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In Google Analytics Essential Training, Corey Koberg shows how to use the Google web analytics platform to generate and evaluate information about the visitors to a web site, including data on site traffic, user behavior, and marketing effectiveness. This course covers the out-of-the-box functionality, from account creation to reporting fundamentals, and explains how to glean insights from the vast array of data available.
Dimensions are built-in segments, and that's one of the primary ways that we will segment data in Google Analytics. Let's talk about organic search engine traffic for a second. Naturally, keywords are always a part of that conversation, and in fact Google Analytics makes it the default dimension for the organic search report. But for now, let's look at this report using a different dimension. At the top of the table we can see a few other popular dimensions for this report, such as Source and Landing pages, and also a dropdown here where we can take and choose any dimension that we like. Let's talk about Landing pages.
The interesting thing about organic search is that we don't get to choose where in our site that traffic gets sent to. The search engine does that by ranking different pages that they feel are the most relevant to the search term. And maybe that's the homepage, maybe it's a blog post, maybe it's an old page that I didn't even realize was still accessible on my site. This is information we want to know. So whatever page on our site ranks for those keywords is going to be the landing page. So we can change the Dimension from Keywords to Landing page. This way we can see which of those are the most popular. In this case, the home page is our most popular organic landing page, followed by the blog, and who we are page listing all of the partners and employees.
But does this really answer the question we're looking for? This just shows us the most popular landing pages from Google's organic searches, but in the aggregate. We're really trying to figure out the most popular keyword and landing-page combinations, and we've lost that connection by switching from one dimension to another entirely. For this, we can use secondary dimensions to show the most popular combinations of the two dimensions we really need to know-- search term and the corresponding landing page. To do that, we change our primary dimension here back to keywords and we select our secondary dimension to be the landing page.
Now we can see a list of the different keywords that people searched on, as well as the landing page that they landed on our site from that keyword. Let's take a look at our non-branded keywords. So we'll apply an advanced filter to exclude the keywords that matches our branded searches. In this case, we're going to click on advanced filter, Exclude > Keyword. In this case we're going to use a regular expression so that we can put multiple keywords in a single box. In our case we're going to put cardinal, and then that vertical pipe is going to mean and/or, and path. That should take care of most of our brand of keywords.
For the most part, it looks like Google has done a pretty good job of matching keywords to the relevant page, although I am a little intrigued when I see this one here, loss aversion, that goes to our homepage. This might be something I need to take action on. I need to do some work on my SEO to make sure Google, Bing, and the rest aren't confused about what my pages are really about. Let's take a look at another real-life example. Perhaps I am in the process of deciding which cities to hold our next Seminar for Success Google Analytics Live Trainings, and I want to see where there is the most interest. One thing I could do is find keywords that match that topic.
I am going to set a secondary dimension of city, and I am going to use inline filters to restrict the keyword set to just the things that have to do with training. So keeping primary and this is the keyword. I am going to change my secondary dimension here based on Visitors to City. I am going to change my advanced filter to restrict the keywords set by including keywords that are just related to seminars. So in this case we'll obviously have words like seminar, but I also want to capture some other words around training, so I am going to again use a regular expression with that vertical pipe bar to include more than one word inside the box. Click Apply and we'll update our report.
What we see here is quite interesting. Keywords that have to do with training are centered around these cities, and so we see Vancouver, Rockville, Franksville all have a significant amount of visits of people looking for that type of training. These might be potential candidates for us to hold future seminars. And we've looked how to isolate traffic, modify our dimensions, and add secondary dimensions, but we can go even further. Pivot tables allow us to take slicing and dicing to the extreme. Let's say I want to look at which keywords are being searched on via the search engines and see if there are any differences in countries. I want to evaluate the quality of traffic as well as the quantity, to see if perhaps traffic from other parts of the world are more or perhaps less likely to stay on our site.
So we're going to choose Bounce Rate as our secondary metric. Let's go ahead and do that. First thing I need to do is clear my advanced filter and select Pivot from the View dropdown. Okay, now I see a list of keywords for my site. I am going to set the Secondary dimension here to be Country. Now remember, I wanted to see Visits, but I also want to see performance, so I am going to set the secondary here to be Bounce Rate. And lastly, I already have Keywords as my primary dimension, so I am going to pivot by source. So what this shows me is people who searched on these keywords in these countries from these sources have these results.
From visits in the US who searched for cardinal path, I see 1,600 visits in total, with an average of Bounce Rate of 35%. When I break it down by individual sources here, I can see Google sent a vast majority of the visits and had a 35% bounce rate. Bing sent less visits but had a better Bounce Rate, et cetera. I can see that for one of these different keywords, broken down by each individual source and country. If I want to break this down even further, I can come back to my advanced filter and I can remove those branded keywords. And I am going to Exclude > Keywords > Matching Regular Expression, and I am going to put in my branded keywords.
In other words, these are words where it's going to indicate that someone is already familiar with my brand, products, services. As you can see, we've got an incredible amount of data and we've sliced and diced that down to get very granular information. One thing I want to point out: when you start slicing and dicing data down this finely, you do need a fair amount of data to actually populate this report and create segments with meaningful data. So while it may be interesting to look at the number of users who converted on a goal that came in on a specific organic keyword on an iPhone from Minneapolis in the last two days, you're going to need to have visitors who actually fall into that segment in order for it to be meaningful.
Combining segmentation and dimensioning allows us to quickly and deeply segment our data to get answers to even the most difficult analysis questions.
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