:02 | are outperforming or underperforming,
| | 02:04 | so don't stress about having a goal
value extremely precise down to the penny.
| | 02:09 | Even if you don't get an extremely
anchored value, it's much better to have
| | 02:12 | it than not, even if it's not as
precise as perhaps an e-commerce cards
| | 02:16 | reporting would be.
| | 02:18 | It's way better than nothing and
provide a basis on which to compare one
| | 02:21 | visitor versus the others.
| | 02:23 | We also have a few more reports to help
us understand conversions on our site.
| | 02:27 | First up here, go back to Standard
Reporting, come down here, is the Goal URLs
| | 02:32 | report, which gives us information
about the number of goal completions and the
| | 02:36 | total value of those completions broken
down by the page our visitor is on when
| | 02:40 | they completed the goal.
| | 02:42 | For this report, we can come up here to
the top in the drop down to select which
| | 02:45 | goal we'd like to see.
| | 02:47 | We can see all goals, or we can
inspect each goal individually.
| | 02:50 | This feature to select a goal or
view data for all the goals is found
| | 02:53 | throughout the Goal reports.
| | 02:54 | Now the Reverse Goal Path report shows
us a unique navigation path that was used
| | 02:59 | to complete the goal.
| | 03:00 | This is an interesting report because
it compares our predicted path that we
| | 03:04 | might set up in, say, a funnel
with what the user actually did.
| | 03:08 | It starts with the goal and works
backwards to see which were the most common paths.
| | 03:11 | But to be honest, I almost never look
at the majority of these reports here
| | 03:15 | in the Goal section during my daily
analysis, because if I'm looking at goals
| | 03:19 | and conversion rates, it's usually
to evaluate the performance of other
| | 03:22 | metrics in other reports.
| | 03:23 | I want to find those pockets of profitability.
| | 03:26 | I want to find that one keyword that just
kills it and converts customers like crazy.
| | 03:30 | I want to find that one landing page that
gets tons of traffic but has never converted a soul.
| | 03:35 | These reports here don't give
me quite as much actionable info,
| | 03:38 | so let's look up at some reports
so we can actually get that info.
| | 03:40 | We're going to go up to the Traffic
Sources report, and if you select one of
| | 03:44 | your Goal tabs up here, what you're going
to see are metrics based upon those goals.
| | 03:48 | In our case, I've defined four goals,
Completing Order, View Software Downloads,
| | 03:53 | Contact Us, and one just for testing.
| | 03:55 | I can see how the different sources
of traffic are performing, not just in
| | 03:59 | terms of visits, but I can evaluate
the quality of that traffic based on my
| | 04:04 | individual goals and if I've assigned a
goal value, I can even get things like
| | 04:08 | per-visit goal value.
| | 04:09 | You see the same feature in nearly
every report in Google Analytics.
| | 04:12 | Defining what these goals are for
your business is the first step in moving
| | 04:16 | beyond counting hits and into
customizing Google Analytics to tell us what we
| | 04:20 | need to know about the
quality of visits on our site.
| | Collapse this transcript |
| Configuring goals| 00:00 | It's an unfortunate reality that the
majority of accounts don't have the most
| | 00:03 | important thing configured, goals.
| | 00:06 | It's tragic really, because it's so easy
to do and statistically speaking, most
| | 00:10 | of you probably don't have
your goals configured either.
| | 00:13 | But hopefully now you are convinced to
stop hit-counting and start evaluating,
| | 00:17 | and this chapter is going
to help you do just that.
| | 00:18 | Now at a couple of points in this course
we have asked you to start brainstorming
| | 00:22 | why you have a site and what
goals are you trying to configure?
| | 00:25 | Well, now it's time for the rubber to
meet the road and actually put some down
| | 00:28 | and configure these goals.
| | 00:30 | Some sites have extremely complex
analysis needs and goal configurations to match.
| | 00:35 | Our goal here in the Essentials course
isn't to overwhelm you with every single
| | 00:38 | possibility and corner case that could
possibly arise, but rather to give you
| | 00:42 | the essentials you need to get
some basic goals up and running.
| | 00:45 | Let's jump to an example.
| | 00:46 | The process we are going to go
through here is really quite simple.
| | 00:49 | We are actually going to go to the site,
and the first thing we are going to do
| | 00:51 | is just go through that process as if
we were the visitor completing the goal.
| | 00:55 | And what we are going to do is we are going
to copy down every single URL that we hit.
| | 00:59 | So the first thing I am going to do
is just copy the URL into a plain text
| | 01:03 | editor and go through the process step by
step like anyone else who is visiting your site.
| | 01:08 | Come to the site. In this example, we are
going to submit a contact form as our goal.
| | 01:11 | So we come here, and the first thing I
am going to do is copy down that URL.
| | 01:15 | Open simple text editor and paste it in.
| | 01:20 | Go back to my site, fill out a test
form here, make it clear to anyone who receives
| | 01:25 | it that this is a test, and Submit.
| | 01:35 | Now this is my final Thank You page.
| | 01:38 | This is the goal that I wanted people to
reach, so I go ahead and copy that down
| | 01:42 | and paste it here as my
submission page complete.
| | 01:46 | Now one of the main reasons that I do
this is I need to make sure that every
| | 01:49 | single step here is unique, especially
that this goal page is unique from the
| | 01:54 | previous page to it.
| | 01:55 | Sometimes we see forms where
there is no final thank you page.
| | 01:58 | You fill out the values in the form,
you click Submit, but the URL hasn't
| | 02:00 | actually changed at all.
| | 02:01 | Let's a take a look at an example like that.
| | 02:03 | If I came here and I submitted this
Contact page here, so just like we did
| | 02:08 | before, scroll down and
submit this as if we were a user,
| | 02:11 | now if you watch the URL up here,
notice that when I submit this, even though I
| | 02:18 | get a successful
completion here--it says, Thanks!
| | 02:20 | it's been submitted--
| | 02:21 | my URL up here hasn't changed at all.
| | 02:23 | So if I were to copy this down and
submit this as my goal, then I wouldn't just
| | 02:28 | get the people who actually submitted the form;
| | 02:30 | I would also get all the people who
just view the form as a blank or really all
| | 02:34 | the people who came to this page at all.
| | 02:36 | So this is why it's really important go
through this process and make sure the
| | 02:40 | Thank You page that we get is actually
unique from the others, so that you can
| | 02:43 | track the goals that you mean to track.
| | 02:45 | If you don't have you any thank you
confirmation page like we had here, it
| | 02:49 | doesn't mean you can track goals;
| | 02:50 | it just means you have got a bit
more of advanced configuration.
| | 02:53 | You are going to have to work with
your administrator to create either a
| | 02:55 | unique page, or you can manually a
fire virtual page view, which your
| | 02:59 | programming team can help with.
| | 03:00 | For now, we are going to take our
example that did have the unique Thank you
| | 03:03 | page and continue on with goal setup.
| | 03:04 | Let me back to our text editor.
| | 03:07 | One of the things that's important about
this is we don't care it all about this
| | 03:10 | part of the URL that has the domain
and the protocol; all I care about is
| | 03:14 | everything from the slash and after.
| | 03:16 | In this part we are calling the request
URI, is what Google Analytics is going to
| | 03:20 | look for to match the goal.
| | 03:22 | So I go ahead and copy this in.
Come back here to Google Analytics.
| | 03:26 | I am going to go under the profile
where we are going to set up the goal,
| | 03:29 | click on our Settings, and
come down here to the Goals link.
| | 03:32 | We have got several Goal Sets
here in which you can create goals.
| | 03:36 | I suggest you give some
thought to this organization.
| | 03:39 | One way might be to have your primary,
most important goals here in Goals (Set 1).
| | 03:43 | You could put some secondary
goals over here in tab two, maybe some
| | 03:46 | engagement goals like time on-site or
number of page view is Goals (Set 3),
| | 03:50 | and even some negative goals like
newsletter unsubscribes or funnel errors
| | 03:54 | down here in tab four.
| | 03:55 | But it's completely up to you.
| | 03:56 | For now I am going to add another goal
here to Goals (Set 1) and I am going to
| | 03:59 | give it a proper name.
| | 04:00 | You want to make these names
relatively short since they are going to be the
| | 04:04 | column titles in all the reports.
| | 04:05 | We are going to mark the goal as Active.
| | 04:09 | In this case, we are going to use the
traditional URL Destination type goal.
| | 04:14 | Here in the Goal URL I am going to go ahead
and paste in that requested URI I have got.
| | 04:17 | In case you need a reminder down here,
it's going to tell you that you don't want
| | 04:20 | any of the domain information--just
everything from the slash and on.
| | 04:24 | Our next option here is the Match Type.
| | 04:26 | We could do Exact Match, Head
Match, or Regular Expression Match.
| | 04:28 | We will go into these a bit more later,
but for now we can just keep it on the
| | 04:31 | default Exact Match.
| | 04:32 | We also have the option of case sensitivity.
| | 04:35 | Now I've never seen a case where a goal
is actually case sensitive, but I guess
| | 04:38 | it's possible you may have one.
| | 04:40 | And here is where we can also
set the optional Goal Value.
| | 04:43 | So if I know that every time I get a
Contact Us message--for argument's sake,
| | 04:46 | let's say it's worth $25--then I can go
ahead and put 25 in there, and Google
| | 04:50 | Analytics will record $25 every time
that we get a successful goal completion.
| | 04:55 | The next step down here is you
optionally have the ability to create a funnel.
| | 04:58 | Now not every goal needs a funnel, but
in the cases like a shopping cart or a
| | 05:02 | Contact Us form where there is a linear
progression from one step to the next,
| | 05:05 | it can be helpful to have a funnel.
| | 05:07 | We will go ahead and put that in.
| | 05:10 | For Goal Step 1, I actually want to go
back to my text editor here and I want to
| | 05:14 | grab the request URI, that first step.
| | 05:16 | I am going to copy that in and just
paste it here into Step 1. Give it a name.
| | 05:24 | Now one thing that's important is I don't
want to put the Goal URL as a funnel step.
| | 05:27 | That's already taken care
of up here in the Goal URL.
| | 05:30 | The funnel steps down here is
just the steps that lead up to that.
| | 05:34 | Go ahead and click Save and you
can see where your new goal has been
| | 05:37 | created right here. And that's it.
Hopefully you will agree
| | 05:40 | it wasn't too tough, and it's a shame that
more folks don't take the time to set it up.
| | 05:44 | If I go into my Reports--let's say
the All Traffic sources report here--
| | 05:48 | I am going to see that I have got my
Goal Set 1, and the goal that I have set up
| | 05:51 | over here, the Contact
Submit, is going to be our Goal2.
| | 05:55 | We just set up this profile and this goal,
so we don't have any data yet, but we
| | 05:58 | would see it here as visitors
start to come and use our site.
| | 06:01 | Now let's go back for one second to
talk about the different types of matches.
| | 06:04 | I am going to click back here into my
Goal Settings, click on Goals, and I am
| | 06:08 | going to click on the one we just created.
| | 06:10 | In our case, we knew exactly the URL
the thank you page was, and we didn't
| | 06:14 | have anything more, anything less, when
we were trying to match multiple goals.
| | 06:18 | So we could set this as an Exact Match.
| | 06:20 | But let's go back to our
text editor for a second.
| | 06:22 | Let's say that instead of just the
thank-you/contact, we actually had a
| | 06:26 | message id on the end here.
| | 06:27 | So let's say we had
something like this, messageid=.
| | 06:32 | Now in this case, we wouldn't want to
just set this entire request URI here to
| | 06:35 | be the goal, because each time someone
submits the form, they are going to get a
| | 06:39 | unique message ID, and therefore you
would only ever match at most one goal.
| | 06:42 | What we want to do is tell Google
Analytics to match this first part here but
| | 06:47 | ignore the second part.
| | 06:48 | So what that is, it's called a head match.
| | 06:50 | If I copy that in there, I select
head match and what I'm saying to Google
| | 06:54 | Analytics, when you see this first part
consider that a goal and ignore anything
| | 06:58 | that comes after it.
| | 06:59 | This is most useful when you have those
query string parameters on then end that are
| | 07:02 | going to interfere with your
ability to create that exact match.
| | 07:05 | The last option we have down
here is a regular expression match.
| | 07:07 | Regular expressions are type of
programming language used to specify patterns.
| | 07:11 | There are lots and lots of advanced
configuration reasons why you might want to
| | 07:15 | use those, and we are not
going to get into all of it here.
| | 07:17 | But one common one we will take a look
at is if I want to match multiple goals.
| | 07:21 | So for this example, I have another
Contact form on our site that you get to
| | 07:25 | from a different place, and I want to
match both of those with this goal.
| | 07:28 | I don't care how you have submitted
the Contact form or where you were;
| | 07:30 | I just want to know that a
Contact form is submitted.
| | 07:32 | Let's go back to our text editor.
| | 07:35 | In this case, I have got my request
URI that we found here before, and that's
| | 07:40 | going to be our primary.
| | 07:42 | But let's say there is another URL
where the request URI was something like
| | 07:46 | /feedback-submitted.
| | 07:49 | Now in this case I want to
match either one of these,
| | 07:51 | so what I am going to do is I am going to
use this vertical pipe character here.
| | 07:55 | What that's going to do is say you can
either match this or that, and I could
| | 07:59 | put some parentheses in
here to make it more clear.
| | 08:03 | This Pipe means "or," so if you match
this here or the second one here, then we
| | 08:09 | are going to submit the goal. So we take this
entire thing, copy that in as our Goal URL and select
| | 08:17 | Regular Expression Match.
| | 08:18 | Now what Google Analytics is going to
evaluate this Regular Expression Match
| | 08:21 | if we match either this or that it's
going to submit the goal just like before.
| | 08:25 | Everything else stays mostly the
same except you may have to look at your
| | 08:28 | funnel steps as well.
| | 08:29 | In this case, I am just going
to remove my funnel entirely.
| | 08:32 | So this is how we create a
goal based on a particular URL.
| | 08:35 | But we also have two other types of goals
known as threshold goals or engagement goals.
| | 08:40 | Let's say, for example, my goal as a
publisher was to get people stay on the
| | 08:43 | site for a long time, read my
content, view my videos, et cetera.
| | 08:47 | I could come over here and select Time
On Site and I could say, well, I want it
| | 08:51 | to be greater than let's say 5 minutes.
| | 08:54 | In this case, anyone here who is on
the site for more than five minutes,
| | 08:58 | regardless of if they did anything like
submitting forms or checking out, would
| | 09:03 | be considered a goal.
| | 09:04 | Similarly, I can also go up
here and select Pages/Visit.
| | 09:07 | I could say that anytime a person has
visited more than let's say five pages,
| | 09:11 | that person would also be
considered to have completed my goal.
| | 09:14 | The last option down here are event-based goals.
| | 09:17 | Events are an advanced
configuration features that allow you to track
| | 09:20 | interactions with your site that don't
necessarily load a new page view, like
| | 09:23 | playing a video or downloading a file.
| | 09:25 | If you have events set up to track
these things, you can also set goals based
| | 09:29 | on these interactions.
| | 09:30 | We won't go into too much detail on
how to create the code for events here,
| | 09:33 | since the programming required is a
bit outside the scope of this course.
| | 09:36 | But similar to the other goals, we
define the goals here using these fields, we
| | 09:40 | assign a value, and click Save.
| | 09:42 | That's basically all there is to goal
setup and the value of goals makes them
| | 09:46 | far and away worth it's
relatively small effort to configure them.
| | 09:49 | So please, stop committing those
crimes against humanity and get yourself
| | 09:53 | the gift of some goals.
| | Collapse this transcript |
| Understanding funnel visualization| 00:00 | Funnels are an optional part of
configuring goals that can help us identify
| | 00:03 | bottlenecks and multi-step processes
and thus provide insight to where we are
| | 00:07 | losing our customers in that process.
| | 00:09 | Let's go ahead and walk through an
example of what a Funnel report might look like.
| | 00:12 | If you click down here to our Goals tab
and click on Funnel Visualization,
| | 00:15 | we are going to see something like this.
| | 00:17 | Okay, in this case I have set up a
funnel and it's going to walk us through a
| | 00:20 | typical shopping cart example.
| | 00:21 | In this case, we view one of
the Product Category pages.
| | 00:24 | I then want people to go on and
actually view the individual product page, put
| | 00:28 | that product in a shopping
cart, and complete the order.
| | 00:30 | Now in the left-hand side here we have
the entrances into each part of this step.
| | 00:34 | On the right side we
have the abandonment points.
| | 00:37 | So what we are looking at there
is there are 6800 people who view
| | 00:40 | this particular page.
| | 00:42 | Those 6800 people entered from these places.
| | 00:44 | 4500 of them came from the homepage. 1700
of them here came from entrance.
| | 00:49 | That means that they
entered the site via this page.
| | 00:51 | 223 came from the privacy policy, et cetera.
| | 00:55 | Now where do they go? Well, 69% of them
went down to the next up, and the others did not.
| | 01:00 | The 2100, where do they go?
| | 01:02 | Well, 1300 of them exited, meaning they left
the site completely after seeing this page.
| | 01:07 | 524 went out of the homepage, some
went to the software page, et cetera.
| | 01:12 | So the 6800, 69% of those
went down to the next step.
| | 01:15 | Not everybody follows the steps that I laid out.
| | 01:18 | In fact, 3300 people came directly
into the product page without going to the
| | 01:23 | category page first.
| | 01:25 | Another 612 came from the homepage, et cetera.
| | 01:29 | Okay, of these 8600 people only 8% went
on down into the shopping cart. Only 8%
| | 01:34 | added that item to it.
| | 01:36 | The other 8000 people went somewhere else.
| | 01:38 | 5000 of them here left the site
completely. They saw my page.
| | 01:42 | They weren't interested.
They went somewhere else.
| | 01:44 | 1100 of them saw something called pop-ups view.
| | 01:47 | Now what could this be?
| | 01:49 | What this actually is is a pop-up of
an enlarged view of the product, which is
| | 01:53 | not necessarily a bad thing, right.
| | 01:55 | I don't want you to think of
abandonment points. Abandonment sounds so bad,
| | 01:59 | but not necessarily.
| | 02:00 | We need to think about this in context
of what people are actually doing and
| | 02:03 | figure if it's really as
bad of a thing as we think.
| | 02:05 | In this case viewing an enlarged version
of the product is not necessarily a bad
| | 02:09 | thing at all, and in fact, that's why
we see over here that the popups/view was
| | 02:13 | one of the entrances into the page.
| | 02:15 | People view the page and then they
head back into the product page itself.
| | 02:19 | The 8% that went down into the
shopping cart, 661 folks. Now there were 711
| | 02:24 | here because another 50
came in from somewhere else.
| | 02:26 | Where do they come?
| | 02:27 | Well, some came from the homepage.
Maybe there's shopping cart link on the top
| | 02:30 | of the homepage.
Some came from the privacy policy.
| | 02:33 | They needed to check out our privacy
policy before they're willing to go
| | 02:36 | through with the cart.
| | 02:37 | On the Exit side here we see that
263 people exited the cart entirely.
| | 02:41 | Some went back to the homepage. What about
these folks, the signin.asp, is that a bad thing?
| | 02:46 | No, not at all, right.
| | 02:47 | These are people who
already have an account with us.
| | 02:49 | These are returning
customers, our bread and butter.
| | 02:51 | That's completely fine.
| | 02:52 | We have some folks who
want to view another pop-up.
| | 02:54 | Maybe they needed to make sure this
was the product they thought it was
| | 02:57 | when it's in the cart.
| | 02:58 | We also have some over here on the entrance
side that enter this site via the shopping cart.
| | 03:03 | And how could that happen?
| | 03:03 | Well, one of the most common ways is because
what we are really looking at here are visits.
| | 03:08 | Now a visit expires 30
minutes after your last click.
| | 03:12 | So if I were looking at a particular
product pondering it over, maybe I decided
| | 03:16 | to go to lunch, maybe I needed
to think about it, sleep on it,
| | 03:19 | if I came back to that page and clicked
Add to Cart, then the first page of my
| | 03:24 | new visit would be the shopping cart,
and so that would be an entrance to the
| | 03:28 | site via the shopping cart.
| | 03:30 | So if your exits on this side were
matched by an equal number of entrances on
| | 03:34 | this side, you would be fine, and
unfortunately, that's not the case here.
| | 03:38 | And the last one we see here is the
completed order. 103 people actually made it
| | 03:42 | all the way through the funnel, 1.19%.
| | 03:44 | Of course, we don't see entrances to
the Thank You page because you cannot get
| | 03:48 | to the Thank You page without
first going through the shopping cart.
| | 03:51 | Okay, we have seen what a funnel
visualization looks like, but let's go ahead
| | 03:54 | and look at how we would actually set this up.
| | 03:56 | Okay, remember we said that a funnel
was an optional step of a goal, so we go
| | 04:00 | ahead and add a goal.
| | 04:01 | In this case, the goal was
to get to the thank you page.
| | 04:04 | Funnels appear on URL Destination goals.
| | 04:08 | So we select that radio button and
then we get all these options down here.
| | 04:11 | Now remember the first step in filling
out any type of goals, whether you are
| | 04:14 | using a funnel or not, is to go
all the way through the process.
| | 04:17 | So we have done that here.
| | 04:19 | We went through each step and we copied
down the URL of the page that we saw there.
| | 04:23 | Now remember, we don't
need domains in this case.
| | 04:24 | We just need the Request URI.
| | 04:27 | So the first step was to view a category page.
| | 04:30 | We will go ahead and copy that over here.
| | 04:33 | In order to create the goal funnel, we
need to click on this down here to create
| | 04:36 | the goal funnel, and it's going to give
us some places to put the optional steps.
| | 04:39 | So the URL for the first up is this.
| | 04:41 | Now remember, we said this
was going to be a head match.
| | 04:44 | We are only going to match category page.
| | 04:46 | We don't care about the
particular category here,
| | 04:47 | so we are going to drop that off, and
all we want to do is make sure that the
| | 04:51 | page starts with category.asp.
And this first one was View Category Page.
| | 04:59 | We go back to our list.
| | 05:00 | We grab the second step, View Product Page.
| | 05:03 | I am going to copy that over and again,
| | 05:05 | let's just go ahead and
only that Head Match part.
| | 05:07 | I don't care which particular product
it is, so I am not going to copy the ID.
| | 05:10 | I just want to make sure it starts with
product.asp. And click to Add a Goal Funnel.
| | 05:15 | Paste that in. Third step.
| | 05:28 | Now the last step is
actually not a step in the funnel.
| | 05:30 | It's the goal itself.
| | 05:31 | So when we copy this one over, we are
going to paste it up here as our Goal URL.
| | 05:35 | It's not going to be step four, but
it's actually going to be the goal step.
| | 05:39 | Enter any Goal Value we
may like, click Save Goal,
| | 05:43 | and the goal has been saved.
| | 05:44 | Now if we go here to the View
Reports, down to the Goals menu, click
| | 05:49 | Funnel Visualization,
| | 05:52 | select the goal, we just created the
Thank You Page, and we will see that that
| | 05:56 | funnel is prepared and ready for
once some visitors come through.
| | 06:00 | Now remember, this is only going to
complete and populate from this point on.
| | 06:03 | It's not going to look back
and look into your historical data.
| | 06:06 | It's only from the moment
you create the goal forward.
| | 06:08 | In this shopping cart example we
have been looking at, we saw a huge drop
| | 06:12 | between steps two and three.
| | 06:13 | But we can use these reports to find
these types of bottlenecks in more than
| | 06:17 | just shopping carts.
| | 06:18 | For many, many sites forms are one of
the most critical parts of a page, because
| | 06:22 | they're that last final frontier
between our visitors taking that next step.
| | 06:26 | Someone filling out a form is just a
button click away from being on the
| | 06:29 | customer path, and we are going to do
everything we can not to derail them.
| | 06:32 | One important thing to determine is
are there areas of our form that are
| | 06:35 | causing them to abandon.
| | 06:37 | Here we see a big dropoff,
but it's not entirely clear why.
| | 06:41 | Something in this form is causing a problem.
| | 06:43 | So one option is a multi-step form to
identify these problematic areas. We take
| | 06:47 | the same form and break it
up into a few different steps.
| | 06:50 | What we see here is that there is a
particular part of the form here that is
| | 06:55 | causing a massive bottleneck.
| | 06:56 | Now at this point as site owners we
have an important decision to make. How
| | 06:59 | critical is this particular data?
| | 07:02 | Is it just nice to have
or is it really necessary?
| | 07:04 | As marketers, we all want as much data
as we can get, and some people are not
| | 07:07 | willing to give up that part of the form.
| | 07:09 | Now my job as a consultant here is not
to tell you how to run your business and
| | 07:13 | say keep it or don't. My job is to show
you the data and show you exactly what
| | 07:17 | the consequences will be of your decision.
| | 07:19 | In this case, if you continue to ask
that question, you can expect to lose 90% of
| | 07:23 | the people who have already begun
the process of filling out the form,
| | 07:26 | so give some thought to asking for
information which is not absolutely required.
| | 07:30 | Generally speaking, the more questions
we ask to people, the fewer of them that
| | 07:34 | are actually going to give it to us.
| | 07:35 | We also need to understand
a key feature about funnels.
| | 07:39 | The options to denote the first
step of your funnel as required.
| | 07:42 | As you are setting up the funnel, you
can select whether you want that first up
| | 07:46 | as required in order for that visit
to be tracked as part of this funnel.
| | 07:49 | Now this is useful if you are
interested in analyzing only, say, the checkouts
| | 07:53 | from a particular page selling mittens.
| | 07:55 | If you don't make the first step
required, people can enter your funnel from step
| | 07:59 | 2, 3 and so on, as seen here.
| | 08:02 | However, if you do make that first
step required, we could see how our
| | 08:05 | numbers will change.
| | 08:06 | Now the funnel is only tracking people
who started with my required first step.
| | 08:10 | Google Analytics is just a tool, and we
can use the tool in some creative ways
| | 08:13 | to find buried insights.
| | 08:14 | For example, one thing that can stop
people from successfully completing a form
| | 08:18 | is when they get an error, especially
if that error isn't too user-friendly.
| | 08:21 | So one question we often have is do
people just quit there, do they keep trying,
| | 08:25 | how many of those actually make? It
turns out this is really easy to answer.
| | 08:28 | Now rather than making a funnel for the
whole form, we are just going to make a
| | 08:32 | special one with two steps.
| | 08:35 | So first we set up a goal here
to complete the Contact page.
| | 08:38 | In our case here, the
goal is FormComplete.html.
| | 08:41 | That part is exactly the same. Nothing new here.
| | 08:44 | However, we are going to create
required first step of hitting that
| | 08:47 | particular error page.
| | 08:49 | In other words, I'm only interested in
analyzing the people who hit the error page.
| | 08:52 | The goal remains exactly the same, but
the steps in the form are going to be
| | 08:56 | different, in this case
just that one single step.
| | 08:58 | Now what this funnel is going to do
is show us exactly how many people who
| | 09:02 | experience this error page were
able to go on and submit the goal.
| | 09:06 | And the form is going to
look something like this.
| | 09:08 | These are all the people who hit the
form error page and these are all the
| | 09:11 | people who actually made
it down to the submission.
| | 09:13 | In our case, just 10% of the people.
| | 09:15 | Now these are people who are trying to
be our customer, but our user-unfriendly
| | 09:19 | form just won't let them.
| | 09:21 | Hopefully this report will convince your
developer to give that form some much-needed love.
| | 09:25 | Now generally speaking, defined funnels
work very well when you actually have a
| | 09:29 | defined funnel, such as a shopping
cart, online application form, et cetera.
| | 09:33 | If you are going to trying to
define a funnel for a process that is
| | 09:35 | ill-defined, such as just reaching our
privacy policy page, there are so many
| | 09:39 | different ways to do that, so many entrances and
exits, that a funnel will be a jumbled-up mess.
| | 09:43 | Stick to well-defined paths and
you will find they reveal much more
| | 09:46 | usable insights.
| | Collapse this transcript |
| Identifying value through E-commerce reports| 00:00 | If you do conduct transactions on the
web, one of the most important goals you
| | 00:04 | will want to measure is the goal of
collecting money, and there is a whole set
| | 00:07 | of reports dedicated to just that topic.
| | 00:10 | To get the info from these reports,
you do need to integrate Google
| | 00:13 | Analytics with your shopping cart so
that your shopping cart or checkout
| | 00:16 | software will send all the data with
the results of those transactions back
| | 00:20 | to Google's servers.
| | 00:21 | While I will briefly touch on that
topic, the programming skills required to
| | 00:25 | integrate with your site's shopping
cart, along with the fact that everyone's
| | 00:28 | cart is different-- it needs to be
customized to each individual site--means
| | 00:32 | it's way outside the scope
of this essentials course.
| | 00:35 | You'll need to consult with your
programmer on how to best integrate the
| | 00:39 | necessary Google Analytics code with your cart.
| | 00:42 | Along those lines, full analysis of a
retail-type E-commerce site can quickly
| | 00:46 | get complex and very specific to that site.
| | 00:49 | So for the purposes of this course we
will try to stick to the essentials and
| | 00:53 | provide an overview.
| | 00:55 | As you become more comfortable with
this type of analysis, I encourage you to
| | 00:59 | continue your pursuit of knowledge in
this fascinating area of web analytics.
| | 01:03 | With that in mind, let's take a look.
| | 01:04 | We can get to the Ecommerce reports
down here in CONVERSIONS, and there is
| | 01:09 | entire Ecommerce section.
| | 01:11 | And just like we saw in Goals,
despite the fact that we do have this entire
| | 01:14 | section here dedicated to these reports,
believe it or not, that's not where you
| | 01:17 | will spend most of your time
doing E-commerce-related analysis.
| | 01:20 | As we've seen many times in this course,
E-commerce metrics can be viewed in
| | 01:24 | many other reports, such as this
one we are going to find here in the
| | 01:26 | TRAFFIC SOURCES section.
| | 01:28 | Here we have the All Traffic report.
| | 01:29 | We can see that since we've enabled E-
commerce for this site, we get this group
| | 01:33 | of E-commerce metrics up here.
| | 01:34 | And most of the time we aren't
interested in looking at E-commerce in isolation,
| | 01:38 | for the same reason we want to avoid
looking at any metric in isolation.
| | 01:41 | What we are really interested in is
evaluating the different segments of users,
| | 01:45 | keywords, landing pages, or in this case
sources and mediums, using the Revenue,
| | 01:49 | Per Visit Value, and other E-commerce
metrics as criteria in order to evaluate
| | 01:54 | these different sources.
| | 01:55 | But that's not to say that there aren't
some uses for these specific Ecommerce reports.
| | 01:59 | Let's take a look at what's available.
| | 02:00 | We will come down here
in the E-commerce section.
| | 02:02 | We will click on Overview to see the
Overview report, and this is much like any
| | 02:05 | of the overview reports we've seen.
| | 02:07 | It's got a data over time graph up here.
| | 02:09 | It's also got some E-commerce-related
key performance indicators, brief review
| | 02:13 | of the quantities sold online.
| | 02:15 | Here it has got some of the products
broken down in percentages and quantities.
| | 02:19 | We can also move this down here to
Product SKU, Product Category, and sources
| | 02:23 | and mediums as well.
| | 02:24 | But the next thing we want to take a
look at, probably more interesting, over
| | 02:27 | here is the Product Performance report.
| | 02:29 | Here we can see all the different
products that are sold, the total quantities
| | 02:32 | sold, the number of purchases that
included one or more of that product, the
| | 02:36 | revenue generated, the average
purchase price, the average quantity per
| | 02:39 | purchase, et cetera.
| | 02:41 | We can also change the dimension
here to show Product SKU rather than by
| | 02:44 | product name, or if we set up categories, we
can evaluate the categories of those products.
| | 02:50 | Next up is the Sales Performance report.
| | 02:52 | Here we can do revenue generated by date,
sorted from the most profitable date
| | 02:56 | to the least profitable date.
| | 02:58 | Down here in the Transactions report we
are able to look into actual individual
| | 03:01 | transactions based on these transaction
number that our shopping cart has been
| | 03:04 | configured to send to Google.
| | 03:06 | This report can highlight the individual
transactions on a particular day and if
| | 03:10 | we want to drill down into any
particular transaction number, we can see which
| | 03:13 | items were purchased, how many, at
what cost--all the information about that
| | 03:18 | particular transaction.
| | 03:19 | And the last of these E-commerce reports,
all the way down here, is the Time to Purchase.
| | 03:23 | One of the requests we get very often
is to help people understand their sales
| | 03:27 | cycle and the selection process that a
customer goes through on their web site.
| | 03:30 | We have two views here.
| | 03:32 | On the top we can see Days to
Transaction, which is going to track how many
| | 03:37 | days it took when you first visit to my site
to the time when you actually pull the trigger.
| | 03:41 | And the second report here.
| | 03:43 | Visits to Transactions, is similar,
but instead of days it's visits.
| | 03:47 | In other words, how many
touches did it take to convince you?
| | 03:50 | Did you need to noodle over it and do
some research or did you just pull the
| | 03:53 | trigger on impulse that first time you visited?
| | 03:55 | Looking at our site here, it looks like
that Android T-shirt purchase might have
| | 03:58 | been an impulse buy.
| | 04:00 | The beauty of online marketing is
that we can track it with so much more
| | 04:02 | precision and accuracy compared to any
other kind of marketing, and you can't
| | 04:06 | get much more precise than E-commerce reports.
| | 04:08 | So we can tell with a high degree of
accuracy exactly how much that keyword is
| | 04:12 | worth to you, and if you have a site
with an E-commerce aspect of any kind, you
| | 04:16 | owe it to yourself and your
organization undertake the effort to get E-commerce
| | 04:20 | reporting correctly integrated into your site.
| | Collapse this transcript |
| Using goal flow to find detailed insights| 00:01 | We talk a lot about funnel analysis
in Analytics and how critical it is.
| | 00:04 | Whether it's a lead-gen form, account
registration, or even a shopping cart, it's
| | 00:08 | one of the most important
things that we can analyze.
| | 00:10 | Funnel analysis inherently lends
itself to visual analysis and the new flow
| | 00:14 | visualization reports do a great job of this.
| | 00:16 | They are tremendous improvements, and
they enable a much greater understanding
| | 00:20 | of our site's performance.
| | 00:21 | We find the reports here in the
CONVERSIONS section. under Goals and Goal Flow.
| | 00:25 | Just like in Visitor Flow, we see the
node and the paths between them, except in
| | 00:29 | this case the nodes aren't pages.
| | 00:30 | They are rather steps in our funnel.
| | 00:32 | We can choose the particular funnel
step that were in analyzing up here in the
| | 00:35 | dropdown; in this case we are
looking at the completed orders.
| | 00:38 | We choose the appropriate dimensions
here. By default we see sources and then
| | 00:42 | how those visitors flow through our
funnel, or in the case of the red over here,
| | 00:47 | how they abandon the funnel.
| | 00:48 | Now if we are only interested in a
particular segment, say new visitors, we can
| | 00:52 | choose that via this dropdown.
| | 00:53 | When we click on a particular source in
the flow visualization, we can choose to
| | 00:56 | highlight the traffic through that part
of the funnel, which shows us where users
| | 01:00 | fall out and come back again, or where
they skip steps and go straight ahead.
| | 01:04 | Let's take a look at that.
| | 01:05 | In this case if I click on reddit.com
and highlight traffic through reddit.com, that
| | 01:09 | goes through the funnel, and we can see
here where they enter the funnel steps,
| | 01:12 | how far they go. In this case they
actually circle back after viewing the
| | 01:15 | shopping cart and view the product
category again and then on to the login step,
| | 01:19 | all the way through to placing an order.
| | 01:21 | To make this a little bit easier to
see over here, we can actually expand
| | 01:25 | this down by clicking the plus button, which
allows us to see this a little bit more clearly.
| | 01:29 | Also, we can look down here at the
table below. That will show us the table
| | 01:33 | representation of what we are
seeing in the visualization.
| | 01:35 | In this case we see reddit.com, and
we can see Step 1 here is View Product
| | 01:39 | Categories, Step 2 to the view the
shopping cart, Step 3 to log in. and then
| | 01:43 | finally to place the order.
| | 01:44 | We can also choose not just the
highlight the traffic that flows through here
| | 01:47 | but to isolate it entirely.
| | 01:49 | To do that, we'd click on the
step and View only this segment.
| | 01:52 | At this point the entire analysis that
we see here, the entire visualization is
| | 01:56 | just the traffic coming from reddit.com.
| | 01:58 | To return back to the original, we
simply click on the breadcrumbs up here
| | 02:01 | to Completing Order.
| | 02:03 | As we hover over each step in the funnel,
we get a summary of what happened at
| | 02:06 | this stage of the funnel, and the third
option when we click on any individual
| | 02:10 | source or node is that we
can view the group details.
| | 02:13 | This gives us a pop-up with the
URL match we entered in the goal
| | 02:16 | configuration process up here.
| | 02:18 | We can see in this case it was
Accessories, Fun, Kids, Wearables--all the type
| | 02:22 | of product categories that I've got.
| | 02:24 | We also see things like the number of
page views, the average time on this
| | 02:27 | particular group, the number of funnel exits.
| | 02:29 | We can see the funnel exits broken down here.
| | 02:31 | We can also choose other things like the
top pages that are part of this category.
| | 02:35 | In this case, I can see all the things
that match our goal up here and the actual
| | 02:39 | pages which match those goals, as
well as all the associated metrics.
| | 02:42 | We also can see Incoming traffic,
Outgoing traffic, Funnel Entrance, Exits, all
| | 02:47 | kinds of great
information in this little pop-up.
| | 02:49 | We are turning back to the report, we can
also choose to change this dimension here.
| | 02:52 | All of our visitor, traffic sources,
content, and system ones are available. In
| | 02:57 | this case let's take a look by keyword.
| | 02:59 | I click on keyword and now the
report is going to change to show me the
| | 03:02 | different keywords that brought traffic here.
| | 03:05 | We may also want to evaluate by
medium, such as organic or CPC traffic
| | 03:08 | through the funnel.
| | 03:10 | Lastly, we may want to look at something
like mobile versus non-mobile traffic.
| | 03:16 | Here we see the traffic that was
mobile or not set, not set being the
| | 03:19 | non-mobile traffic.
| | 03:20 | If we highlight the traffic here
through the mobile then we are going to take a
| | 03:23 | look at the first two steps of the
funnel, and what we actually see is that no
| | 03:27 | one completed the funnel.
| | 03:28 | When we look at the second step here,
which is to view the shopping cart, we see
| | 03:32 | that some came back around here to
view the product categories, but no one
| | 03:35 | actually made it past the second step.
| | 03:37 | Interestingly, we also see some mobile
visitors who go directly to the shopping cart.
| | 03:42 | This can happen when a session times
out and the next page that you hit is the
| | 03:45 | shopping cart but since it was a new
session it looks as if you've entered this
| | 03:48 | visit into the shopping cart.
| | 03:50 | This visualization is one of the things
that I really like about this funnel, in
| | 03:54 | that it's intuitively clear.
| | 03:55 | This is not necessarily a linear process.
| | 03:57 | Some folks are already logged in, some
folks aren't, some go back and add more
| | 04:01 | to the shopping cart before checking out,
some continue on to the process, or as
| | 04:04 | we see in this case, no one even
makes it past the second step.
| | 04:08 | If we look down here at the bottom of
the table, the numbers confirm what
| | 04:11 | we're seeing up here. People make it
to the first step, the second step, but
| | 04:14 | nobody actually makes it here
to this third or fourth step
| | 04:17 | that is in the mobile category.
| | 04:18 | So since we notice that no one in this
particular mobile segment made it past
| | 04:22 | the checkout, this might raise the
question: Is this checkout process usable
| | 04:26 | for our mobile users?
| | 04:27 | Let's expand our date range back to
2009 and see if we've ever had a mobile
| | 04:32 | visitor check out in two years.
| | 04:34 | Do that here up in the Date Range
Selector. Simply going to change this to
| | 04:38 | 2009 and click Apply.
| | 04:41 | And our moment of truth. On the mobile
line here, it looks like, no, we've never
| | 04:45 | had a mobile checkout here.
| | 04:46 | So clearly the next step here is to
evaluate our mobile traffic and figure out
| | 04:50 | why it's not working, if we need to
build a more mobile-friendly site, or exactly
| | 04:53 | what's going on with this particular problem.
| | 04:55 | As you can see, this Goal Flow
Visualization report is very powerful,
| | 04:59 | insightful, and actionable.
| | 05:01 | It makes it easy for us to visualize
the paths our visitors take on the way to
| | 05:04 | conversion and spot those trends quickly.
| | Collapse this transcript |
|
|
13. The Home TabReal-time data for time-sensitive analysis| 00:00 | The real-time reports in Google
Analytics are an interesting development and
| | 00:03 | they're fun to look at, even if not
everyone will find them consistently useful.
| | 00:07 | However, there are a few specific use
cases where having access to real-time
| | 00:10 | data could be critical.
| | 00:12 | So like so many reports, they're great
for those times when you need them, even
| | 00:15 | if you don't use them every day.
| | 00:16 | Let's go to the reports first, and then
we'll take a look at a couple use cases.
| | 00:19 | Here we come here to the Home tab,
| | 00:21 | click on Real-Time reports, and
first up is the Overview report.
| | 00:25 | Immediately we notice this big bold
count of active visitors for the site.
| | 00:29 | Below that we see a breakdown of new
versus returning visitors, and to the right
| | 00:32 | we see two column graphs.
| | 00:34 | They allow us to view traffic trends over time,
minute by minute, and even second by second.
| | 00:39 | This is an easy visualization to see
spikes or drops in live traffic, but
| | 00:43 | insights are a little
harder to come by after that.
| | 00:45 | So let's move on to the
meat of this report down below.
| | 00:49 | Here we see the top ten four different dimensions:
| | 00:52 | top ten referral sources, showing us the
top ten web sites referring to our site,
| | 00:56 | the top ten pages our active visitors have
viewed in the last 30 minutes, and the top ten keywords
| | 01:02 | that brought the current visitors to our site.
| | 01:04 | We also see the top ten locations of
our active visitors, and we can drill into
| | 01:08 | the titles of any of these widgets to
see some more details, or we can use a
| | 01:11 | left-hand navigation to
get our reports from there.
| | 01:14 | Let's start here by
looking at the Locations report.
| | 01:17 | The Locations report look similar at
the top, with a count of active visitors,
| | 01:20 | and then it breaks the visitors down
country by country, showing the countries
| | 01:24 | that represent at least 1% of traffic,
and the grouping all the traffic from
| | 01:27 | the rest into Other.
| | 01:29 | We see the same column graphs showing
page views per minute and per second.
| | 01:33 | At the bottom here we see a full list of
countries that our visitors are showing
| | 01:37 | our site on and a map that shows this visually.
| | 01:40 | A fun feature with this map is that
Google has actually integrated Google Earth here.
| | 01:43 | You can see the Map and
the Earth options on top.
| | 01:46 | Google Earth has higher browser requirements,
| | 01:48 | so if doesn't load on your browser,
you can get the same info, but with a
| | 01:51 | slightly less visually immersive
view on the plane map. But don't worry;
| | 01:55 | it's essentially the same information.
| | 01:56 | Google Earth even lets you zoom into
the level where you can see individual
| | 01:59 | buildings in your city, and it can be
helpful for things like identifying if
| | 02:03 | the traffic shown is coming from
your building or perhaps the conference
| | 02:06 | center across town.
| | 02:07 | Just keep in mind these maps may be up
to several years old in some cases and
| | 02:11 | are stretching the bounds of what I
consider to be reliable geolocation
| | 02:14 | granularity and specificity.
| | 02:17 | The Real-Time Traffic Resources has the
same live stats on top, except that the
| | 02:21 | traffic breakdown here is by medium.
| | 02:24 | Other than that, this report is much
like the regular All Traffic Sources report
| | 02:27 | found in standard reporting section,
except that you're limited to the
| | 02:30 | dimensions that are presented.
| | 02:32 | You can still drill down into organic
traffic here and see the sources and the
| | 02:36 | keywords that brought them in,
but that's about as far as it goes.
| | 02:38 | If I click on Organic, we can see that report.
| | 02:41 | Now the last report that we've got up
here is the Content report, and on top it's
| | 02:46 | identical to the Real-Time Traffic
Sources report, but the bottom here
| | 02:49 | represents live data about
the content for active visitors.
| | 02:53 | So all this is really cool to see, but
one downside is you won't see conversions
| | 02:57 | or E-commerce data here.
| | 02:58 | Processing things like that
takes additional processing,
| | 03:01 | so for now we are limited
to visits and pages views.
| | 03:04 | These reports are particularly
valuable to the publishing industry or anyone
| | 03:08 | whose page content changes more
than once per day. Think about this.
| | 03:12 | If your front page is like CNN or The
New York Times and it changes every hour
| | 03:15 | with breaking news, you can't look at a
report that summarizes entire days for a
| | 03:20 | page to draw any conclusions about the
content, because that one page wasn't
| | 03:23 | static for the entire day,
| | 03:25 | so you don't know with the metrics
represented are for the morning version of
| | 03:28 | that or the afternoon or anywhere in between.
| | 03:30 | In web analytics the assumptions is
somewhat that the page is going to be page
| | 03:34 | for a day, and that's not always the
case in situations like these where the
| | 03:38 | homepage has lots of iterations inside
of a single day. Or if you're holding a
| | 03:42 | conference in a particular location,
these could help you understand how people
| | 03:45 | in that location are using your web
site or even see the impact of social media
| | 03:49 | at an events, since social is a
very right-now-oriented channel.
| | 03:53 | A similar use for these reports
would be for popular retailers that offer
| | 03:56 | promotions or events.
| | 03:58 | They could know in real time how
offline campaigns or in-store events are
| | 04:01 | affecting the online performance. Or if a
company is fortunate enough to have a
| | 04:05 | highly anticipated product launch, these
would be great reports to watch in real time.
| | 04:09 | So while these reports won't
necessarily be part of your day-to-day
| | 04:12 | analysis--unless you're a publishing
company, or in charge of certain social
| | 04:16 | media marketing--but they can
provide timely insights on how people are
| | 04:19 | interacting with your site.
| | Collapse this transcript |
| Using intelligence alerts to flag important events| 00:00 | The real point of web analytics is
not just to collect data; it's to get
| | 00:03 | insights. And through a feature
called Intelligence, Google Analytics is
| | 00:07 | going to help make analysis of our
site for easier and help us draw accurate
| | 00:11 | conclusions faster.
| | 00:12 | To get to the intelligence reports, we
navigate by clicking over here on the
| | 00:15 | Home tab, click INTELLIGENCE EVENTS,
and we have the option of the Overview,
| | 00:20 | Daily Events, Weekly Events, or Month Events.
| | 00:22 | For now let's just look into Daily Events.
| | 00:24 | There are two different
ways that I use this tool.
| | 00:27 | The first I'll call forensic and
the second we'll call insights.
| | 00:31 | In forensic mode here what I am really
looking for are things that are jumping
| | 00:35 | out for me but I can't necessarily
explain it. As I look at this graph
| | 00:38 | right here of visits,
certainly this day jumps out.
| | 00:41 | There is a large jump in visits here
and I want to figure out what that is.
| | 00:45 | That's not necessarily a
particularly easy thing to do.
| | 00:48 | Often what we are trying to do here
in analysis is not things that couldn't
| | 00:51 | ever possibly be figured out another
way, but ways that we can do things
| | 00:55 | faster, easier, quicker.
| | 00:57 | We only have a certain amount
of time in our day for analysis;
| | 00:59 | we need to be as efficient as possible,
| | 01:01 | so I need to figure out
quickly what's going on here.
| | 01:03 | As I put my mouse over this date, I
can see the number of visits have gone up, and
| | 01:07 | down below you will see this green bar.
| | 01:09 | This green bar indicates the number
of intelligence alerts that have been
| | 01:12 | detected on that particular day.
| | 01:14 | In our case we have three alerts.
| | 01:16 | If I click on this green bar, the
bottom of the screen is going to update to
| | 01:20 | show what those alerts that
Google Analytics found for us.
| | 01:23 | So there are three things of interest
here. First is that page views have
| | 01:26 | gone up, visits have gone up, and
visits particularly from the source of
| | 01:29 | reddit have gone up.
| | 01:30 | If I look over here I will
see this idea of Importance.
| | 01:34 | Importance is also known as
significance, and the idea here is this is going to
| | 01:38 | tell us how different this
is from what was expected.
| | 01:41 | In our case, there was a large increase
in the number of pages views and visits.
| | 01:46 | Normally we would expect to get
between 0 and 275 visits from Reddit.
| | 01:49 | In this case we had 2500 visits on that day--
| | 01:52 | over 500% increase.
| | 01:54 | If I click on the little icon right
here, I will actually isolate the graph
| | 01:58 | above to show just that.
| | 02:00 | In this case it becomes very
obvious, yes, this is a major event that
| | 02:03 | happened on that date.
| | 02:05 | When we look at just the visits here
from reddit.com, we can see that this was
| | 02:09 | something way out of the ordinary.
| | 02:10 | If I click back to our original screen,
we see that that bump in 2500 visits is a
| | 02:16 | large contributor to our overall visits here.
| | 02:18 | It's a quick way for us to understand
what's happening when we see something
| | 02:21 | out of the ordinary.
| | 02:22 | But I think the real value of this
tool isn't so much in explaining what's
| | 02:26 | already obvious to us, but in uncovering
insights that we may never have seen before.
| | 02:30 | If we are diligent analysts, we might
log in every single day, we might go
| | 02:34 | through our list of hundreds of
different reports, and we might analyze every
| | 02:38 | single type of medium, every single
type of source, every keyword, every
| | 02:42 | campaign that we are running, all the
different traffic from different areas of
| | 02:46 | the world, different cities, different
states, different countries, and we might
| | 02:49 | look for all these little
anomalies and differences.
| | 02:52 | When we clicked on reddit.com, it
became apparent that there was something
| | 02:55 | strange that had happened there,
| | 02:56 | but we wouldn't necessarily know to go
click on visits from reddit.com that day
| | 02:59 | or click every other source
that brought us traffic that day.
| | 03:02 | This isn't something that we as human
analysts are perfectly good at is going
| | 03:05 | through report after report after report.
| | 03:08 | However, this is the perfect job for a
computer, to churn through all of these
| | 03:11 | reports every day, looking for
something out of the ordinary and then alerting
| | 03:15 | us when that happens.
| | 03:16 | The problem is, if we are writing the
computer program to do that, how are we
| | 03:20 | going to tell the computer to sift
through all this data and alert us?
| | 03:24 | We could do it by quantity and we could
say when a certain change in quantity
| | 03:28 | happens to alert us.
| | 03:30 | The problem there is if we are
thinking it's something like page views, right
| | 03:34 | down here we see there are 20,000 page
views on one, other pages on our site may
| | 03:38 | only get a couple hundred or
even a couple of dozen pages.
| | 03:40 | So if we set a page-view limit of
let's say an increase of a hundred pages, we
| | 03:44 | probably are going to alert every
single day for certain pages that get
| | 03:47 | thousands of page views.
| | 03:49 | Or we may never get an alert for a page
that only gets a couple of dozen but it
| | 03:53 | goes up to a hundred and that would be
significant for that particular page.
| | 03:57 | The other option is we could
look at things via percentage.
| | 04:00 | The problem there is things that have a low
quantity are going to have high percentages.
| | 04:05 | For example, if we are used to getting
one conversion and all of a sudden that
| | 04:07 | goes to three conversions, percentage-
wise that's going to be a very large jump.
| | 04:11 | But in reality I don't necessarily
want to get alerted when three things sell
| | 04:15 | instead of one thing.
| | 04:16 | That's not going to be significant to
me in the overall case of my business
| | 04:19 | where I'm selling thousands of items per day.
| | 04:20 | So percentages can be problematic as well.
| | 04:23 | What we are really trying to say, in
English, is I want to be noted when something
| | 04:26 | significant happens.
| | 04:27 | So what we are really looking at is
something that's different from the expected.
| | 04:31 | The way Google Analytics is going to
look at this is through essentially
| | 04:34 | standard deviations.
| | 04:36 | It's going to look and see what was
expected, and then it's going to look
| | 04:39 | at what was actually happening, and it's
going to look at how different those things were.
| | 04:44 | That's what we have over here
in the gray bar of Importance.
| | 04:46 | When things are significantly different from
what was expected, it's going to be more important.
| | 04:51 | So this algorithm isn't based purely
on quantity and it's not purely based
| | 04:55 | on percentage either.
| | 04:56 | It's closer to what you might
think of a standard deviation.
| | 04:59 | The key to this is that it's going
to stop us from getting false positive
| | 05:03 | predictions, because as analysts if
day after day we get false positive
| | 05:07 | predictions of things that are
supposed to be important or supposed to be
| | 05:10 | significant but actually aren't,
| | 05:11 | we are going to start ignoring those.
| | 05:13 | In fact, we have the ability to
control how much of those we get or don't get
| | 05:18 | by the slider up here where we can say we
want the alert importance to be low or high.
| | 05:22 | If I select over here to low then I'm
going to see more alerts here that don't
| | 05:27 | necessarily meet a high threshold of importance.
| | 05:29 | If I say, listen, I am very busy today,
| | 05:31 | I only want to see the high level
alerts, then I can move this over to high.
| | 05:36 | I'm going to get far fewer alerts,
but the alerts that I do get will be of
| | 05:39 | very high importance.
| | 05:41 | In this case, we can see things that
were predicted to be in a certain range
| | 05:46 | but their actual was very far away from that.
| | 05:48 | So in this case we expected to
have 11% to 12%; instead I got a 24.
| | 05:53 | In this case 3.7 to 4.7; instead I got a 26%.
| | 05:58 | These things have a high level of
importance and therefore probably something
| | 06:01 | that we're going to want to be alerted to.
| | 06:02 | So let's go ahead and put this to use.
| | 06:05 | Let's scroll up here. Let's set my alerts somewhere
here in the middle and back to our overall graph,
| | 06:10 | and we can see our visits here and
some medium alerts that have come across.
| | 06:14 | One of the things that I think is
critically important for this is when I
| | 06:18 | talk about insights,
| | 06:19 | we are really looking for that
needle in a haystack, except I don't even
| | 06:22 | necessarily know that the needle exists.
| | 06:24 | What I mean by that is if I were to see
this large spike here, even without the
| | 06:28 | Intelligence reports, I would
probably figure out what that was.
| | 06:32 | I would do some searching.
| | 06:33 | I would go through my reports and I
would see that something happened there of
| | 06:36 | significance and I would go
and figure that out myself.
| | 06:39 | The real benefit of the Intelligence
reports is for uncovering insights that I
| | 06:43 | probably never would have found,
because I had no reason to believe that
| | 06:46 | anything out of the ordinary was happening.
| | 06:49 | As I look across my visits graph, there
are a couple of days that kind of jump
| | 06:52 | out of me as large spikes.
| | 06:54 | But as I look down here across my
alerts I have a few days where there aren't
| | 06:58 | necessarily anything in the visits
graph that would make me go and look at that
| | 07:01 | if I didn't otherwise have a reason to do so.
| | 07:03 | For example, let's take a
look here at one of these.
| | 07:06 | On November 15, up here in this graph
the normal visits top line data over time
| | 07:10 | graph doesn't give me any reason to
believe this is a day out of the ordinary.
| | 07:14 | In fact, it looks to be a
little bit of a low-performing day.
| | 07:16 | If I scroll down here what I see
is something out of the ordinary
| | 07:20 | I probably never would have found.
| | 07:22 | The prediction algorithm
expected between 0 and 140.
| | 07:26 | In other words this is not a page
that gets a lot of traffic. But in this
| | 07:29 | particular day instead of 0 through 140
as it was expected, there are over 1100
| | 07:33 | visits to the Go Gopher Figurine page.
| | 07:38 | Similarly, if I come over here and take
a look on November 28, I see that this
| | 07:42 | was again not necessarily out of the
ordinary day by any stretch, a little
| | 07:46 | higher than the ones around it, but
nothing on the course of the months that
| | 07:50 | would be anything of interest. But as I
scroll down here, I see that there were
| | 07:53 | some high-importance events.
| | 07:55 | In this case in terms of orders being
completed, it was expected to be somewhere
| | 08:00 | in the low $200 range, and was up over 950.
| | 08:04 | Revenue was expected to be between $1000
and $2600; instead it was $22,000--over
| | 08:09 | 500% revenue boost there.
| | 08:11 | I think this is really one
of the major values of this.
| | 08:14 | If I click on this particular report, I
see that there is a significant event on
| | 08:17 | that day, but I may have never known
to find this needle in the haystack if I
| | 08:21 | wasn't particularly looking for
this, which frankly I wouldn't be.
| | 08:24 | What we are doing here is utilizing
the power of Google servers to search
| | 08:28 | through and find these things that I may
not otherwise find and to surface those
| | 08:32 | up so that we as analysts can spend
our time looking at things that are
| | 08:35 | interesting and different rather than
searching through numbers, which is what
| | 08:39 | the computers can do.
| | 08:40 | Intelligence can be used in many ways,
such as to find these insights that were
| | 08:43 | buried beyond our view or as we saw on
the first example, a bit more forensic, to
| | 08:48 | explain something that we saw
but couldn't necessarily explain.
| | 08:51 | So the best way to find out what type
of intelligence Google has found on your
| | 08:55 | site is to simply open
these reports and start digging.
| | Collapse this transcript |
| Creating custom intelligence alerts| 00:00 | Google does a really good job of
constantly scanning your account and looking
| | 00:03 | for things you may be interested in.
| | 00:04 | But we also have the ability to
simply go in and create our own alerts for
| | 00:07 | things that we know we are interested in.
| | 00:09 | We can also tell Google to contact us
directly when those things occur so we
| | 00:13 | don't have to worry about logging in.
| | 00:14 | It's a sinking feeling when you log
into your Analytics after some time and
| | 00:17 | found that something has changed on
your site that has broken your analytics
| | 00:20 | tracking and your goals haven't
been tracking for the last month.
| | 00:22 | Or worse yet, what if you realize
something was wrong with your site and you
| | 00:26 | didn't realize it, because let's be
honest, how often we actually go in and use
| | 00:30 | our own contact form to contact ourselves?
| | 00:32 | Relying on your visitors to alert
you when something is broken isn't a
| | 00:35 | great strategy either.
| | 00:37 | So one obvious custom alert might be
to tell us when there has been a drastic
| | 00:39 | drop in some of our own
key performance indicators.
| | 00:43 | So first let's create an alert based on
goal conversion rate dropping by more than 80%.
| | 00:46 | If we go here to the Home tab > click
on Intelligence Events > Overview,
| | 00:51 | we see our Custom Alert tab.
| | 00:53 | Click on Manage custom
alerts and Create new alert.
| | 00:56 | The first thing we want to
do is give it a good name.
| | 01:00 | We have the option of applying to the
current profile or other profiles at the same time.
| | 01:04 | We can also select the
period we want to be analyzed.
| | 01:07 | In this case we will keep it at one day.
| | 01:09 | I also want it to email me
when this alert is triggered.
| | 01:12 | If I wanted to receive text messages
when these alerts are triggered, I can just
| | 01:15 | click here, enter my cell phone number,
and Google Analytics would send me a text
| | 01:19 | message with the
instructions on how to verify my phone.
| | 01:23 | Down here we set the alert conditions.
| | 01:25 | In this case I want it to apply to all
traffic, and I am going to select when
| | 01:29 | the goal conversion rate drops by more than
80% based on the same day in the previous week.
| | 01:33 | Same day in the previous week is
important because this keeps us from
| | 01:38 | getting alerts when our traffic coming from
the weekday drops to the traffic on Saturday,
| | 01:42 | even though that's a normal trend, so we
don't want to get an alert when that happens.
| | 01:46 | We click Save Alert.
| | 01:47 | Now we see that we have this custom
alert goal conversion drop by 80%.
| | 01:51 | Now one potential problem with what I
have done here is that the goal conversions
| | 01:54 | are based on quantity, not value.
| | 01:56 | You may have heard of instances where
stores do a typo and they put items for
| | 01:59 | sale for $10 that would normally cost $1000.
| | 02:01 | Well, their goal metrics probably
would be through the roof, as people will be
| | 02:06 | snapping up those items and the
quantity of conversions would be very high.
| | 02:09 | In these cases we wouldn't be getting
any alerts if it was based on goals.
| | 02:13 | So instead, figure that out, we have to
send an alert based on revenue rather
| | 02:16 | than goal conversion rate.
| | 02:18 | For example, I could say if revenue drops
by more than 80% based on the same day in
| | 02:21 | the previous week, I want to receive an alert.
| | 02:24 | Another thing we'd like
to think about is traffic.
| | 02:26 | Hopefully if your site goes down for
an extended period of time, you won't need
| | 02:29 | Google Analytics to tell you about it.
| | 02:30 | But what we see quite often is that
when your site changes and it breaks your
| | 02:35 | Google Analytics tracking code by
mistake, you wouldn't necessarily know
| | 02:38 | anything is wrong until you actually log
in to Google Analytics and notice it, and
| | 02:42 | by then you've lost all that data.
| | 02:44 | So setting up a page-view based alert,
say a 60% drop, would help with that.
| | 02:48 | A similar traffic based alert is also
hopeful for when Google Analytics sees a
| | 02:53 | traffic drop due to an
implementation problem or a marketing problem.
| | 02:57 | So let's say here that
traffic drops by more than 60%.
| | 03:05 | So again we put in our name here.
| | 03:07 | We select the period that we are interested in.
| | 03:09 | We want this to apply to all
traffic and alert me when visits is going to
| | 03:14 | decrease by more than 60%.
| | 03:15 | So we created that alert, and it's
going to tell us when traffic drops.
| | 03:20 | We can also think about setting this up
based on different types of marketing.
| | 03:23 | Let's say, for example, that you want to
track if your site suddenly got dropped
| | 03:26 | out of the search engine rankings.
| | 03:28 | What we could do is set this up on a
weekly or a monthly alert based on organic
| | 03:32 | traffic rather than all the traffic.
| | 03:34 | Let's go ahead and set that up.
| | 03:35 | Create a new alert. In this
case, let's set it to be the Month.
| | 03:42 | I want to be emailed.
| | 03:44 | Instead of All Traffic here, I am
looking specifically in Traffic Sources or
| | 03:56 | Medium matches organic.
| | 03:57 | In this case, I want to know when
visits drop by a percentage decrease more than
| | 04:06 | let's say 20% in this case rather than 60%.
| | 04:09 | My organic traffic is pretty steady,
| | 04:11 | so in this case I'll want to know if it
drops by 20% as that would be quite unusual.
| | 04:15 | Another thing that I might want to do
here is create another alert that would be
| | 04:19 | the case where if my percentage of
traffic increases by more than 20%, as that
| | 04:22 | would be an interesting event as well.
| | 04:27 | The last example here will be something
measuring response to branding efforts.
| | 04:31 | Measuring that response can be somewhat
difficult, but if you're about to launch
| | 04:34 | a viral campaign and you want to see
when the buzz hits, this can be done very
| | 04:38 | easily with custom alerts.
| | 04:39 | Let's say you want to set up an alert
based on specific keywords that you care about.
| | 04:43 | In that case, we want to have Google
send us an email if let's say our metrics
| | 04:48 | go up or down by 20%.
| | 04:50 | We are going to call this our
branding traffic, and we'll say up by 20%.
| | 04:58 | Our alert conditions in this
case applies to a specific keyword.
| | 05:03 | Rather than matching exactly, I
simply want to put in here either that it
| | 05:06 | contains or if you know how to use
regular expressions, you could do that.
| | 05:14 | Alert me when the visits
increase by more than 20%.
| | 05:19 | You could set this to be the day or
the week, however granular you want to be.
| | 05:22 | Now there is only one caveat with all of these:
| | 05:25 | these alerts are generated each night,
| | 05:27 | so the tightest timeframe
you can select is a day.
| | 05:30 | So if your web server goes down at
9 a.m. it's not like you're going to
| | 05:33 | be immediately paged.
| | 05:34 | This isn't a minute-by-minute
uptime monitoring service per se,
| | 05:37 | so if you need that kind of thing,
there are plenty of tools out there that do
| | 05:40 | that, but this isn't going to be it.
| | 05:42 | However, custom alerts are a powerful tool
that keep a lookout on your behalf and
| | 05:45 | keep you from getting
blindsided when things do change.
| | Collapse this transcript |
| Creating and customizing dashboards| 00:00 | The latest version of Google Analytics
offers a much-improved dashboard tool
| | 00:04 | that gives us quick access to some of the
key performance indicators for a given profile.
| | 00:08 | The new dashboard is widget based and
designed to be interactive and customizable.
| | 00:12 | We go to the Home tab and use a left-hand
navigation to begin assembling our dashboard.
| | 00:17 | In this case we will start with a blank canvas.
| | 00:19 | We put our title down here
and begin selecting our widgets.
| | 00:24 | There are four different types of
widgets available to us: a basic metric
| | 00:28 | display, a pie chart, a
timeline chart, and a table.
| | 00:31 | Let's start with the METRIC widget. In
this example let's take a look at bounce
| | 00:34 | rate for CPC traffic.
| | 00:36 | Bounce rate is an important metric for
CPC because that's just past the point
| | 00:40 | where you pay for traffic.
| | 00:41 | In other words if someone clicks on
your ad and then leaves immediately
| | 00:44 | thereafter, then they went just far enough
to hit you with the bill for a click,
| | 00:48 | which is a bad thing.
| | 00:49 | So first I pick my metric.
| | 00:51 | I can look through this long list here,
but it's easier just to type in "bounce,"
| | 00:55 | and we can see all the metrics that show
with the word Bounce showing up here. In
| | 00:58 | this case there is two.
| | 00:59 | I am going to select the one
I want, which is Bounce Rate.
| | 01:02 | The next step we can do is to apply a filter.
| | 01:04 | I don't want to see the bounce rate for
all the traffic, just CPC, so I need to
| | 01:07 | filter out the rest.
| | 01:09 | First I open this dropdown to select
Only show, and then I add the dimension
| | 01:13 | mention I want. Just like with the
metrics, just typing in what I want and our
| | 01:16 | medium is easier than going through the list.
| | 01:19 | Next I will select my operator.
| | 01:20 | Here we have several options for how we
can identify the mediums we want to see.
| | 01:24 | We can type in the exact dimension.
| | 01:26 | We can use Regular Expressions,
Begins With, Ends With, Containing, Less
| | 01:29 | than, Greater than.
| | 01:30 | In this example we will choose contains and
then just type in the medium, which is CPC.
| | 01:35 | We give our widget a name. In this
case I am going to call it CPC Bounce Rate.
| | 01:38 | And if we'd like, we can even select
a report down here I want link to to
| | 01:42 | provide further information about this metric.
| | 01:44 | As you start to type in this field,
you will see the various reports that are
| | 01:47 | available included in the words we're typing.
| | 01:49 | So in this case if I start to type
advertising, I can see the Advertising /
| | 01:52 | AdWords forms. In our case I want to
look at AdWords / Campaigns or AdWords /
| | 01:57 | Keywords since this is related to the
thing that I'm looking at. In our case I
| | 02:00 | will choose keywords and click Save to save it.
| | 02:03 | Okay, we can see our widget in the
bounce rate right now, and we can see that our
| | 02:07 | bounce rate in this case is 55% and
that it's 11% below the site average.
| | 02:12 | And this little sparkline on the right
doesn't necessary show me everything I
| | 02:15 | need. So I can click up here on the link,
which is going to take me directly into
| | 02:19 | the Keyword report which
we linked in the widget.
| | 02:21 | Let's go back to the dashboard so
we can continue adding more widgets.
| | 02:25 | On this particular widget we also have
the option of clicking this gear, which
| | 02:28 | will allow us to edit each of these.
| | 02:30 | We can change the options that we selected.
| | 02:32 | We can change the different types of
widgets. In this case instead of changing
| | 02:35 | it, we are just going to add some more widgets.
| | 02:36 | I do that by clicking the
ADD WIDGET button on the top.
| | 02:39 | For this example, I want to see
which mediums are making up the largest
| | 02:42 | portion of my traffic.
| | 02:43 | So I am going to select PIE chart, and then I
am going to select my metric, which is visits.
| | 02:48 | I am going to group these
by the dimension of Medium.
| | 02:53 | I can choose to show 3 to 6 slices.
| | 02:56 | I know I have more than 6 million
mediums and I want to see as many as possible,
| | 02:58 | so by selecting 6 slices, my widget will
show the top five medium by the number
| | 03:02 | of visits and then group all
the other mediums into one slice.
| | 03:06 | I could add a filter here if I wanted to,
but in this case I want to see all the traffic,
| | 03:09 | so for now I'll just name
my report Visits by Medium.
| | 03:12 | I am going to link to the
Traffic Sources / All Traffic report.
| | 03:18 | One thing to note is you can also
just put in a direct hyperlink here. If I
| | 03:21 | wanted to, I could simply type a URL in
here and link directly to that as well.
| | 03:25 | We click Save and now we can see our new
widget show up on our dashboard next to
| | 03:30 | the widget we previously created.
| | 03:31 | If we look through other options here,
we also have the ability to create a
| | 03:34 | timeline where we can choose one to
two metrics to display in this Data Over
| | 03:37 | Time chart, add a filter length to report
just like we see in the previous two visits.
| | 03:42 | For the table widget, we pick a
dimension of one to two metrics and when you're
| | 03:46 | picking your metrics, try to pick one
quantitative metric, like visits, to show
| | 03:49 | how much of your traffic you are
looking at and then try to pick a performance
| | 03:53 | metric, like goal conversion rate or revenue,
to tell you how that portion is performing.
| | 03:57 | And here we have the option to apply a
filter, name our widget, pick a report
| | 04:01 | to link to, and save.
| | 04:03 | You can also add widgets to the
dashboard directly from reports. For example, if
| | 04:07 | we wanted to keep an eye on which times
of the day were converting the highest for
| | 04:10 | our paid traffic we can navigate to the
Day Parting report and configure it as we'd
| | 04:14 | like and then click the ADD TO
DASHBOARD button. Let me show you how.
| | 04:19 | Here we are in the Advertising section.
Under AdWords we see the Day Parting
| | 04:22 | report and it's going to show us the
different hours of the day that resulted in
| | 04:26 | different visits and all the
metrics associated with those.
| | 04:29 | In this case I am interested in
Ecommerce to understand how those particular
| | 04:32 | hours of the day are resulting in money.
| | 04:35 | I can go up here to the top, click on
ADD TO DASHBOARD, and we will see this
| | 04:38 | report can be added to any of
the dashboards I have created.
| | 04:41 | We can add a timeline or table. Click
ADD TO DASHBOARD and we'll see those
| | 04:44 | reflected in our new dashboard.
| | 04:46 | Now keep in mind not all of the
widgets have the ability to keep all the
| | 04:50 | customizations and filters that you
can apply inside of reports, so make sure
| | 04:53 | you double-check what you see here in
the dashboard with what your report looked
| | 04:57 | like when you added it to the dashboard.
| | 04:59 | If you need more than one dashboard
you can click the + New Dashboard button
| | 05:02 | here in the left-hand navigation and
either build one from scratch in a blank
| | 05:05 | canvas or you can use a starter
dashboard and customize it from there, building
| | 05:09 | as many dashboards as you need.
| | 05:11 | The dashboard is a great tool to
give us a bird's-eye view of the key
| | 05:13 | performance indicators that
matter to you and your organization.
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ConclusionGoodbye| 00:00 | Thank you for completing Google
Analytics Essential Training.
| | 00:03 | I truly hope you enjoyed it and learned a lot.
| | 00:06 | Hopefully you're diving deep into your data
and pulling out all kinds of insights you
| | 00:10 | can use within your organization.
| | 00:12 | However, if you do feel like you need some help, I'll
point out again that there is assistance available.
| | 00:17 | There is a network of Google-certified
partner consultants like myself, as well as plenty
| | 00:21 | of free tools, such as the Conversion University,
both of which are linked from the main Google
| | 00:26 | Analytics website.
| | 00:27 | Now let's talk briefly about next steps.
| | 00:30 | We have talked at length about how to measure
what's happening on your website so we can
| | 00:34 | learn from it and pull out those insights.
| | 00:36 | Now, it's time to take action
to actually improve things.
| | 00:40 | Thanks for joining me.
| | Collapse this transcript |
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