Churn Analysis Template
A structured churn analysis framework for SaaS PMs. Covers churn rate calculation, voluntary vs involuntary split, cohort analysis, exit interview synthesis, root cause identification, and a prioritised retention action plan. Free to copy, download, and use. No signup required.
# Churn Analysis Template **Product:** [Name] **PM:** [Name] **Period analysed:** [e.g. Q1 2025 / Last 90 days] **Date:** [Date] --- ## 1. Churn rate summary **Definitions used (fill in exactly one per row):** | Metric | Formula | Value | |---|---|---| | Monthly user churn rate | Churned users / Starting users × 100 | % | | Monthly revenue churn rate (MRR churn) | Churned MRR / Starting MRR × 100 | % | | Net MRR churn (accounts for expansion) | (Churned MRR − Expansion MRR) / Starting MRR × 100 | % | | Annual churn rate (approximation) | 1 − (1 − monthly rate)^12 × 100 | % | **Benchmark comparison:** | Stage | Acceptable monthly MRR churn | Your rate | Status | |---|---|---|---| | Early stage (< ₹50L ARR) | < 5% | % | 🟢 / 🟡 / 🔴 | | Growth stage (₹50L–₹5Cr ARR) | < 3% | % | | | Scale (> ₹5Cr ARR) | < 2% | % | | --- ## 2. Voluntary vs involuntary churn | Type | Definition | Count | % of total churn | MRR lost | |---|---|---|---|---| | **Voluntary** | User actively cancelled | | % | ₹ | | **Involuntary** | Payment failed, card expired, UPI mandate cancelled | | % | ₹ | | **Total** | | | 100% | ₹ | **Involuntary churn recovery:** - Payment retries attempted: [N] times over [N] days - Recovery rate (failed → recovered): [%] - MRR recovered via dunning: ₹[Amount] - Opportunity: [% of involuntary churn that could be recovered with better retry logic] *Involuntary churn in India is higher than global benchmarks due to UPI mandate failures and card expiry patterns. A retry + grace period flow typically recovers 20–40% of failed payments.* --- ## 3. Churn by segment | Segment | Users at start | Churned | Churn rate | MRR churned | |---|---|---|---|---| | Free users | | | % | ₹0 | | [Tier 1 — e.g. Pro] | | | % | ₹ | | [Tier 2 — e.g. Team] | | | % | ₹ | | By tenure: < 3 months | | | % | ₹ | | By tenure: 3–12 months | | | % | ₹ | | By tenure: > 12 months | | | % | ₹ | | By acquisition channel: Organic | | | % | ₹ | | By acquisition channel: Paid | | | % | ₹ | **Key insight from segmentation:** [Which segment has the highest churn? What does that tell you about product-market fit for that segment?] --- ## 4. Cohort churn analysis Track what % of each monthly cohort is still active N months later. | Cohort | Month 1 | Month 2 | Month 3 | Month 6 | Month 12 | |---|---|---|---|---|---| | [Month 1 cohort] | 100% | % | % | % | % | | [Month 2 cohort] | 100% | % | % | % | % | | [Month 3 cohort] | 100% | % | % | % | % | **Retention curve shape:** [ ] Continuously declining (no habit formed — product/market fit problem) [ ] Steep drop in Month 1, then stabilising (activation problem) [ ] Gradual decline, stabilising around Month 3 (healthy — some long-term users) [ ] Flat after Month 2 (strong habit — focus on acquisition) **Month where churn is highest (greatest drop):** [Month] **Hypothesis for why:** [Activation failure / value not delivered / competitive switch / pricing] --- ## 5. Exit interview synthesis *Synthesise feedback from churned users: exit surveys, cancellation reasons, support tickets, and any direct interviews.* **Exit reason distribution:** | Reason | Count | % | Notes | |---|---|---|---| | Too expensive / didn't justify cost | | % | | | Missing feature / doesn't do what I need | | % | | | Switched to competitor | | % | Which competitor? | | No longer need the product | | % | Seasonal / role change | | Bad experience / bugs | | % | | | Didn't use it enough to justify paying | | % | Activation failure | | Other | | % | | **Top 3 feature gaps mentioned by churned users:** 1. [Feature] — mentioned by [N] churned users 2. [Feature] — mentioned by [N] churned users 3. [Feature] — mentioned by [N] churned users **Most common competitor switched to:** [Name — and why users chose it] --- ## 6. Root cause analysis For each major churn driver, identify the root cause (not the symptom). | Churn driver | Surface symptom | Root cause | Category | |---|---|---|---| | [Driver 1] | [e.g. "Too expensive"] | [e.g. User never completed activation — never saw ROI] | Activation | | [Driver 2] | [e.g. "Missing feature X"] | [e.g. Wrong ICP — feature X is core to a segment we can't serve] | Positioning | | [Driver 3] | [e.g. "Switched to competitor"] | [e.g. Competitor has better [specific capability]] | Product gap | **Root cause categories:** - **Activation failure** — user never experienced the core value proposition - **Product gap** — missing feature that competing products have - **ICP mismatch** — we acquired a customer segment we can't serve well - **Pricing** — price doesn't match the value delivered to this segment - **Support/experience** — bugs, slow responses, poor onboarding --- ## 7. Retention action plan | Root cause | Proposed fix | Effort | Impact | Owner | Target date | |---|---|---|---|---|---| | [Root cause 1] | [Specific action] | S/M/L | H/M/L | [Name] | [Date] | | [Root cause 2] | | | | | | | [Root cause 3] | | | | | | **Quick wins (< 1 week, high impact):** - [ ] [Action 1] - [ ] [Action 2] **30-day retention target:** [Current %] → [Target %] **MRR at risk if no action:** ₹[Current churn rate × MRR]
How to use this Churn Analysis template
Always split voluntary and involuntary churn before drawing any conclusions
Voluntary and involuntary churn require completely different fixes. Involuntary churn (payment failures) is an ops problem — fix it with retry logic, grace periods, and proactive card expiry warnings. Voluntary churn is a product problem. If you mix them, you'll build features to solve a payment infrastructure problem, or build payment infrastructure to solve a product problem. Calculate both separately from day one.
Look at cohort churn, not aggregate churn
Aggregate monthly churn is the average of many cohorts at different stages. A growing company can have a stable 5% monthly churn while each individual cohort is worsening — because new cohorts (which are larger) dilute the older, higher-churning cohorts. Cohort analysis shows the true picture. If Month-1 churn has increased over the past three cohorts, that's a signal that something in onboarding or ICP targeting has changed.
Don't trust exit survey data at face value — dig one level deeper
'Too expensive' is the most common exit survey response and usually the least honest. It is what users say when they can't articulate 'I never felt enough value to justify the cost.' Before treating it as a pricing problem, check whether those users completed activation. If 80% of 'too expensive' churners never generated a PRD (or equivalent core action), the problem is activation, not pricing. Fix the aha moment first.
Run win/loss interviews on churned users within 14 days of cancellation
Exit survey data is shallow — 1-2 clicks, no context. A 20-minute conversation with a churned user in the week after they cancel yields 10× more useful signal. Users are often willing to talk because they have a concrete opinion about why they left. Ask: 'What was the moment you decided to cancel?', 'What would have made you stay?', 'What did you switch to and why?' Record every call; synthesise monthly.
Want a Churn Analysis grounded in your actual customer data?
PMRead ingests your customer interviews, feedback, and Slack threads — and generates PRDs backed by real evidence, not guesses.
Frequently asked questions
What's the difference between MRR churn and user churn?
User churn counts the number of accounts lost. MRR churn counts the revenue lost. For products with multiple pricing tiers, these tell different stories. If you have high user churn on free users and low MRR churn, your product is losing non-paying users (acceptable) while retaining paying customers (healthy). If you have low user churn but high MRR churn, you're losing your high-value customers disproportionately — a serious problem. Always report both.
When should we prioritise churn reduction over acquisition?
When monthly churn exceeds 5% for B2B SaaS, acquisition investment is inefficient — you're filling a bucket with a large hole. The rule of thumb: if your churn-adjusted payback period exceeds 24 months (i.e. CAC / (ARPU × gross margin × (1 - monthly churn)^24 is negative), fix churn before scaling acquisition. Below 3% monthly churn for B2B SaaS, focus on acquisition.
How many exit interviews should we do per month?
At early stage (< 50 churned users/month): interview every churned paid user you can reach. At growth stage: interview a stratified sample — at least 5 from each major churn reason category. The minimum viable cadence is 5 interviews per month. Fewer than that and patterns don't emerge. More than 20 per month and the marginal new insight diminishes rapidly — focus on acting on what you've learned, not collecting more data.
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