Cohort Retention Heatmap
Did the customers we signed last spring stay? A cohort retention heatmap groups customers by when they joined - one row per acquisition cohort - and reads across the columns to show how many were still active one month later, two months later, and so on. The result is a triangle, because newer cohorts have had less time to age.
| Cohort | Size | M0 | M1 | M2 | M3 | M4 | M5 | M6 |
|---|---|---|---|---|---|---|---|---|
| Jan | 142 | 100 | 88 | 74 | 61 | 56 | 53 | 51 |
| Feb | 138 | 100 | 87 | 73 | 62 | 57 | 54 | - |
| Mar | 151 | 100 | 89 | 76 | 64 | 59 | - | - |
| Apr | 164 | 100 | 92 | 81 | 70 | - | - | - |
| May | 158 | 100 | 93 | 83 | 72 | - | - | - |
| Jun | 171 | 100 | 94 | 85 | 74 | - | - | - |
| Jul | 166 | 100 | 94 | 86 | - | - | - | - |
| Aug | 179 | 100 | 95 | - | - | - | - | - |
Month-3 retention improves steadily for every cohort acquired after March, from 61% to 74% - the step change lines up with the onboarding release.
Attribute the gain to onboarding and hold the older cohorts as the control rather than re-running the comparison on aggregate retention.
What is a cohort retention heatmap?
A cohort retention heatmap groups customers by when they joined - one row per acquisition cohort - and reads across the columns to show how many were still active one month later, two months later, and so on. The result is a triangle, because newer cohorts have had less time to age.
Why is a cohort retention heatmap useful?
A single overall retention number mixes customers acquired under different products, prices and onboarding flows. Cohorts separate them, which is the only way to tell 'retention improved' from 'we acquired more customers who had not churned yet'.
How RetainCLM uses it
If the cohort that signed up after an onboarding change retains 12 points better at month three than the one before it, that is evidence the change worked - and no aggregate retention chart could have shown it.
The chart above is drawn from illustrative demo data chosen to make the visualization legible. It does not describe real RetainCLM customers. On a live account the same chart is drawn from your own customer, usage, billing and support data.
How to read it
Read down a column to compare cohorts at the same age - that is the comparison that controls for tenure. Reading across a row shows one cohort's decay curve; the shape usually flattens once the early churners are gone.
Limitations to keep in mind
The bottom-right of the triangle is thin: recent cohorts have few observations and small cohorts swing wildly. Do not read a 3-customer cohort's 67% as comparable to a 300-customer cohort's 67%.
Related concepts
A cohort retention heatmap is most useful alongside customer intelligence, churn prediction. RetainCLM builds these views on one unified customer record, so a pattern you notice in one visualization can be followed into the next without exporting anything.
Questions about the cohort retention heatmap
What is a cohort retention heatmap?
A grid where each row is a group of customers acquired in the same period and each column is months since acquisition, shaded by the percentage of that cohort still active. It shows retention as a function of tenure rather than of calendar date.
Why is cohort analysis better than an overall retention rate?
An overall rate blends customers of every tenure together, so growth in new signups can hide worsening retention. Cohorts hold tenure constant, which is what makes before-and-after comparison valid.
How do you read a cohort retention table?
Read down a column to compare different cohorts at the same age, and across a row to see a single cohort decay over time. Column comparison is what tells you whether retention is genuinely improving.
Related visualizations
See a cohort retention heatmap built on your customers
Book a demo and we will build this chart, and the rest of the library, against your own retention and revenue data.
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