Cohort revenue curve
A cohort revenue curve takes one batch of customers, say everyone who signed up in the same month, and plots their revenue as a running total against time. How steeply the line climbs at any given month is how much that batch is paying right then, and where the slope steepens or flattens shows the batch adding revenue through expansion or losing it to churn.
Definition
A single cohort's revenue accumulated against its own elapsed time, plotted as one trajectory rather than summed with every other cohort active in a given calendar month.
Reading one cohort across its lifetime
The same revenue data can be read two ways. Stack every cohort's contribution for a given calendar month into one column, and the height of that column is the whole base's revenue that month — the aggregate, read vertically. Follow a single cohort across the columns instead, watching its contribution accumulate month over month from the month it formed, and you get that cohort's revenue curve, read horizontally.
Most SaaS dashboards default to the vertical reading, because aggregate MRR and aggregate retention are computable from data most teams already have. The cohort revenue curve needs cohort-tracked data, which is less often available out of the box. What it buys is the ability to ask cohort-anchored questions: how is this group of signups doing across its life, and how does it compare to the group before it.
The exact shape depends on choices that have to be named before a curve is fully specified: whether the cohort is defined at signup or at conversion, and the view-construction axes — calendar-month versus months-since-origin on the x-axis, smoothed MRR versus cash-in-hand for revenue, and so on. Activation work favors signup-defined cohorts, because that keeps the pre-conversion attrition that activation works on inside the cohort's trajectory.
What the slope and shape reveal
At any point on the curve, the slope is the cohort's revenue contribution that month — steep where the cohort is contributing heavily, flattening as members churn or downgrade. Cumulative revenue accumulates, so the curve rises; the signal is in how fast.
That makes the curve's shape a direct read on retention and expansion. A curve that climbs steeply and keeps climbing is a cohort holding its revenue and adding to it. A curve that climbs early and then goes nearly flat is a cohort that converted but didn't hold — revenue contribution decaying toward zero as the cohort thins. Two cohorts can post identical month-one revenue and diverge completely by month six, and the curve is where that divergence becomes legible. The decay concentrated in the first few months is starting churn; the gentler flattening after that is sustaining churn, and the curve separates them by where along its length the slope gives way.
Offsetting the curve by CAC
Shift the whole curve down by the cohort's acquisition cost and it starts below zero — the money's been spent, no revenue has landed yet — then climbs as revenue accrues. The month it crosses zero is that cohort's actual CAC payback, read as a visible crossover rather than computed as an aggregate ratio. Everything above the line after that is per-cohort profit. Because the offset is the cohort's own CAC and the climb is the cohort's own paid revenue, the crossing reflects what that specific cohort did, not a blended assumption smoothed across the whole base.
Example
A self-serve SaaS team plots each monthly signup cohort as cumulative revenue against months since signup. The February and May cohorts both start near $4k of month-one revenue, so on an aggregate dashboard they look interchangeable. On the curve they don't: February's trajectory keeps a steady upward slope through month six, while May's bends flat after month three. May converted just as well but is losing revenue contribution early — a starting-churn problem invisible in the month-one number.
Offsetting each curve by its cohort's CAC, February crosses zero at month nine and May still hasn't crossed by month six. The team now has a per-cohort payback read and a specific question to chase: what changed between February and May to flatten the curve.