Churn quietly compounds in the background while founders chase new signups. Plug in your starting customers, monthly churn, ARPA, and gross margin to see how fast a cohort decays, how much MRR you lose over a year, and what each customer is actually worth.
Two solo founders launch in the same month with 100 customers each at $29/mo. One has 5% monthly churn. The other has 7%. After 12 months, founder A still has 54 customers. Founder B has 42. That two-percentage-point gap costs founder B $348/mo in MRR by year-end — and it never recovers, because churn compounds. This calculator makes the compounding visible.
Methodology. Deterministic projections from a single churn rate applied uniformly to a starting cohort. Real cohorts have variance, vintage effects, and seasonality — treat the output as a directional model, not a forecast. How we research.
Starting customers is the size of the cohort you’re modeling. You can use your current paying customer count, a hypothetical launch cohort, or any round number to study the dynamics. The math behaves identically at 50, 100, or 10,000 — only the absolute numbers change.
Monthly churn rate is the percentage of paying customers who cancel each month. For B2B SaaS aimed at small businesses, monthly churn typically lands between 3% and 7%. Consumer SaaS skews higher, 5% to 10%. Enterprise sits at 1% to 2%. Five percent is a realistic default for solo-founder products. We unpack the definition in what is churn rate.
ARPA — average revenue per account — is what each paying customer brings in per month, blended across plans. If half your users pay $29 and half pay $49, ARPA is $39. What is MRR covers the upstream metric this rolls into.
Gross margin is the percentage of each subscription dollar that survives the cost of delivering the service: hosting, database, payments, transactional email, AI inference. A typical Next.js + Supabase + Stripe SaaS at low scale runs 80–90% — we walk the line items in SaaS cost at $1K MRR. AI-heavy products run 40–65%.
Four formulas drive this calculator:
These are simplifications. Real cohorts don’t churn uniformly — the first 30 days are usually the worst, then survivors stabilize. Real LTV factors in expansion revenue, contraction, and cohort vintage. But for a directional read on “how badly is churn eating my business,” the simple version is exactly the right tool.
Here’s the load-bearing observation: churn is multiplicative, not subtractive. A founder with 5% monthly churn doesn’t lose 60% of customers in a year (5% × 12). They lose 46% — because each month’s churn is applied to a smaller surviving base. That’s the good news. The bad news is the same mechanism makes it look manageable for the first three to four months, and then suddenly half your cohort is gone.
The difference between 5% and 7% looks small on a slide. Over 12 months, 5% leaves you with 54% of the cohort. Seven percent leaves 42%. That’s a 22% difference in survivors compounding forever. Across multiple cohorts, the gap widens. A product with 7% churn has to acquire 40% more customers just to keep up with what 5%-churn product does without breaking a sweat.
This is why churn shows up in SaaS metrics that matter as the highest-leverage number. Conversion-rate optimization moves a percent or two on top-of-funnel. Churn reduction moves a percent or two on the entire base, every month, forever.
At 30 paying customers, a single cancellation is 3.3% “monthly churn.” Two cancellations is 6.7%. The number swings wildly because the denominator is tiny. Founders read these noisy monthly numbers as signal and either panic (when one bad month lands) or get complacent (when one quiet month lands).
Two fixes. First: compute trailing-3-month or trailing-6-month churn instead of point-in-time monthly. The longer window smooths the noise. Second: separate involuntary churn (failed payments, expired cards) from voluntary churn (active cancellation). Involuntary is a Stripe configuration problem; voluntary is a product-fit problem. Lumping them together hides the actionable signal.
Below ~50 paying customers, monthly churn rate is barely meaningful. Track absolute cancellations and read the cancellation reasons individually. The statistics arrive once you cross ~100 paying customers.
Aggregate monthly churn is (cancellations this month) / (paying customers at start of month). It blends all customers together regardless of when they signed up. Cohort churn tracks a specific intake group — everyone who signed up in March 2026, say — and follows that group across time.
The gap matters because new signups have systematically higher churn than 12-month-old subscribers. Aggregate churn under-reports your real new-customer churn problem because seasoned survivors dilute the number. Two products with identical 5% aggregate churn might have wildly different cohort curves: one has 12% month-1 churn dropping to 2% by month 6, the other has flat 5% throughout. The first has a fit problem on intake. The second has a sustained value problem.
This calculator uses aggregate-style math (single rate applied uniformly) because it’s the simpler model and matches how most founders intuit the metric. For real diagnosis, build a cohort retention table in your billing data — the SaaS churn reduction playbook walks through how.
Three categories of intervention, in roughly the order of impact for a solo founder:
Onboarding. The largest single lever. Most churn happens in the first 30 days because users never reached the “aha” moment that justified the subscription. Fix the activation step that gets them there — even a one-time email sequence or a short Loom can move first-month retention 5–15 points.
Involuntary churn. Often 20–40% of total cancellations are failed payments. Stripe’s Smart Retries, dunning emails, and card-update prompts recover most of it. This is config, not product work. Cheap to fix.
Pricing fit. Customers churn when the price exceeds the value. If everyone churns after month 3, you might be priced too high for the value delivered — or the product doesn’t deepen enough over time to justify ongoing payment. Review your pricing playbook and customer counts at each tier to recalibrate.
Annual plans are a structural churn fix — they don’t reduce desire to cancel, but they convert monthly cancellation decisions into yearly ones, which most users defer indefinitely. Offer 2–3 months free for an annual upfront and you’ll move 20–40% of revenue to annual.
Workflow 1: stress-test churn. Hold ARPA and starting customers constant. Move churn from 5% to 7% and watch the year-12 cohort shrink. That’s your sensitivity to churn drift.
Workflow 2: LTV decisions. Compare LTV at your current churn vs. a target churn. The gap is what onboarding work is worth in dollars per customer — and dictates how much you can spend on customer acquisition.
Workflow 3: funnel sizing. Starting customers + churn tells you what your steady-state base will be if you stop acquiring. Many solo founders are surprised how much new acquisition just replaces churn rather than growing the base.
Churn is the silent compounder of SaaS. Two-point gaps in monthly churn become entire-cohort gaps within a year. The fix is rarely a single tactic — it’s a habit of measuring trailing windows, separating involuntary from voluntary, watching cohorts not aggregates, and treating onboarding as a continuous engineering project. Use the calculator to make the compounding visible; use the playbook to do something about it.
The stack, prompts, pricing, and mistakes to avoid — for solo founders building with AI.