Cohort Retention Heatmap
Visualize retention curves, diagnose churn patterns, and calculate LTV from your actual data.
Mode A: type M1–M12 rates. Mode B: paste a CSV.
Retention Curve
LTV Reality Check
Enter ARPU to see your actual LTV vs the formula — the gap might surprise you.
What if M1 improved?
Curve Shape Analysis
Smile Curve — Healthy Flattening
Drop is steep M1→M3, then decelerates. Users who survive onboarding stay long-term. This is the textbook SaaS retention curve.
Nail M1 activation — this is your highest-leverage point. The long-tail users are already there.
Figma saw 71% of M12 retained users acquired in the first 3 months of their tenure.
Retention Advisor
Your product retains 40% of users at Month 6 — below Good benchmark for b2b-smb SaaS.
Nail M1 activation — this is your highest-leverage point. The long-tail users are already there.
Figma saw 71% of M12 retained users acquired in the first 3 months of their tenure.
Last reviewed: March 2026
What is Cohort Retention Analysis?
Cohort retention analysis is the practice of grouping users by their acquisition date and tracking what percentage remain active at each subsequent month. Unlike aggregate churn metrics (which show you a single number), cohort analysis reveals the full arc of user engagement: where the largest drops happen, how quickly the curve flattens, and whether retention is improving across successive cohorts.
The cohort retention heatmap visualizes this data as a matrix: rows are cohorts (grouped by acquisition month), columns are months since first acquisition, and cell color ranges from red (low retention) through yellow to green (high retention). The pattern that emerges — a steep drop in the first few months, then a flattening "smile" — is the signature of a product that successfully habits-forms its most engaged users.
How to Read a Retention Heatmap
Start with the leftmost column (M1) and scan downward across rows. If newer cohorts (bottom rows) show higher M1 values than older cohorts, your product is improving. If the values are flat or declining, investigate what changed in your acquisition channels or onboarding.
Next, scan horizontally across any single row. A healthy SaaS retention curve drops steeply from M0 to M2, then decelerates significantly. If the color stays dark red across all 12 columns, your product has no stable core — users never develop the habit that drives long-term retention.
Finally, look for diagonal patterns across the heatmap. Users acquired in the same calendar month may show similar behavior regardless of which cohort they're in — this is seasonality. The tool marks these cells with a subtle purple dashed border when detected.
SaaS Retention Benchmarks by Tier and Vertical
Retention benchmarks vary dramatically by business model. B2B Enterprise SaaS — with annual contracts and high switching costs — achieves world-class M12 retention of 75%+ (Salesforce, Workday). B2B SMB products target 60% at M12 (Figma, Notion, Slack). B2C consumer apps operate in a different universe: Duolingo and Spotify achieve ~25% M12 retention, which is considered world-class.
The vertical selector in the tool adjusts benchmark lines on the retention curve chart. Benchmarks are sourced from published S-1 filings, academic cohort studies, and investor reports. The "Good" tier reflects what a well-run company in each vertical achieves at scale. Use it to set a realistic near-term target before aiming for world-class.
Why LTV from a Retention Curve is More Accurate Than the 1/Churn Formula
The traditional LTV formula — LTV = ARPU / average_churn_rate — assumes every customer churns at the same constant rate throughout their entire lifetime. This is mathematically convenient but empirically wrong. Real SaaS retention curves are front-loaded: the steepest drop happens in the first 1–3 months as users who never activated leave. After Month 6, churn stabilizes at a much lower rate.
The LTV Reality Check card in this tool calculates two numbers side by side. Actual LTV sums ARPU × retention_rate for each month — capturing the real cash flow. Formula LTV uses 1/average_churn. For typical SaaS curves, the formula overstates LTV by 15–35%, which means founders who model unit economics using the formula are making acquisition budget decisions on inflated projections.
How to Improve SaaS Retention: The 7 Curve Patterns
Retention improvement starts with diagnosing which of the seven retention curve patterns applies to your product. The Smile Curve (healthy flattening after M3) calls for M1 activation investment. The Early Cliff (M1 below 50%) demands onboarding redesign. The Deep Churn Trap (M1 below 40% with continued decline) is a product-market fit signal — paid acquisition should pause until product fundamentals are addressed.
The Curve Shape Analysis section automatically detects your pattern and provides a concrete action. The What-If Simulator lets you model the LTV impact of improving M1 retention by specific percentage points — turning abstract advice into dollar-denominated impact before you commit engineering resources.
What is Net Revenue Retention (NRR) and How Does Retention Drive It?
NRR measures revenue retained from existing customers including expansions. User retention is its foundation — you cannot expand revenue from churned customers. A product with M6 retention of 70%+ has the substrate for NRR above 100%, because enough customers are active long enough to upgrade, expand seats, or increase usage.
The cohort retention heatmap pairs directly with the Churn & NRR Calculator and LTV:CAC Ratio Calculator. Where NRR shows aggregate retention health, the heatmap shows the lifecycle shape that produces it — giving you the diagnosis behind the number.