Google Analytics Tips
Illustration by Richard Downs

Google Analytics Tips

with Corey Koberg

Video: User Segments: A huge improvement

In the previous videos we looked at And the options that we have here are that we It's often easiest to think about this kind of analysis on a simple Now granted, if we were just doing the session type So, let's go back to the interface and look In our preview we see we have limited that Transactions per user, over ten.
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  1. 54s
    1. Welcome to the series
  2. 5m 17s
    1. Separating Branded Keywords in the Channels Report
      5m 17s
  3. 4m 59s
    1. Combining data from multiple accounts into a dashboard
      4m 59s
  4. 8m 4s
    1. Why you may have more users than sessions (Advanced)
      8m 4s
  5. 8m 48s
    1. Extracting Google Analytics data to spreadsheets the easy way
      8m 48s
  6. 4m 40s
    1. Creating an industry vertical
      4m 40s
  7. 24m 39s
    1. Part one: Why nearly everyone gets segmentation wrong
      10m 59s
    2. Part two: Why nearly everyone gets segmentation wrong
      8m 17s
    3. Part three: Why nearly everyone gets segmentation wrong
      5m 23s
  8. 7m 18s
    1. R Programming and Google Analytics
      7m 18s
  9. 6m 29s
    1. Query string parameters
      6m 29s
  10. 17m 17s
    1. YouTube videos
      9m 3s
    2. Local visitor time zones
      8m 14s
  11. 9m 56s
    1. Importing data with Custom Data Import
      9m 56s
  12. 5m 41s
    1. Five Time zone tips to save your data
      5m 41s
  13. 4m 5s
    1. Creating a Custom Report in 30 seconds
      4m 5s
  14. 14m 16s
    1. Migrating Custom Variables to Custom Dimensions
      14m 16s
  15. 7m 11s
    1. Test driving the API in 5 minutes
      7m 11s
  16. 15m 20s
    1. What is Google’s Universal Analytics?
      7m 7s
    2. Upgrading from Classic to Universal Analytics
      8m 13s
  17. 18m 0s
    1. Finding my most valuable customers
      9m 54s
    2. Making Adwords work smarter and more profitably
      8m 6s
  18. 13m 25s
    1. Using an advanced filter to get accurate information on subdomains
      7m 20s
    2. When to use a segment vs. a filter
      6m 5s
  19. 27m 8s
    1. Cohort analysis now built into Google Analytics
      8m 22s
    2. Permanent filters and removing internal traffic
      8m 19s
    3. Filters everyone should use to get clean data
      10m 27s
  20. 26m 37s
    1. Intro and built-in segments
      14m 57s
    2. Custom segments via the Segment Builder
      11m 40s
  21. 12m 25s
    1. User Segments: A huge improvement
      6m 23s
    2. Advanced Topic: Sequenced Segments
      6m 2s
  22. 23m 58s
    1. Demographics
      6m 45s
    2. Interest categories
      5m 26s
    3. Enabling Audience reports and understanding where the data comes from
      11m 47s
  23. 20m 49s
    1. Installing Google Analytics on WordPress
      8m 28s
    2. Google Analytics debugging and troubleshooting: The basics
      6m 10s
    3. Google Analytics debugging and troubleshooting: With a tag management system (TMS)
      6m 11s
  24. 17m 8s
    1. How Google Analytics accounts, properties, and views are structured
      6m 36s
    2. User management: 2013 update
      6m 19s
    3. Best practices for user management
      4m 13s

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Watch the Online Video Course Google Analytics Tips
5h 4m Intermediate Dec 04, 2013 Updated Dec 03, 2014

Viewers: in countries Watching now:

Get a new Google Analytics tip every other week from online marketing expert Corey Koberg. Most users unlock just a fraction of the power that Google Analytics offers, so in this course Corey exposes tips and tricks to unlock insights into one of the most sophisticated tools in the marketer or site owner's arsenal. He offers peeks into the latest power features, advice for deeply mining your digital data, and actions you can take to optimize your site for both traffic and conversions. Corey answers common questions about online marketing and web analytics, including installation, tag management, reporting, custom variables/dimensions, attribution modeling, segmentation, multichannel funnels, data accuracy, visualizations, Universal Analytics, and more. What's more, Corey welcomes your questions and will shape future videos based on member requests, so send them to us at

Google Analytics
Corey Koberg

User Segments: A huge improvement

In the previous videos we looked at advanced segments but we really only focused on those that included conditions that could be met with in a single visit. For example, we'd say show me a segment that contains visits where a user purchased over a hundred dollars or show me segments where they saw a certain video or came from a certain state. But recently Google released some improvements that take our ability to segment to a whole new level. And they better reflect the type of analysis that we often do. Now before, if we came over her to the advance segments area.

And we created a new segment, we would come down, we selected our conditions. I believe we did a search, revenue And the options that we have here are that we can specify the revenue per session, or revenue per hit. But more likely per session, which is the same thing as saying per visit. We said that was going to be greater than or equal to let's say, $100. The big difference now, is, if we come up here to the filter, and we do this by user instead, we see a new option emerge. We have, now, the ability to look at revenue per user. And what this means is that this condition can be met over the course of several visits that may even span multiple dates.

Now we still have the ability to that hit level as well. But it's these new user segments that are really a big deal. And as someone who deals with large enterprise, big data type analytics all day long. I can attest that this is a really, really big deal. This is one of the very few tools that offers this kind of functionality. Let alone for free. And there was some speculation that this would be a feature that's held back for the premium version of Google Analytics that costs six figures per year. But, here it is in the free product for all of us to take advantage of. And even better, that since this is a server site feature, you can take advantage of this even if you don't yet have universal analytics.

So this could take advantage of any of the tracking codes that are sent in the data back. So let's take an example here. Let's think about airlines and frequent flyers. Airlines almost always sell out at Christmas and other major holidays, so they aren't necessarily so excited about that one traveler who bought one expensive ticket per year to make that Christmas trip home to Grandma's house. They may want to target that person who buys a medium priced ticket every single week. They're going to fly out on a Monday and back home on a Friday. That's the traveler they're trying to get more of. It's often easiest to think about this kind of analysis on a simple grid that's going to show these multiple visits across the top and in the users across the side.

So, for example, we might see something like this. We've got the flyers across the side here and across the top are our various visits. Now, we saw that first case here. Got Flyer #1 here, who visited, bought the expensive ticket here and v Visit #2 and never bought another ticket any time here. So the total amount that they spent was just the price of that one big ticket. Now granted, if we were just doing the session type analytics we've been looking at before, this Flyer #1 would, without a doubt, appear to be the best. This was the big spender, this is the person we were supposedly going to look for in terms of our analysis.

But in the scenario we just laid out, this isn't actually the person that the business case wants. The business case wanted the second one here, the $400 every single time, every single week, day in and day out. We've also got a third one down here who spent more per ticket, so we see that both of these spent more than the top person, even though these were the lowest, cheapest prices there were. So the cheapest prices for Flyer #2 who had the most money. The second cheapest here were the ones that were one above it. And then the one who bought the most expensive ticket actually ended up with the lowest overall total contribution to revenue from that particular flyer.

So you can see in this case in our analysis it's pretty simple here, that the flyers that we want to target are not necessarily the ones who are spending a lot in single session but overtime. And what we want to do is have our web analytics reflect the fact that this is the analysis we want to do. So, let's go back to the interface and look at a couple of different ways to do this analysis. The first thing I want to point out is date range. We can go back 90 days in our user analysis. So, this is known as a look back window and we have a 90 day look back window at which we're going to look at the total behavior of a single visitor.

So the first thing we said we're going to do is just by revenue. So in this case, it's just like we did with sessions, except it's going to be by user. So we select Users here. We've got revenue per user greater than or equal to, we can go ahead and test and preview that. In our preview we see we have limited that down in terms of the visitors that we've got. If we wanted to, we could name this, User Segment over $100. The second way we could examine this analysis is not just by looking at revenue but we had talked about the idea of someone coming back and making multiple purchases.

So perhaps our analysis is less about that dollar figure and more about the behavior itself. So let's do one that shows all visitors who've done over ten transactions regardless of the number of visits. So we can come here. Specify transactions. Transactions per user, over ten. Make sure that we update our title as well. Another thing that we might want to do while we're at it here is go the other direction. Perhaps we want to have low value users as well, or at least, the users who do not exhibit this behavior. So we can say, Users who did less than three transactions.

Now with those segments defined, you could apply them both at the same time, and derive even more insight. We can do all of the usual analysis we've looked at in the previous videos about which channels they come in from. Which marketing efforts were working there, what content they consumed, what path they took through the site, etcetera. All those things can be applied and we can do it for both those who are in the low value there, less than three transactions, and those who are above ten, and look at those simultaneously. And remember this is not just about revenue. We often use this as our first example, but it's far from the only type of analysis to do.

This could be really powerful when we start examining the entire purchase funnel. Particularly if we've done a good job of defining micro-conversions in steps along the way. So we could look at people who read a white paper and then use their configuration tool and then checked out. Now this can get even more powerful if we not only look at the entire scope of multiple visits, but the sequence in which they happen. So this is what we'll look at in a future video. But for now, I encourage you to spend some time thinking about how your analysis is going to change now that you can think about a segment in terms of the entire user, not just one particular visit.

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