Cohort Analysis Calculator

Frequently Asked Questions

What is cohort analysis?

Grouping customers by acquisition period and tracking metrics over time. Reveals patterns hidden by aggregate data: are recent cohorts retaining better? Is LTV growing? Standard view: month of acquisition × month of behavior, showing retention curves and revenue per cohort.

What metrics should I track by cohort?

Most common: retention rate, revenue per customer, churn, expansion. SaaS-specific: NRR, gross retention, average contract value. E-commerce: repeat purchase rate, average order value, days between orders. Track 6-24 months minimum to see real patterns.

How do I read a cohort retention chart?

Diagonal patterns reveal trends. Improving recent cohorts = product-market fit improving. Steep early drop-off = onboarding problem. Late drop-off = value realization issue. Flat curves = sticky product. Compare cohorts side-by-side to isolate cause and effect.

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This calculator provides estimates for informational purposes only. Business results vary by industry, market conditions, and execution. Not a substitute for professional business consulting, accounting, or legal advice. Consult qualified professionals before making business decisions.

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How This Calculator Works

A cohort is a group of customers acquired in the same period - usually the same month - and tracking how that single group retains over time reveals product health that blended company-wide metrics hide. This tool takes a starting cohort size, monthly ARPU (average revenue per user), gross margin, and the percentage of the cohort still active at months 1, 3, 6, 12 and 24. From those five anchor points it builds a smooth month-by-month retention curve by linear interpolation, extends it past month 24 at the last observed churn rate, then walks the curve out to 36 months multiplying surviving customers by ARPU and margin to produce total cohort revenue, gross profit, lifetime value per customer, and an estimated average customer lifetime.

Formula / Method

Between anchors the retained fraction is interpolated - e.g. for months 6–12, retention(m) = r6 + (r12 − r6) × ((m − 6) ÷ 6). Each month: active customers = cohort size × retention; revenue = customers × ARPU; gross profit = revenue × gross margin. LTV per customer = total 36-month gross profit ÷ cohort size. Average lifetime ≈ the sum of the retention curve (its integral), which equals 1 ÷ monthly churn for a constant-churn curve. Beyond month 24 the curve decays at the monthly churn implied by the month-12-to-24 slope.

Worked Example

Start with 1,000 customers, $50 ARPU, 75% gross margin, and retention of 85% (m1), 70% (m3), 60% (m6), 50% (m12), 40% (m24). At month 6, 600 customers generate 600 × $50 = $30,000 revenue and $22,500 gross profit. At month 12, 500 customers remain. Summing gross profit across all 36 months and dividing by the original 1,000 yields an LTV of roughly $500–$650 per customer, with an average lifetime in the low-to-mid 20s of months. A cohort that instead held 70% at month 12 would push LTV materially higher - small retention gains compound into large lifetime-value gains.

Common Mistakes

Confusing logo retention with revenue retention - this curve tracks customers; expansion revenue from survivors can lift dollar retention above 100% even as accounts churn.

Reading aggregate retention instead of per-cohort - a fast-growing user base hides that recent cohorts churn faster than older ones.

Entering month-1 retention as the churn rate - if 15% churn in month 1, retention is 85%, not 15%.

Assuming the curve keeps falling forever - healthy products reach a retention floor (the curve flattens) once the loyal core remains.

Ignoring gross margin - LTV is built on gross profit, not revenue; a 30%-margin business has a far lower LTV than the topline suggests.

Tips & FAQ

What does the curve shape tell me? A steep early drop signals an onboarding or activation problem; a curve that flattens after month 6 means you have found product-market fit and the survivors are sticky; a curve that never flattens points to a fundamental fit issue; a rising "smile" curve means expansion is outpacing churn - best-in-class. What is good month-12 retention? Consumer subscriptions 25–50%, SMB SaaS 50–70%, mid-market 70–85%, enterprise 90%+. How do I lift the curve? Cut time-to-value so users hit the "aha" moment in the first session, build weekly habit loops, personalise onboarding to reduce 30-day churn, and run proactive customer-success outreach to at-risk accounts. Why does retention beat acquisition? Improving month-12 retention from 50% to 60% can raise LTV more than doubling the top of the funnel - and it makes every future acquisition dollar more efficient.

Cohort Analysis Calculator

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