Free Cohort Retention Curve Generator

Three things to look at

The drop in months 1-3 (how aggressively customers churn early), the flattening point (where the curve levels off, which is where you reached PMF for that cohort), and the flat-floor level (how high the curve plateaus, which is the ceiling on long-term retention). A healthy SaaS curve drops fast in months 1-3, flattens around month 6, and holds 30 to 50 percent long-term. A struggling curve declines linearly past month 12 because customers never form habits.

The math, top to bottom

half_life      = month at which retention = 50%
                 (interpolate between input months)
avg_lifespan   ≈ sum(monthly_retention) for 0..N
                 (good approximation for typical curves)
ltv_multiplier = avg_lifespan
ltv            = arpu * gross_margin * avg_lifespan

The lifespan approximation gets less accurate as time horizons extend (you need to extrapolate the tail), but for 24-month cohorts it's accurate to within 5 percent. Long-tail customers (the ones who stayed 5+ years) drive enormous LTV that 24-month cohorts can't measure yet.

Realistic benchmarks by tier

• Enterprise SaaS: 12-month retention 90%+, half-life 24+ months, lifespan 60+ months
• Mid-market SaaS: 12-month retention 80-90%, half-life 12-18 months, lifespan 30-50 months
• SMB SaaS: 12-month retention 60-80%, half-life 4-8 months, lifespan 18-30 months
• Self-serve / consumer SaaS: 12-month retention 30-50%, half-life 2-4 months, lifespan 8-15 months
• Consumer subscription apps: 12-month retention 10-25%, half-life 1-2 months, lifespan 3-8 months
• Best-in-class SaaS at any tier: curve flattens above 50% by month 6 (truly sticky product)

Why the early-month drop is the most important number

Month 1 retention is the most diagnostic single number in a SaaS retention curve. It tells you whether the user activated. Month 1 below 50% means most signups never get to the value moment. Improvements in onboarding, activation, and time-to- first-value all show up first as month 1 retention lifts. Improvements anywhere else (support, expansion, upsell) compound on whatever month 1 number you have.

Cohort comparison: the right way

If your Q3 cohort has higher month-3 retention than your Q1 cohort, your retention work is paying off. If they're identical, nothing changed. If Q3 is worse, something regressed (a product change, a CAC mix shift, a quality of traffic decline). Cohort-on-cohort comparison is the closest thing to a real-time signal you can get for retention work, and it's why teams that take retention seriously plot every new cohort against the previous one.

Why retention compounds in LTV

Lifespan in the LTV formula is the sum of retention. So a 5 percentage point lift in month-12 retention (from 35% to 40%) doesn't just add 5% to LTV — it adds 5% retained forever, plus 5% more month-13 customers who can churn or renew, plus 5% more month-14 customers, etc. Small retention improvements compound into very large LTV improvements when the time horizon is long. This is why investors look at NRR/GRR before any other SaaS metric.

A worked example

Your cohort retains 70% in month 1, 55% in month 2, 48% in month 3, 42% in month 6, 40% in month 9, 38% in month 12, 35% in month 18, 32% in month 24. Sum across these ~26. Half-life is approximately 2.5 months (interpolating between m2 at 55% and m3 at 48%). Lifespan ~26 months. With $200/month ARPU and 80% gross margin, LTV = $200 × 0.80 × 26 = $4,160. Healthy mid-market SMB territory. Improve month-1 retention from 70 to 80 percent without changing later months and lifespan jumps to ~28 months, LTV to $4,480. A 14 percent point one improvement turned into 8 percent more LTV. Compound improvements is what retention work pays.