Overview of Cohort Tables in Analysis Workspace
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Overview of Cohort Tables in Analysis Workspace

March 7, 2020


(lively music) – [Travis] 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.

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  1. hi Adobe, is there any plan to be able to use calculated metrics in cohort analysis? I can see how this function will allow me to determine on avg how long it takes for a visitor to submit a lead. But unfortunately my 'lead' is currently a calculated metric.

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