Cohort Retention Calculator
Visualise D1, D7, D30, D90 retention curves
Enter D0 cohort size and how many users came back on D1, D7, D30, D90 — get retention percentages, a visual decay curve, and benchmarks against best-in-class apps.
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What is a Cohort Retention Calculator?
Cohort retention is the truest test of product-market fit. Acquisition can be bought, but retention must be earned. The standard checkpoints — D1 (next day), D7 (one week), D30 (one month), D90 (three months) — together draw a decay curve that tells you whether your product is sticky enough to compound.
This calculator takes D0 cohort size plus the number of users who returned on each checkpoint. It returns retention percentages and a visual decay curve, with overlays for benchmarks: consumer social (WhatsApp D30 ~75%, Instagram ~60%), SaaS productivity (~30-50% D30), and the median product (~13% D30).
Use it monthly for cohort-over-cohort comparison, after major releases (did this version retain better?), or to settle internal debates about "are we growing or churning?". A flat decay curve at D30 (a "smile" returning to higher retention later) is the gold-standard PMF signal.
Why use this Cohort Retention Calculator
Built for Indians, by Indians. Every number, every formula, every slab — tuned to FY 2026-27 reality.
Decay curve visual
See your retention shape — falling fast, leveling, smiling — at a glance.
Best-in-class benchmarks
WhatsApp, Instagram, Spotify, Slack overlays for context.
Cohort comparison
Run two cohorts side-by-side to see if a release improved retention.
Browser-only
Cohort numbers never leave your machine.
Using the Cohort Retention Calculator in 4 steps
No onboarding, no signup. Answer three fields and the numbers update live.
Pick a cohort
Users who signed up in a specific week or month. Cohort size = D0 active users.
Pull return data
From your analytics: how many of the original cohort were active on D1, D7, D30, D90?
Enter and plot
Calculator returns percentages, plots the decay curve, overlays benchmark.
Compare cohorts
Pick another cohort (e.g. post-feature launch) and overlay to see if retention improved.
Tips to get the most out of it
D30 is the most diagnostic checkpoint. If D30 is below 15%, you don't have product-market fit yet — focus on activation and core-loop usage, not on growth spend.
A "retention smile" — drop, plateau, slight rise after D60 — is the holy grail. It means power users emerge and pull metrics up.
Always use cohorts of consistent size. Comparing a 50-user cohort to a 5,000-user cohort is statistically dishonest.
Define "active" precisely. For SaaS, often "performed core action ≥1 in the day". For social, "opened the app". The definition affects retention massively — be consistent.
Real-world scenarios
How Indians actually use this calculator — concrete inputs, concrete outcomes.
Consumer app launch
New consumer app cohort 10K. D1 35%, D7 18%, D30 9%, D90 4%. Below median product. Sprint focuses on D1 retention — adding push-notification onboarding sequence. Next cohort D1 = 52%.
B2B SaaS retention
B2B SaaS D0 200, D30 85 (42%), D90 72 (36%). Above SaaS median, healthy. Replicate the activation playbook to next 5 cohorts.
Feature-launch impact
Pre-launch cohort D30 = 22%. Post-launch cohort (with new onboarding) D30 = 31%. 9-pp lift = 41% relative increase. Validates the launch invested in onboarding.
Frequently Asked Questions
Still have a question? Our team replies within a business day.
They are the standard checkpoints used across consumer and SaaS. D1 = activation health. D7 = early stickiness. D30 = monthly engagement. D90 = sustained engagement. Other days (D14, D60) are useful but less benchmarked.
For most categories, yes. Median products land 10-15% D30; great consumer apps 30-60%; great SaaS 30-50%. 5% means most users churn quickly — investigate activation funnel.
Weekly cohorts give cleaner comparison. Rolling cohorts (e.g. "all users from 30-60 days ago") smooth noise but blur week-on-week launch effects. Weekly is the standard.
Churn = % who don't return. Retention = % who do. They are complements (1 = retention + churn) but retention curves show the shape over time, while churn is usually a single-period number.
Yes — D180 and D365 are common for SaaS. This tool focuses on the first 90 days (where most product decisions happen). For longer-term retention, use a dedicated cohort analytics tool.
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