How to build a SaaS retention curve from billing data (cohorts from first payment, one row per acquisition month), the three shapes (drop then flat, steady decline, cliff at renewal) and what each means, the customer curve against the revenue curve, and the four mistakes that produce a false curve.
Short answer
What should a SaaS retention curve look like?
Drop, then flat. A healthy curve loses the customers who never reached value in months one to three, then levels into a plateau that barely moves. The plateau height is the number that matters: above 70% to 80% for B2B is strong. A curve that keeps sloping is a product being rented rather than adopted; a curve with a cliff at month twelve is a renewal problem, not an onboarding one.
A churn rate is one number about one month. A retention curve is the whole story of a cohort, and it answers the question the churn rate cannot: when do customers leave, and do the ones who stay ever stop leaving? This is how to build the curve from billing data, the three shapes it takes and what each one means, the four mistakes that produce a false curve, and what to do about each shape.
The retention curve is the average of a cohort table, so build the table first. Rows are acquisition months. Columns are months since acquisition. Each cell is the share of the row's cohort still active at that column.
| Cohort | Size | M1 | M2 | M3 | M6 | M12 |
|---|---|---|---|---|---|---|
| Jan | 120 | 91% | 85% | 82% | 80% | 78% |
| Feb | 135 | 90% | 84% | 81% | 79% | |
| Mar | 110 | 88% | 81% | 78% | 77% | |
| Apr | 142 | 92% | 87% | 84% | ||
| Average | 90% | 84% | 81% | 79% | 78% |
The illustrative table above is the healthy shape: a ten-point drop in month one, a further six by month two, three more by month three, and then almost nothing. The average row is the retention curve. Read the diagonal too: it is the most recent month for every cohort, and a diagonal that is worse than the column above it means something changed recently for everyone at once, which is usually a price change, an outage, or a competitor.
Three rules for the cells. Start the cohort at first payment, not signup. Define active as paying, from the billing system, unless you are deliberately measuring usage. And do not fill a cell whose month has not fully elapsed; the blanks in the table are honest, a partial month is not. The SQL for this table, with the CTEs, is in the cohort analysis guide.
The healthy curve. Early-life churn removes the customers who never activated, and the survivors stay. Two numbers describe it: the depth of the drop (how many never reached value) and the height of the plateau (how many the product holds). The plateau is worth more than the drop. A curve that falls to 60% and stays there for three years is a better business than one that falls to 80% and keeps sliding.
The same share of remaining customers leaves every month, at month three and at month thirty. Tenure is not building attachment. This is the shape of products used for one project, of customers who were the wrong fit from the start, and of pricing that charges before the habit forms. It is also the shape that makes lifetime value a fiction, because LTV projections assume a plateau exists.
Retention holds for eleven months and drops at twelve or thirteen. That is the annual renewal, and the drop is customers who were auto-renewed once and not twice. These customers stayed a full year; onboarding did its job. The fix is a renewal process that starts ninety days before the date, not more onboarding.
Build the same table with MRR instead of account counts and you get the revenue retention curve. On most SaaS books it sits above the customer curve, because the small accounts churn first and the survivors expand. A revenue curve that climbs above 100% is a cohort whose expansion outgrew its churn, which is net revenue retention over 100% measured the right way, on a cohort rather than a blended month. A revenue curve that sits below the customer curve means the large accounts are the ones leaving, and that is a different emergency from the one the customer curve shows.
A SaaS retention curve plots the share of a signup cohort still paying at month 1, 2, 3 and onward. Each acquisition month is its own line, or the lines are averaged into one. The height of the curve at any month is cohort retention at that month; the shape of the curve tells you when customers leave and whether the ones who stay, stay for good.
A good SaaS retention curve drops in the first one to three months, then flattens into a near-horizontal line. The early drop is customers who never reached value; the flat part is the customers the product actually holds. For B2B SaaS, a curve that flattens above 70% to 80% is strong; for self-serve and SMB products the plateau is lower and the first-month drop steeper. The plateau matters more than the starting slope.
One row per acquisition month, one column per month since acquisition, each cell holding the share of that cohort still active. Start the cohort at first payment, not at signup, or the trial conversion rate blends into the curve. Define active as paying (billing) rather than logged in (product), unless you are deliberately measuring usage retention. The retention curve is the average down each column, and the diagonal of the table is the most recent month for every cohort.
A curve that declines at a steady rate every month means the same share of remaining customers leaves each period regardless of tenure, which means tenure is not building any attachment. Common causes are a product used for a single project rather than an ongoing process, a customer base that was never the right fit, or a pricing model that charges before the habit forms. It is the shape that makes lifetime-value projections fiction, because there is no plateau to project from.
The annual renewal. A curve that holds and then drops at a fixed month is customers who were auto-renewed once and chose not to renew twice, or who reviewed the contract at its first anniversary and left. The fix is a renewal process that starts ninety days before the date, not onboarding, because those customers stayed for a year.
Build both. The customer curve tells you when accounts leave; the revenue curve tells you whether the accounts that leave are the ones that matter. In most SaaS books the revenue curve sits above the customer curve, because small accounts churn first and expansion lifts the survivors. A revenue curve that ends above 100% is net revenue retention over 100% on that cohort, which is what a board wants to see.
The cohort table is illustrative, not a customer's data. The benchmark plateau (70% to 80% for B2B) is the widely quoted industry range, not a measurement Exeechain took. The four false-curve mistakes are the ones Exeechain's own churn accounting is built to avoid: involuntary churn is recorded in its own lane, and a reactivation is recorded as a win-back rather than as retention. The formula side of retention is in customer retention rate.
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