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.

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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

Experience

Mike Taylor

Built a 50-person growth agency.
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Premium subscription required.
Python experience recommended.
Usually completed in 22 minutes.
1. Scenario
Bank of Yu Offices – Late at night
The following code is a Google Colab notebook which contains the script necessary to run a Cohort Analysis. Make a copy then review the code and run each cell to see what it does. There is an accompanying video underneath the link to the notebook that walks you through it. You will be expected to answer questions about the results of the analysis afterwards.
Ashton Donaghy
at Bank of Yu

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

This course is a work of fiction. Unless otherwise indicated, all the names, characters, businesses, data, places, events and incidents in this course are either the product of the author's imagination or used in a fictitious manner. Any resemblance to actual persons, living or dead, or actual events is purely coincidental.

2. Brief

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.

3. Tutorial

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.

CohortAnalysis.ipynb
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4. Exercises
5. Certificate

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