What a churn audit delivers
A churn audit answers five critical questions:
- Who is churning? Which segments, cohorts, and customer profiles?
- When are they churning? At what point in the lifecycle?
- Why are they churning? What are the root causes?
- What did we miss? Were there signals we could have caught earlier?
- What do we do? What actions will reduce churn going forward?
Before you start
Gather your data sources
| Source | What it provides |
|---|---|
| CRM | Customer details, deal history, renewal dates, CSM notes |
| Billing system | Revenue history, upgrades, downgrades, cancellations |
| Product analytics | Usage data, feature adoption, login frequency |
| Support platform | Ticket volume, resolution times, escalations, CSAT |
| Customer feedback | NPS scores, survey responses, churn reasons |
| CS notes | QBR notes, call summaries, risk flags, health assessments |
Define your timeframe
Analyse 12-24 months of churn data. Less than 12 months won't give you enough pattern visibility. More than 24 months may include data from a fundamentally different stage of your company.
Assemble your team
A churn audit requires cross-functional input. Include:
- RevOps — data extraction and analysis
- Customer Success — qualitative context and customer relationships
- Product — usage data interpretation and product gap analysis
- Sales — deal quality assessment and ICP validation
Step 1: Quantify the churn
Metrics to calculate
- Gross Revenue Churn: Churned ARR / Beginning ARR
- Logo Churn: Churned Customers / Beginning Customers
- Net Revenue Retention (NRR): (Beginning ARR + Expansion - Contraction - Churn) / Beginning ARR
- Gross Revenue Retention (GRR): (Beginning ARR - Contraction - Churn) / Beginning ARR
Segment by
- Customer segment (Enterprise, Mid-Market, SMB)
- ACV band ($0-25K, $25-100K, $100K+)
- Cohort (by quarter signed)
- Industry
- Acquisition channel
- CSM
Look for segments where churn is significantly above or below average. These outliers tell you where to focus.
Step 2: Identify patterns
Timeline analysis
| Churn Timing | Likely Root Cause |
|---|---|
| 0-3 months | Onboarding failure, misset expectations, poor ICP fit |
| 3-6 months | Value not realized, adoption stalled, use case mismatch |
| 6-12 months | Usage declined, champion left, competitor emerged |
| 12+ months | Contract re-evaluation, budget cuts, strategic shift, vendor consolidation |
Renewal analysis
What percentage of customers who enter a renewal cycle actually churn? How many downgrade? How does this vary by segment? Track renewal outcomes over time to identify whether the problem is getting better or worse.
Contraction analysis
Contraction often precedes full churn. Customers who downgrade once are significantly more likely to churn at next renewal. Track the churn rate of previously-contracted accounts separately.
Step 3: Diagnose root causes
Categorize stated reasons
- Price/budget — customer can't justify the cost
- Product gaps — missing features or capabilities
- Implementation failure — never fully deployed or adopted
- Poor support — unresolved issues eroded trust
- Champion loss — key advocate left the organization
- Competitive displacement — switched to an alternative
- Business closure/acquisition — company no longer exists as customer
Validate with behavioral data
Stated reasons don't always match reality. Cross-reference with behavioral signals:
| Stated Reason | Behavioral Validation |
|---|---|
| "Too expensive" | Was usage actually declining? Were they using key features? |
| "Missing features" | Did they submit feature requests? Were workarounds available? |
| "Poor support" | What was their ticket volume, resolution time, CSAT? |
| "Switched to competitor" | When did evaluation start? What triggered it? |
Build the root cause breakdown
| Root Cause | % of Churned ARR | Preventable? | Priority |
|---|---|---|---|
| Poor ICP fit | 25% | At point of sale | High |
| Onboarding failure | 20% | Yes | High |
| Product gaps | 20% | With roadmap investment | Medium |
| Champion loss | 15% | With multi-threading | Medium |
| Competitive displacement | 10% | Partially | Medium |
| Business closure/M&A | 10% | No | Low |
Step 4: Analyse the misses
Audit leading indicators
For each churned account, look back 90 days before the churn event. What signals were present?
| Signal | Present 90 days before churn? |
|---|---|
| Usage decline (>20% drop) | Check product analytics |
| Support ticket spike | Check support platform |
| NPS/CSAT decline | Check survey data |
| Champion role change | Check CRM contacts |
| Missed QBR/check-in | Check CS calendar |
| Login frequency drop | Check product analytics |
| Feature adoption stall | Check product analytics |
| Payment issues | Check billing system |
Calculate predictability
- >70% of churns had visible signals: Your data is good; you need better monitoring and response processes.
- 40-70% had visible signals: Some gaps in data collection; improve instrumentation and signal coverage.
- <40% had visible signals: Major gaps in visibility; invest in data infrastructure before process changes.
Identify gaps
For churns without visible signals, ask: What data would we have needed? Is it available but not tracked? Does it require a new data source? This tells you where to invest in instrumentation.
Step 5: Build the action plan
Prioritization framework
Score each potential action on three dimensions:
- ARR impact (40% weight): How much churned ARR would this address?
- Feasibility (30% weight): How quickly and easily can this be implemented?
- Confidence (30% weight): How confident are we that this will work?
Example action plan
| Action | Root Cause Addressed | ARR Impact | Timeline | Owner |
|---|---|---|---|---|
| Implement ICP scoring in sales process | Poor ICP fit | $125K | 30 days | Sales Ops |
| Redesign onboarding for time-to-value | Onboarding failure | $100K | 60 days | CS Leadership |
| Build automated health score alerts | Late detection | $75K | 45 days | RevOps |
| Launch champion tracking program | Champion loss | $75K | 30 days | CS |
| Prioritize top 3 feature gaps | Product gaps | $100K | 90 days | Product |
Set targets
| Metric | Current | Target | Timeline |
|---|---|---|---|
| Gross Revenue Retention | 88% | 92% | 12 months |
| Logo Retention | 82% | 88% | 12 months |
| At-risk detection rate | 45% | 75% | 6 months |
| Time-to-value (days) | 45 | 21 | 6 months |
| Save rate (at-risk accounts) | 20% | 40% | 9 months |
The audit cadence
| Cadence | Activity |
|---|---|
| Weekly | Review at-risk accounts, track leading indicators, execute save plays |
| Monthly | Analyse churn/contraction events, update root cause breakdown, assess action plan progress |
| Quarterly | Full cohort analysis, segment deep-dives, action plan refresh, target assessment |
| Annually | Complete churn audit refresh, strategy review, investment planning |
The deliverable
Your churn audit should produce six outputs:
- Churn summary — headline metrics, trends, and segment breakdowns
- Pattern analysis — when churn happens, who it affects, and how it manifests
- Root cause breakdown — weighted analysis of why customers leave
- Predictability assessment — how much churn was detectable in advance and what signals matter
- Action plan — prioritized initiatives with owners, timelines, and expected impact
- Targets — specific, measurable goals with timeframes for accountability
Get a complete view of your churn risk — account by account.
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