Michael wrote last week about how to create engagement buckets. That’s a fundamental start, but the next question analysts invariably ask then is: how do we track how users move from one state to another? In other words, how do we track if users are becoming more engaged, or less? And more crucially then, how can we encourage movement between these engagement states?
Let’s continue with Michael’s example using the music finder. Suppose, we’re interested in tracking how users move from the Core cohort (play a song 1x a week) to a Power cohort (play a song at least 2x a week).
We can construct the following cohort in the Cohort report to track the trend:
We can then visualise this cohort in the Insights report:
Now that we can track how users are changing from Core to Power, we can try to come up with different hypotheses of the actions we can do to grow that cohort over time. This is where the Signal report comes in handy. We can construct the following query:
From the above report, we see the following two events that we might be able to take action on:
Users who have used Search at least twice in the last quarter
Users who have followed a playlist at least 3 times within 7 days
Two possible actions we might undertake then, to try to increase the Core to Power cohort:
Optimise the Search tool - could we put it in a more prominent position, or find other ways to encourage users to use the tool?
Could we recommend playlists to users to follow? Or place the ‘follow playlist’ button in a more prominent location?
Then, as we implement these actions, we can track at a high level, if they have caused our cohort size to increase. Of course, there’s a more precise way (using the Impact report) we can measure if these actions do indeed contribute to the increase in the cohort size, independent of extraneous variables, but that’s a topic for another day!