Are we retaining users?
With hundreds of users signing up, your product looks successful. But questions are being asked about “stickiness”. If too many users eventually leave, soon you’ll have none left.
Cohort Analysis
A cohort is a group of users who share something in common, be it their sign-up date, first purchase month, or acquisition channel...More
Thanks again for staying late
I have a sensitive project we need to work on
Despite all the good work we've done to bring in new users, I'm worried they're not sticking around
After they make their first loans, are they making more?
That's the important thing for us to crack
But of course I don't want to alarm anyone that we're looking into it
I'd appreciate if you could run a cohort analysis for me
Share the results directly with me, no one else
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A cohort analysis is a form of longitudinal study used in medicine, social science and marketing. A cohort is a group of users who share something in common. You could group users by their sign-up date, first purchase month, or acquisition channel. By tracking these groups over time, you can spot trends, and understand repeat customer behavior.
This type of analysis is essential for discovering retention trends because looking at aggregate metrics you can't tell if users are coming back. For example you could have a growing total user count, but that might be bolstered by the number of new users joining. When you group users into cohorts based on the month they signed up, you can more easily notice that a large percentage of them are dropping off.
Cohort analysis requires a cohort period, be it day, week or month, which you use as the X axis when visualizing it. If you're using monthly cohorts, a cohort period of 2 would refer to the second month they were active, 3 would be the 3rd month and so on. This data can either be visualized as a line chart, with one line per cohort and the cohort period on the x axis, or as a heatmap. Heatmaps shade each box representing a cohort and cohort period by the percentage of users who did the relevant action and were retained. Dark or light spots can indicate peaks or dips in retention activity, which makes it easy to scan and highlight anomalies.
I ain't going to walk you through how this whole works. So the first section here is just importing all the libraries and you just want to hit shift, enter where you can hit the play button hit, and it's going to connect you to Google CoLab to the backend. And then I should say connected and then finish running and you shouldn't have any issues there.
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