SaaS Churn: Cohort vs Aggregate vs Net Dollar

Churn is a single word that hides at least three different calculations, each of which can be defended as correct in isolation and all of which produce different numbers from the same dataset. Reporting one without naming which is a common source of overstated pitch-deck metrics in B2B SaaS.

The piece below covers the three calculations in order of investor utility, the worked example that shows where they diverge, and the five mistakes that show up repeatedly when bootstrapped founders graduate to investor conversations.

Three numbers, three different stories

Aggregate churn looks at the whole customer base in a single window. Cohort churn slices the base by signup month and tracks each slice independently. Net dollar retention asks the dollar question: of the revenue you had a year ago from a fixed set of customers, how much do you still have today, including upgrades, downgrades, and cancellations.

The three numbers can move in opposite directions inside the same business. NDR can rise to 115% while logo churn deteriorates from 4% to 6% monthly, because expansion from the surviving cohort outpaces logo loss. A board that sees only NDR misses the customer-acquisition problem; a board that sees only logo churn misses the expansion engine. Both views matter, and they answer different questions.

1. Aggregate churn (and why it lies)

The standard aggregate formula:

Aggregate monthly churn = Customers lost in month / Customers at start of month

The structural problem is the denominator. A SaaS at 200 customers that adds 40 in month one and loses 8 by month-end reports 8 / 200 = 4% churn. The 8 lost customers, however, were not drawn evenly from the 200. They were drawn disproportionately from the most recent signups, which always exhibit higher month-one drop-off than mature cohorts.

Aggregate churn averages a high-churn early-cohort population with a low-churn mature-cohort population and produces a single number that does not describe either. It also depends on how the denominator treats customers who joined during the period: an average customer count includes people who had only weeks to leave. Flat aggregate churn during fast growth can therefore hide deteriorating early-cohort retention, with a worse new cohort and a steadier old one averaging out to the same number.

The Fader and Hardie probability-of-retention work shows that cohort retention curves follow predictable shapes well-fit by Beta-Geometric distributions, where month-one drop-off is consistently the steepest segment^[1]. Aggregating across vintages flattens the curve and obscures the most actionable signal in the data.

When aggregate churn is acceptable: stable customer base, low growth rate, no cohort-quality changes between periods. The conditions under which aggregate churn is acceptable are also the conditions under which the metric is uninformative.

2. Cohort churn

Cohort churn pins each signup month to its own retention curve and reports the curve shape, not a single point estimate.

Calculation procedure:

• Group customers by signup month (the cohort).
• For each cohort, count how many remain active in month 1, month 2, month 3, and so on.
• Express each as a percentage of the cohort starting size.
• Plot the curves on the same axis to compare cohort quality across time.

What the curve shape reveals: most cohorts lose customers fastest in the first months and then flatten, because the customers least suited to the product leave first^[1]. Self-serve SMB cohorts typically drop much further than mid-market cohorts with annual contracts, so compare each cohort with your own earlier cohorts rather than another company's curve. Sharp month-one drop is an onboarding problem; a steep month-three drop is a value-realization problem; a steady month-twelve decline is a price-vs-alternative problem.

Cohort churn rates feed the right LTV calculation. The CLV Calculator uses a single churn rate by design, so the input has to be the cohort-stabilized churn (typically the month-six-to-twelve average), not aggregate churn. Plugging a blended aggregate rate into an LTV formula produces a lifetime that no real cohort lives, too long for new customers and too short for mature ones.

The minimum honest cohort report has three vintages: the most recent fully-completed cohort, a six-month-old cohort, and a twelve-month-old cohort. Three points define a curve well enough to spot improvement or deterioration trends.

3. Net dollar retention

NDR is the dollar version of cohort retention with expansion added back. The formula:

NDR = (Starting MRR + Expansion − Contraction − Churn) / Starting MRR

The starting MRR is the recurring revenue from a fixed cohort of customers, measured at a starting date. Twelve months later, the same cohort produces some new MRR figure: some customers churned (their MRR is gone), some downgraded (contraction), some upgraded or added seats (expansion). NDR is the ratio of ending to starting MRR for that fixed cohort.

NDR above 100% means the cohort is generating more revenue today than it was a year ago, even after losing some customers entirely. Self-serve SMB products usually find 100% harder to reach than seat- or usage-priced products sold to growing companies, because their customers have less room to expand.

Why NDR is the investor-facing number: it captures the compounding revenue engine. A SaaS at 110% NDR doubles its starting cohort revenue every 7.3 years with zero new customer acquisition. That is the specific dynamic that turns a SaaS from a high-CAC growth-at-all-costs business into a self-funding compounding asset, and the only metric that exposes it is NDR.

The expansion side of NDR is where the CAC math reverses sign: dollars spent on customer success and onboarding can flow into expansion revenue from customers you have already paid to acquire, so measure their payback the same way you measure new-acquisition CAC payback.

Logo churn vs revenue churn

Logo churn counts customers. Revenue churn counts dollars. The two diverge whenever customer ARPU varies, which is most of the time.

A SaaS with 100 customers averaging $50 ARPU loses 5 customers in a month: 5% logo churn. If the 5 lost customers were on the $50 plan, revenue churn is also 5%. If three of them were on a $200 enterprise plan and two were on the $20 starter plan, revenue churn is ($600 + $40) / $5,000 = 12.8%. Same logo churn, very different revenue impact.

Investors evaluating mid-market and enterprise SaaS focus on revenue (gross dollar) churn because customer count is a less faithful indicator of business health when ARPU is bimodal. Investors evaluating SMB self-serve focus on logo churn because ARPU is tighter and customer count is the better proxy for product-market fit.

Honest reporting always shows both: logo churn for product health, revenue churn for financial health, NDR for the full revenue cohort picture.

One additional dimension worth tracking explicitly: gross dollar retention (GDR), which is NDR with expansion stripped out. The formula:

GDR = (Starting MRR − Contraction − Churn) / Starting MRR

GDR has a hard ceiling at 100% (you cannot retain more than what you started with) and represents the floor of revenue durability. NDR can mask weak GDR if expansion is strong: a SaaS at 110% NDR with 78% GDR has a 32-point expansion engine offsetting a structurally leaky base. The same SaaS at 110% NDR with 95% GDR has a healthy base with moderate expansion. Same headline, very different operational reality, which is why GDR belongs next to NDR in any investor report.

Worked example: same data, three results

One product, twelve months of data, three churn calculations. A B2B productivity SaaS at $50k starting MRR, 250 customers averaging $200 ARPU.

Starting state (Jan 1):
  Customers      250
  MRR            $50,000
  Avg ARPU       $200

Activity over 12 months:
  New customers gained        +180  (avg ARPU $180, joining evenly)
  Jan 1 customers churned     -55   (lost $11,000 MRR)
  New customers churned       -30   (about 3% a month on the books)
  Jan 1 customers downgraded   15   (lost $9,000 MRR)
  Jan 1 customers expanded     60   (gained $14,400 MRR)

Ending state (Dec 31):
  Customers      345
  MRR            $71,400  ($44,400 Jan 1 cohort + 150 × $180)

CALCULATION 1: Aggregate churn (lost / average customers)
  Customers lost: 55 + 30 = 85
  Avg customers in window: (250 + 345) / 2 = 297.5
  Annual: 85 / 297.5 = 28.6%
  Monthly equivalent: 1 - (1 - 0.286)^(1/12) = 2.76%

CALCULATION 2: Cohort churn (Jan 1 cohort only)
  Starting cohort: 250 customers
  Still active Dec 31: 195 customers
  12-month logo retention: 195 / 250 = 78%
  Equivalent monthly churn: 1 - 0.78^(1/12) = 2.05%
  Annual: 22%

CALCULATION 3: Net Dollar Retention (Jan 1 cohort)
  Starting MRR (Jan 1 cohort): $50,000
  Lost to churn:                -$11,000
  Lost to contraction:           -$9,000
  Gained from expansion:        +$14,400
  Ending MRR (Jan 1 cohort):    $44,400
  NDR: $44,400 / $50,000 = 88.8%

Three numbers from one dataset: 2.76% a month aggregate, 2.05% for the January cohort, 88.8% NDR. The aggregate sits above the mature cohort's rate because the base now includes new customers, who leave faster in their first months, and it would drop if growth slowed even with no change in any cohort. The NDR shows the January cohort lost real revenue net of expansion, which means this business is acquiring its way out of a leaky-bucket problem.

An LTV built on the January cohort's 2.05% is $200 / 0.0205 = $9,756 of revenue per customer. One built on the blended 2.76% is $7,233, 26% lower, and it is not the new-customer figure either: new customers churn faster early, then slow down if they survive. Value each group on its own curve; the blended number fits neither.

Which number an investor actually looks at

Which number carries the most weight shifts with stage and segment:

• Pre-seed and seed (any segment): cohort retention curves matter more than NDR because expansion mechanics have not had time to compound. The question is whether recent cohorts flatten at a level the business can live on.
• Series A SMB SaaS: NDR and gross dollar retention side by side, because self-serve expansion is limited and the floor (GDR) decides whether growth compounds.
• Series A mid-market and enterprise: NDR, because expansion within accounts is the main growth engine and the clearest sign of product value.
• Late-stage and public: many public SaaS companies report a net retention rate in their quarterly results, and analysts compare it across peers.

The implicit ranking: NDR is the headline, gross dollar retention is the floor, cohort logo retention is the leading indicator. Aggregate churn rarely appears in investor materials because the metric is too easy to mask growth-driven dilution behind.

Annualizing churn correctly

The most common arithmetic mistake in churn reporting is converting between monthly and annual rates with linear math. Linear conversion overstates annual churn at every monthly rate above zero, with the error growing as the rate grows.

The correct conversion uses geometric compounding:

Annual churn = 1 − (1 − monthly churn)^12

The opposite direction:

Monthly churn = 1 − (1 − annual churn)^(1/12)

Numerical examples showing the error magnitude:

• Monthly churn 1.0%: linear annual = 12.0%, geometric annual = 11.4%. Error: 0.6 points.
• Monthly churn 2.0%: linear annual = 24.0%, geometric annual = 21.5%. Error: 2.5 points.
• Monthly churn 3.0%: linear annual = 36.0%, geometric annual = 30.6%. Error: 5.4 points.
• Monthly churn 5.0%: linear annual = 60.0%, geometric annual = 46.0%. Error: 14.0 points.

At low rates, the error is small enough to ignore. At rates above 3% monthly, the linear conversion is wrong by amounts that change LTV calculations meaningfully. The fix is one cell in the spreadsheet, and consistency across reporting periods.

The same compounding logic applies inside cohort retention: a "12-month retention of 78%" is equivalent to a 2.05% monthly geometric churn rate, which is the rate to plug into the LTV formula. Working from cohort retention curves to monthly churn is straightforward; working from a single point estimate of "monthly churn" to a 12-month retention number requires the geometric conversion that linear math gets wrong.

Common reporting mistakes

• Reporting one number without naming which. "Our churn is 3%" is unparseable until clarified as monthly or annual, logo or revenue, aggregate or cohort. The honest report names all four dimensions.
• Using aggregate churn in LTV calculations. A common source of misstated LTV in pitch decks. The fix is one cohort table away.
• Conflating gross retention with NDR. Gross dollar retention strips out expansion and reports only churn plus contraction. A business at 95% gross retention and 110% NDR has 15 percentage points of expansion compounding the cohort. Reporting them interchangeably collapses the most important diagnostic in the data.
• Excluding free-to-paid conversions from cohort definitions. A cohort defined as "first paid month" excludes the trial-period attrition that explains most month-one drop-off. Some founders report cohort retention from first paid month and a separate trial-conversion rate; others bundle them. Pick a convention and disclose it; the comparability collapses without it.

Picking the right churn calculation is not a stylistic choice. Each of the three metrics answers a different question, and the gap between them is where most LTV inflation hides. The cleanest reporting practice is showing all three side by side in any document that touches an investor, an acquirer, or a board, and naming the calculation explicitly every time the word "churn" appears.