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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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