Retention reveals repeated value
Unit 5 of 8 · Lesson 2 of 4Index
Signups are up 40 percent this month and Maya is delighted. Week two retention is 9 percent, and nobody has looked.
Read the shape, not the level
- 01
Follow one group
Take everyone who joined in the same week and count who comes back in week one, two, three and four.
Follow one group - 02
A curve that flattens
Some people found something worth repeating. That is the group to study first.
A curve that flattens - 03
A curve that keeps falling
People have not found a reason to come back yet. More features rarely fix that.
A curve that keeps falling - 04
Signups can hide a leak
A fuller bucket with a hole in it is still leaking. Watch the curve, not the top line.
Signups can hide a leak - 05
Cause, or the kind of person?
When one group stays longer, ask whether what they did caused it, or whether people who stay simply behave that way.
Cause, or the kind of person?
The same cohort, two possible stories
Interactive teaching example · All numbers and tickets are fictional.
A flattening tail suggests a group may be finding repeated value. Four weeks cannot establish a lasting plateau. Each week counts return in that interval, always divided by the original 100.
How Slack handled it
A behaviour that predicts retention is a hypothesis about what causes it. Test it before you pull it like a lever.
Slack
Slack's leaders have said that teams which had sent around 2,000 messages were far more likely to keep using it.
That number became a well known target for getting new teams started. It is also a correlation: teams that were going to adopt Slack anyway are exactly the teams that send lots of messages.
Public commentary. No affiliation.
Doing it with AI, and where it breaks
Group churned and retained users by what they did in their first week, and ask a model which action best separates the two.
It presents correlation as cause. The action that predicts retention is often a sign of already being the kind of user who stays. Say so in your write up.
Here are first week actions for users who stayed and users who left. Which actions differ most between the groups? For each, give one reason it might cause retention and one reason it might only be a sign of it.
Build it
A cohort retention curve and one testable hypothesis about the drop off.
Google Sheets
What my retention chart can and cannot tell me
Publish a 500–800 word project analysis. Define the starting event, return action, cohort, and interval. Include counts, explain one competing interpretation, and propose a next check. If you used synthetic events, say so beside the chart and do not present them as customer results.
Include: A cohort table and chart with a clear denominator and unobserved periods marked.
- Write in your own voice for someone facing the same product problem.
- Use AI to critique the structure and find unsupported claims. Verify every source, number, and quotation yourself. Add a short note describing how you used AI.
- Remove private customer, company, and participant information. Label practice scenarios and synthetic data clearly.
- Publish on Medium, Substack, or a relevant Reddit community that permits project write-ups and links. Follow its posting rules; share a useful account of the work.
Include this plain attribution with a clickable link:
I developed this project while learning product management at pmcademy.com.
The worked solution
Stuck, or want to compare?
There is a worked solution. Try the build first. Reading it before you have attempted anything is the fastest way to learn nothing.
Checkpoint
If you did the build, these take two minutes. If you cannot answer one of them, that is the part to go back to.
Moving on marks this lesson complete. Finish the build first, it is the part that counts.