Cohort Charts

Cohort charts

A cohort chart answers whether the business is getting better at keeping customers — which a single blended retention number cannot, because blended retention mixes a good recent cohort with a bad old one and reports the average.

Construction rules — get these wrong and the chart lies

Fixed denominator. Measure each cohort against its own starting population, tracked forward. Measuring against a moving denominator that includes new customers dilutes churn and makes retention look better than it is. This is the most common way cohort retention gets overstated.

One cohort assignment, forever. A customer belongs to the cohort of their first period and never moves. Reassigning on upgrade or reactivation destroys comparability.

Logo and revenue are different charts. Never mix them on one grid. Logo retention counts customers; revenue retention counts dollars and can exceed 100% through expansion. Show both — the divergence is the insight. High logo churn with low revenue churn means you are losing small customers, which is a go-to-market signal rather than a product crisis.

Define reactivation explicitly. A customer who lapses and returns is either new or resumed, and that is a decision you record, not one you leave implicit.

The triangle heatmap

Rows are cohorts, columns are periods since acquisition. The characteristic staircase comes from recent cohorts having fewer periods of history.

Cohort    n     M0    M1    M2    M3    M4    M5
2026-01  142   100%   88%   84%   81%   80%   79%
2026-02  156   100%   90%   86%   84%   83%
2026-03  201   100%   91%   88%   86%
2026-04  188   100%   89%   87%
2026-05  173   100%   92%
2026-06   94   100%

Non-negotiables:

• Show n, the cohort size. 120% retention on a 4-customer cohort is one upsell. Without n the reader cannot weight what they are seeing.
• Suppress cells under 5 entities. They are noise, and they can identify individual customers. Mark them as suppressed (— with a note), never leave them blank — blank reads as zero.
• Do not truncate the immature tail. Recent cohorts have fewer columns; that is honest. Dropping partial periods biases the curve upward, because the periods you drop are the ones with attrition still to come.
• Mark partial periods — a month-to-date column is not comparable to a complete one.
• Sequential colour ramp, not categorical, not diverging. Retention is ordered. Use diverging only when charting change versus a target, which has a real midpoint.
• Colour ramp needs enough lightness range to survive greyscale, and cells should carry their values as text anyway — the colour is the pattern, the number is the fact.

Reading it

• Along a row — how one cohort decays over its life
• Down a column — whether newer cohorts retain better at the same age. This is the improvement signal, and it is the reason to build the chart
• Along the diagonal — everyone's experience in the same calendar month. A bad diagonal means something happened then — an outage, a price change, a billing failure — affecting all cohorts at once regardless of age

That diagonal read is the one most people miss, and it is often where the actionable finding is.

Retention curves

The same data as lines: x is periods since acquisition, y is retention, one line per cohort.

Better than the heatmap for trend across cohorts; worse for spotting the calendar-month diagonal. Build both when the question is "are we improving".

• Colour by cohort recency with a sequential ramp — older cohorts lighter, newer darker. A categorical palette here throws away the ordering the reader needs.
• Label lines directly at their right end rather than using a legend of twelve entries.
• Overlay a median or a target curve for reference.
• Curves flatten — that plateau is the durable base. Where it settles matters more than the initial drop.
• Do not smooth between periods. Monthly retention is monthly (ui-antipatterns).

Layer cake

Stacked area of revenue by cohort over calendar time. Each band is a cohort; total height is total revenue.

The single best chart for showing that growth is compounding rather than churning through customers. Bands that persist and thicken mean expansion; bands that thin means the base is leaking and new sales are refilling a bucket.

• Order bands oldest at the bottom, newest on top — the growth story reads upward.
• Only the bottom band and the total are precisely comparable (a stacked-area limitation); do not ask the reader to compare middle bands.
• Cap at ~12 cohorts before grouping older ones into a single base band.

Cohort NRR

Net revenue retention for a cohort at a given age, charted as a line across cohorts.

• Plot at a fixed age (for example NRR at month 12) so cohorts are compared like for like. NRR at "latest available" compares a 3-month-old cohort with a 24-month-old one, which is meaningless.
• That constraint means the newest cohorts are absent from the chart. Say so rather than leaving a gap the reader interprets as a decline.
• Band the stage-appropriate benchmark, and label the stage (chart-annotation).

Common failures

Related skills

• chart-selection — when a cohort view is the right form
• svg-charting — building the grid and the ramp
• design-tokens — sequential ramp tokens
• artifact-accessibility — greyscale survival of the ramp, values in cells
• chart-annotation — stating the diagonal or column finding in the title