Acquisition Cohorts

The most common type. Group customers by when they first signed up or made their first purchase. Track their retention, revenue, and behaviour over subsequent weeks or months. Acquisition cohorts are ideal for measuring the impact of product changes, pricing changes, and acquisition channel quality over time.

Example: "All customers who signed up in March 2025" is an acquisition cohort. Comparing their Day 30 retention to the February 2025 cohort tells you whether your March product improvements actually worked.

Behavioural Cohorts

Group users by a specific action they took (or didn't take), regardless of when they signed up. Behavioural cohorts answer product questions: "Do users who complete onboarding in the first 7 days retain better than those who don't?" — this is a behavioural cohort analysis.

Example: "Users who connected at least one integration within 14 days of signup" vs "users who didn't" — compare 90-day retention across both groups to quantify the value of integration adoption. If the first group retains at 75% and the second at 30%, you've found your most important onboarding milestone.

When to Use Each

• Use acquisition cohorts for: measuring product improvement over time, evaluating channel quality, modelling LTV by vintage
• Use behavioural cohorts for: identifying "aha moments," optimising onboarding flows, proving feature value to product teams
• Use both together: acquisition cohort × channel source reveals which channels bring the highest-quality customers

Table Structure

Rows represent cohorts (e.g., "Jan 2025 cohort" = all customers acquired in January 2025). Columns represent time periods (Month 0, Month 1, Month 2...). Each cell contains the percentage of that cohort still active or retained at that time period. Month 0 is always 100% — it's the starting point.

What Good Looks Like

In a healthy B2B SaaS cohort table, you'll see:

• Month 1: 85–92% (small early drop from low-fit trials)
• Month 3: 75–85%
• Month 6: 70–80% (retention curve starting to flatten)
• Month 12: 65–75% (the curve should be nearly flat by now)

The critical signal is the flattening. If the curve drops steeply from Month 6 to Month 12, you have ongoing churn in your retained base — typically caused by annual contract renewals failing, which is a sales/success problem, not a product problem.

What Bad Looks Like

A steep, continuous decline with no flattening — particularly if each successive cohort retains worse than the prior one at the same time period — indicates either product-market fit issues or a deteriorating acquisition channel mix (e.g., aggressive paid acquisition bringing in lower-fit customers who churn faster).

MRR Cohorts

Instead of tracking user retention, track MRR retention per cohort. This reveals whether retained customers are expanding or contracting revenue over time. A cohort that retains 75% of customers but 100% of MRR has significant expansion happening from retained customers — an excellent signal. A cohort that retains 90% of customers but 70% of MRR has serious downgrade pressure.

Expansion vs Contraction by Cohort

Segment cohort revenue movements into: new MRR (not applicable for existing cohorts), expansion MRR (upsells, seat additions), contraction MRR (downgrades), and churned MRR (cancellations). Plot this as a waterfall for each cohort vintage. Cohorts from your best-performing acquisition channels or product vintages should show strong expansion curves offsetting contraction.

LTV Curves

Plot cumulative gross profit per customer by cohort vintage over 24–36 months. This is the single most powerful chart to show investors — it demonstrates that the business generates more lifetime value per customer than it costs to acquire them, with a clear payback crossover point. For India SaaS, the payback crossover should ideally occur before Month 18.

The Flattening Retention Curve Signal

The definitive product-market fit signal in cohort data is a retention curve that flattens and holds at a meaningful level. This means some portion of your user base has found genuine, ongoing value in your product — they are not churning, even over 12+ months. The absolute retention level matters less than the flattening itself.

A retention curve that flattens at 40% for a consumer app indicates strong PMF — 40% of acquired users become long-term users. A curve that flattens at 60% for a B2B SaaS indicates excellent PMF. A curve that never flattens — that continues declining toward zero — indicates the product does not have a retained core audience, regardless of how well it acquires users.

Using Behavioural Cohorts to Diagnose PMF

Segment your user base into "activated" (reached core value milestone) and "not activated" groups. Compare retention curves for each. If activated users retain at 70%+ and non-activated users retain at 15%, you have strong evidence that your product delivers real value — your problem is onboarding, not product-market fit. This distinction is critical for prioritisation.

India Startup Example

A Bangalore-based B2B HR SaaS found that customers who processed their first payroll within 14 days retained at 82% at Month 6. Customers who hadn't processed payroll by Day 14 retained at 23%. This single cohort finding redirected 60% of their engineering sprint toward onboarding flow — and monthly churn dropped from 4.2% to 1.8% over the following two quarters.

SaaS: Monthly Cohorts

Group by signup or first payment month. Track logo retention, MRR retention, and expansion MRR separately. Key insight: at what month does the logo retention curve flatten? This is your "stabilised customer" benchmark and inputs directly into LTV calculations. Review cohort data monthly in leadership meetings — a two-month lag in identifying cohort deterioration costs 3–4 months of recovery time.

D2C: First-Purchase Cohorts

Group customers by the month of their first purchase. Track 30-day, 60-day, and 90-day repeat purchase rates. Also track Average Order Value evolution by cohort — do customers increase AOV over time (premiumisation) or decrease it (deal-hunting)? For India D2C, festive season cohorts (October–November) often show different repeat patterns than non-festive cohorts due to purchase intent differences.

Marketplace: GMV Cohorts

For marketplaces, cohort GMV (not just retention) is the key metric. Group sellers or buyers by their first transaction month. Track monthly GMV contribution per cohort member over time. High-GMV cohorts from referral channels vs paid channels tell you where to invest acquisition spend. Also track active days per month per seller by cohort — declining activity predicts GMV contraction 60–90 days before it shows up in revenue.

Confusing User Cohorts with Revenue Cohorts

A user cohort tracks whether customers are active; a revenue cohort tracks how much they spend. These can diverge dramatically. A cohort may retain 80% of users but only 50% of revenue if high-value customers churn disproportionately. Always run both analyses. For investor reporting, revenue cohorts are more relevant; for product decisions, user cohorts are often more actionable.

Not Controlling for Channel Mix Changes

If you shift acquisition spend from SEO (high-intent, high-retention) to Instagram (lower-intent, lower-retention) during a growth push, your newer cohorts will appear to retain worse — but the product hasn't changed. Always segment cohort data by acquisition channel. Otherwise, you may misdiagnose a channel quality problem as a product problem and make the wrong investment.

Ignoring Small Cohort Sizes

Cohort data from months with fewer than 30–50 customers is statistically unreliable. Early-stage startups often over-interpret small cohort retention numbers. Aggregate your first 3–6 months of cohorts for meaningful analysis; only separate them once each cohort is large enough to produce stable percentages.

Measuring the Wrong Retention Event

Retention should be measured by the event that signals ongoing value delivery — not just any login. For a project management tool, "retention" should mean "created at least one task" not "opened the app." Define your retention event carefully, validated by correlation analysis: which actions in Month 1 correlate with being a retained customer at Month 6?

Corpfy's Cohort Analysis dashboard is an interactive retention heatmap — enter your monthly cohort retention data and the dashboard colour-codes every cell (green >80%, yellow 40–60%, red <20%) so you can see retention decay patterns at a glance. Best and worst cohorts are auto-detected and the average retention for any month column is calculated automatically.

What you get in the dashboard