Churn Risk Heatmap
Two dimensions, one intensity. Patterns you cannot miss. A heatmap lays values out on a grid and encodes magnitude as colour intensity. Rows are one dimension, columns another, and every cell is the value where they meet.
| Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Enterprise | 11 | 10 | 12 | 11 | 10 | 11 | 12 | 11 | 10 | 11 | 10 | 11 |
| Mid-market | 24 | 25 | 26 | 27 | 26 | 29 | 34 | 39 | 44 | 49 | 54 | 58 |
| SMB | 31 | 32 | 33 | 34 | 33 | 35 | 36 | 35 | 37 | 38 | 39 | 40 |
| Startup | 42 | 44 | 43 | 45 | 46 | 45 | 47 | 48 | 47 | 49 | 50 | 51 |
| Self-serve | 55 | 56 | 58 | 57 | 59 | 60 | 61 | 60 | 62 | 63 | 64 | 66 |
Churn risk in the mid-market row has risen every month since June, while every other segment is flat - the pattern is segment-specific, not seasonal.
Investigate what changed for mid-market accounts in June rather than treating this as a company-wide retention decline.
What is a churn risk heatmap?
A heatmap lays values out on a grid and encodes magnitude as colour intensity. Rows are one dimension, columns another, and every cell is the value where they meet.
Why is a churn risk heatmap useful?
It makes concentration obvious. A dense dark corner in a grid of a hundred cells is visible in well under a second - the same information as a table nobody would read.
How RetainCLM uses it
Put customer segments on the rows and months on the columns, and shade by average churn risk. Risk that is climbing in one segment shows up as a row that darkens from left to right, long before it lands in a churn number.
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
Scan rows for 'this group is different' and columns for 'something happened that month'. A single dark cell is an event; a dark row is a structural problem.
Limitations to keep in mind
Colour is read far less precisely than position, so a heatmap ranks and locates but does not quantify. Always print the values in the cells when the numbers matter, and never encode the whole message in hue alone.
Related concepts
A churn risk heatmap is most useful alongside customer segmentation, customer intelligence. 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 churn risk heatmap
What is a churn risk heatmap?
A grid that shows average predicted churn risk for each combination of two dimensions - typically customer segment and time period - with colour intensity standing in for the risk level.
Why use a heatmap instead of a table?
A heatmap and a table hold the same numbers, but the heatmap makes concentration and trend pre-attentive: you see where the problem is before you read anything.
What colour scale should a risk heatmap use?
A single-hue sequential scale when the measure simply goes from low to high. A diverging scale is only correct when there is a genuine neutral midpoint, which risk scores do not have.
Related visualizations
See a churn risk 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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