Overview of Cohort Tables in Analysis Workspace overview-of-cohort-tables-in-analysis-workspace

The Rolling Calculation setting within Cohort Tables can be used to analyze cohorts period-over-period, to understand how the same users are retained (or churn) over time.

Transcript
Hi, this is Travis Sabin, product manager for Adobe Analytics and today I’m excited to show and share with you an overview of the great new settings and features we’ve added to the cohort table and Analysis workspace. So, let’s begin. Here you can see the new Builder state for the cohort table. There’s a lot more here and I’m gonna through each one of them individually to kind of outline what we’ve added and what it can do. So, one of the first things you’ll notice, obviously the Inclusion and Return Criteria are a little bit different. We now have segment drop zones. Previously you could only apply a segment to the panel but now you can apply individual segments for both inclusion and return, so let’s say I wanted to analyze my U.S. users as part of my inclusion but I only wanted to track Android users on return and so, I can add different segments to each of these sections and I can add up to 10 different segments for both inclusion and 10 for return as well. And then as far as the actual criteria itself, you still need a metric in order to actually build a cohort table. Segments are optional as you can see. Previously you could only add one metric but now you can add up to three and so, if I drag three different segments over here, these may not all work together but just for the sake of the demo, I can add up to three different segments and you can see, we also have this operator here so you can choose to either group them all together that the visitors must meet all three of these criteria or you can or them so they meet one of these three criteria and then additionally we have operators and numeric values, so if you wanna filter out or make users meet a certain threshold in terms of number of visits or orders or whatever it might be, you can come in and add and make changes to the operators here, so you can really filter and narrow down the user groups that you want to include as part of your segment and you can do the same thing over here on return, you can add up to three different metrics. So, for the sake of today, I’m gonna keep things simple and compare visits and online orders but that’s the new Inclusion and Return Criteria settings being able to add multiple segments and multiple metrics as far as your criteria definition. So, that’s the first one. And secondly, you can see down here we have two types of cohort tables, Retention is our default cohort table, it’s the tried and true normal one that you see. After it builds, you can see here’s the standard cohort table like we’ve had in the past. But we now have a new cohort type called Churn. If you choose Churn, and hit Build. Churn will now do the inverse of our Retention and it is marked by the red indicating the fallout instead of the retention of the users, how often are they churning and not returning to your given site or app and so, Churn is a great, easy way to see the type of behavior of users who are not coming back and possibly the opportunity of those who you know you wanna do a little bit more to engage and focus on that user set. So, that is Churn. Next, we have a new setting calling Rolling Calculation. Currently the cohort is based on users who meet the inclusion criteria and do the return criteria at any point in the subsequent date range that you have, so users in week four don’t have to meet the return criteria in weeks three, two and one but if I change this to Rolling Calculation, now you can see I have a completely different type of cohort table. Users must meet the criteria in the previous period, so these two here in week two are of this 2,400 in week one which are a subset of the inclusion group here of the 31,000 and that’s the same across the board, so in order for being included in week two, you have to have met the criteria in week one and so on and as you can see, in this dataset, none of my users are persisting down through weeks three and four and five doing continual repeat week after week behavior. And so, Rolling Calculation is really good for period over period, retention and analysis to know how your users are coming back on a repeat basis performing.
So, that’s the third setting. The next one is here in the Advanced, we have two new features. The first is a Latency Table. Latency Tables provide a good view of pre, post-analysis. So, with a Latency Table, you can see that the cohort is a little bit different. Our inclusion has now shifted out to the middle and everything to the right of it is a standard cohort showing you repeat and return users after the inclusion event but I can also now with Latency Table see pre-inclusion activity on the same table really quickly and easily, so this is great for analyzing specific events like product launches or campaigns to see what was the behavior like before the event and then the change in behavior after and so, Latency Tables again are really good for pre, post-analysis and a great feature here in our new Cohort Table Builder. So, that’s our fourth new feature that we’ve added and then finally, the last one is the Custom Dimension Cohort. And in many cases, we’ve had users request and say hey, I wanna do something not time based here on the left, I wanna compare something else, a different dimension. And so, the new Custom Dimension Cohort will allow for that. So, if I drag on the browser dimension, I wanna compare browsers side by side to know which one is driving the most online orders for my company. So, once that builds, I can now see instead of time here on the left, I have a list of my top 14 dimensions items that have returned for that specific dimension, in this case, the different browsers and so, I can see which browsers are driving a lot of inclusion and return, so Google Chrome is doing great here, 65, but I can see how that stacks up against Safari, Firefox and others to quickly evaluate which browsers are performing best at driving online orders and which ones might not be doing so well, so maybe my web experience on some of these other browsers isn’t as good as it is on Google or maybe most of my visitors are coming from Google, so I wanna be doing specific things to target Google Ads but the Dimension column is a great way to do some real cool, non-time-based dimension analysis, so you could also compare campaigns, products, pages, any other dimension you can think of to do quick side-by-side, period-over-period analysis. You can also use the Filter option here on this specific dimension, if you wanna analyze three off four specific dimensions, and not the entire list, you can add those here or if you have one that’s not in your top 14, you can find that as well. That’s our fifth new feature. So, those are all the new features that we have for the new Cohort Table. I hope that these are great things to help you in uncovering new insights for your business and thank you for your time.

For more information, please see the documentation.

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