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Google Analytics Essential Training (2010)

Selecting and comparing date ranges


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Google Analytics Essential Training (2010)

with Corey Koberg
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  1. 6m 2s
    1. Welcome
      1m 13s
    2. How to get the most from this course
      3m 11s
    3. What's new in this update?
      1m 38s
  2. 5m 19s
    1. The pitfalls of hit counting and turning data into information
      3m 6s
    2. Web analytics: A tool and a process
      2m 13s
  3. 15m 30s
    1. Defining goals and conversions: Why do you have a web site?
      5m 40s
    2. Understanding data: Averages, segments, trends, and context
      1m 51s
    3. Introducing segments
      2m 38s
    4. Understanding trends and context
      5m 21s
  4. 11m 25s
    1. How does Google Analytics work?
      2m 18s
    2. Setting up an account
      2m 49s
    3. Installing tracking code on a site
      6m 18s
  5. 24m 20s
    1. Understanding accounts and profile administration
      6m 59s
    2. Navigating the reports and the Data Over Time chart
      4m 45s
    3. Selecting and comparing date ranges
      6m 50s
    4. Using annotations to make notes in data
      2m 30s
    5. Using the help tools
      3m 16s
  6. 24m 20s
    1. Viewing data in different formats (overview, tabular, pie, bar, compare to site)
      6m 10s
    2. Navigating data with site usage, goals, and e-commerce metrics
      9m 20s
    3. Sorting data with inline and advanced filters
      8m 50s
  7. 10m 26s
    1. Understanding the importance of segmentation in data analysis
      4m 40s
    2. Slicing data with dimensions
      5m 46s
  8. 7m 38s
    1. Why share data?
      1m 10s
    2. Managing user accounts and profiles
      4m 8s
    3. Emailing reports
      2m 20s
  9. 29m 12s
    1. Understanding who is visiting a site
      1m 20s
    2. Analyzing location data
      4m 52s
    3. Using language identification to segment users
      1m 35s
    4. Differentiating new users from returning users
      2m 1s
    5. Understanding visitor loyalty vs. recency
      4m 25s
    6. Comparing data according to visits, visitors, and page views
      2m 10s
    7. Sorting data by browser capabilities
      3m 56s
    8. Analyzing data from mobile browsers
      2m 34s
    9. Using flow visualization to see common paths
      6m 19s
  10. 23m 50s
    1. Linking an AdWords account to Google Analytics
      2m 46s
    2. Identifying campaigns and segmentation options
      5m 55s
    3. Using keyword reports
      1m 31s
    4. Fine-tuning your match type with the Matched Search Queries report
      3m 44s
    5. Optimizing traffic by time of day
      1m 37s
    6. Using the Destination URL report to identify landing pages
      1m 45s
    7. Identifying the best placement options for ads
      2m 0s
    8. Keyword positions
      4m 32s
  11. 40m 3s
    1. Understanding where site visitors come from
      2m 32s
    2. Analyzing the All Traffic Sources report
      2m 4s
    3. Identifying direct traffic
      2m 20s
    4. Identifying users who were referred to your site
      3m 9s
    5. Viewing search engine reports (overview, organic, and paid)
      4m 52s
    6. Introducing campaign tracking
      11m 17s
    7. Planning, creating, and logging a tracking strategy
      2m 58s
    8. Tracking offline campaigns
      7m 11s
    9. Finding data in a Campaign report
      3m 40s
  12. 36m 43s
    1. Analyzing top content by metrics and the navigation summary
      3m 29s
    2. Sorting top content according to page title
      3m 57s
    3. Understanding when to use content drilldown
      2m 25s
    4. Measuring the importance of top landing and top exit pages
      3m 41s
    5. Identifying slow-performing pages with the Site Speed report
      4m 6s
    6. Understanding the Site Search and Usage report
      3m 29s
    7. Analyzing the Search Terms and Search Term Refinement reports
      4m 12s
    8. Using the Site Search Pages report to understand how users search
      5m 19s
    9. Configuring Site Search
      6m 5s
  13. 33m 49s
    1. Understanding the Goal reports
      4m 24s
    2. Configuring goals
      9m 55s
    3. Understanding funnel visualization
      9m 48s
    4. Identifying value through E-commerce reports
      4m 35s
    5. Using goal flow to find detailed insights
      5m 7s
  14. 24m 25s
    1. Real-time data for time-sensitive analysis
      4m 21s
    2. Using intelligence alerts to flag important events
      8m 59s
    3. Creating custom intelligence alerts
      5m 48s
    4. Creating and customizing dashboards
      5m 17s
  15. 43s
    1. Goodbye
      43s

Video: Selecting and comparing date ranges

One of the most critical steps of doing Web analysis is defining and comparing date ranges. This is a powerful feature in Google Analytics, and could be used in nearly every report in the interface. In each report, we will only see the data for the date range selected. We'll come here, and select the profile that we're interested in doing analysis on. And when we first login, what we're going to see here is the last 30 days, up until yesterday. The reason for this is because the current day is not yet complete, and so it could skew our data. If I want to select the entire month of October, I simply click on the month of October.

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Google Analytics Essential Training (2010)
4h 53m Beginner Oct 08, 2010 Updated Dec 20, 2011

Viewers: in countries Watching now:

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.

Topics include:
  • Setting up an account
  • Installing tracking code on a site
  • Reading the dashboard and understanding high-level metrics
  • Understanding how visitors use and navigate web site content
  • Analyzing visitor and traffic source reports
  • Tracking AdWords and other marketing campaigns
  • Planning and configuring goals
  • Utilizing segmentation for deeper analysis
  • Understanding the raw data and how it's collected
  • Selecting and comparing date ranges
  • Using flow visualization to see how visitors navigate through a site
  • Identifying slow-performing pages
  • Performing real-time analysis
  • Using annotations and other best practices
  • Configuring and analyzing internal site search
  • Determining the best report view to use
  • Navigating reports with tabs
  • Cleaning up data with inline filters
  • Sharing data and reports
Subjects:
Business Online Marketing Web Data Analysis Web Analytics SEO
Software:
Google Analytics
Author:
Corey Koberg

Selecting and comparing date ranges

One of the most critical steps of doing Web analysis is defining and comparing date ranges. This is a powerful feature in Google Analytics, and could be used in nearly every report in the interface. In each report, we will only see the data for the date range selected. We'll come here, and select the profile that we're interested in doing analysis on. And when we first login, what we're going to see here is the last 30 days, up until yesterday. The reason for this is because the current day is not yet complete, and so it could skew our data. If I want to select the entire month of October, I simply click on the month of October.

If I want to select the entire month of September, I click on September, and it will auto pre-fill those dates for me. I click Apply, and then I will see the date range change to the month of September, and the corresponding data down here will update to show just that date range. I can also select any custom date range that I want. For example, I could put up here, going all the way back to 2009, and I can put my cursor here to select the particular date that I want. In this case, I want to do September 1, 2009 through September 1, 20011.

Click Apply, it will automatically update. Now in this case, because I've selected a long date range, I probably want to back this down to either weeks or months in order to smooth out some of the volatility that we'll see. Okay, that looks better. I can understand trends much easier. Now, note that I see a large drop off here at the end. That's because my date range only includes up to September 1. So, because I'm looking at this on a monthly basis, the month of September of 2011 only includes data for one day. In this case, I probably want to modify this to go up through August 31, to be a more accurate comparison on a month by month basic.

One of the most powerful features is to be able to compare the current time period, versus a time period in past. For example, if I come here, and I want to select the month of October 2011, versus the previous month: September. I select the Compare to past check box, and you can see it pre-fills in the time period. I can change the time period that I'm comparing to by putting my cursor here in the first box, and selecting whichever period I want. In this case, I can compare August 1, or the entire month of September. One thing to be careful for is noting, when you're changing the date range, which particular box you have selected.

Notice that the top one always needs to be the most recent, versus the most past. So in this case, if I wanted to do October 2011, I need to put that here in the most recent, versus the past of September. Now that I have the bottom one selected, I can select September, and it will give me the accurate October versus September, which are the dates I want to analyze. Now, notice what you see here. Because I'm still selected on month by month basis, I only see two single data points for the month of September, versus the month of October. I want to go back and push this back to the daily granularity, so that I can see each of those months overlaid one on top of the other.

In this overlay, I can see the performance for any individual metric, and how one month that versus the others. As I can see, they both had a bit of a run up here in the beginning, they both tended to smooth out, there was some separation here, and then there was a large bump for the orange one here, which represents the month of September, as we got towards the end of the month, and then they came back down towards together at the end. Getting a high level view on the data over time graph up here is interesting, but it's not just here that changes with the date range comparisons; it's every report inside of Google Analytics. For example, if I were to come down here to the Traffic Sources report, and look at my All Traffic sources report, I would see different sources and mediums over those particular date rages.

Here we can see that from October versus September, there was a 60% drop in the traffic that was referred from google.com. Perhaps more interestingly is referrals from reddit.com in the month of October were 5000, but just 68 back in September. So we saw 7000% increase in the number of referrals that came from the reddit.com site over those two months. In addition to the traffic sources, I may come down here to my Content reports, and take a look at some of the reports that we've got about the content of our site, and how visitors are interacting with it.

One thing that jumps out at us right away is a large spike in the number of page views here on this particular day. If I want to investigate this a little bit closer, I can go and change my date range to just include that day; October 24 versus September 24. So on the top, I select just the day October 24, versus September 24, and all of my reports are going to change to just reflect those two days. As I scroll down here, it's going to become apparent to me where those differences in page views are. I see we jumped from 2,700 all the way up to 43,000 page views for this particular page.

One thing that I want you to be careful about when we're doing these comparisons here is to make sure we're looking at apples to apples. Yes, in this case, I am comparing the 24th of the month versus the 24th of the month, but in terms of user behavior, that's not necessarily the most important thing to think about. For example, one of these days is a Monday, and one these days is a Saturday, so on most sites, you can expect to see very different traffic, and comparing those two days, one versus the other, doesn't make a lot of sense. The other thing that I want to be careful about, in addition to days of the week, is just the overall number of days. If I'm talking about a quantity, such as the number of transactions, the amount of revenue, the number of visitors, I want to make sure the number of days that I have in each date range is the same.

For example, if I compare the month of September, I have 30 days, whereas the month of October is 31 days, so we have a different number of days being counted in each one of those. Another thing to keep in mind is in September we had a holiday. So September the 5th on a Monday is not going to necessarily be the same as that same first Monday in the month of October, because one is going to be a holiday, versus the other one is going to be a regular work day. We can expect to see different numbers there, and comparing those two may not be an apples to apples comparison. We can see a few other examples where this can cause us problems in our analysis.

For example, if I look at this case, I can see some troubling results here. In January, the green line here, I can see that in almost all cases, actually in every case, things were better than they were in July. So fast forward seven months, and my traffic has dropped, my revenue has dropped, and in general, I'm doing worse, and this is very concerning. The problem here is I haven't really looked at those apples to apples comparisons. What we want to do is break this down on a year to year basis, where we're comparing the same time periods in one year, versus the same time period in the next. In this case, when we do that, what we can see is we're actually doing better.

From 2007 to 2008; in 2008, my metrics are up across the board. The reason it didn't look like it in the top was because I was comparing the Christmas time period, versus the middle of the summer, and if you have any kind of seasonal traffic, which many of you do, you maybe comparing times that don't make sense to compare against each other, and a year over year comparison may make more sense. We'll come back to this ability to restrict, include, and compare by date range repeatedly throughout our analysis, and seeing what's changed is often more powerful than the absolute value in isolation.

Find answers to the most frequently asked questions about Google Analytics Essential Training (2010).


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Q: The course was updated on 12/19/11. Can you tell me what's changed?
A: Many movies were updated to reflect the changes in the Google Analytics user interface and new movies were added to the course as well, with topics including using flow visualization to see common paths, identifying slow-performing pages with the Site Speed Report, using goal flow to find detailed insights on funnels and conversion paths, analyzing real-time data for time-sensitive analysis, and fine-tuning match types with the Matched Search Queries report.
Q: Where can I learn more about internet marketing?
A: Discover more on this topic by visiting internet marketing on lynda.com.
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