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churn-analysis
GitHub用于深入分析客户流失原因，区分可避免与不可避免流失。通过计算流失率、NRR及细分数据，生成包含预警信号和优先干预措施的结构化报告，以制定留存策略。
触发场景
调查客户流失原因
识别高风险客户群体
计算净收入留存率
构建留存干预计划
安装
npx skills add mohitagw15856/pm-claude-skills --skill churn-analysis -g -y
SKILL.md
Frontmatter
{
"name": "churn-analysis",
"description": "Produce a structured churn analysis that separates avoidable from unavoidable churn. Use when investigating why customers are leaving, identifying at-risk segments, calculating net revenue retention, or building a retention intervention plan. Produces a churn report with rate calculations, categorised reasons by avoidability, segment breakdown, timing analysis, early warning signals, and prioritised interventions ranked by estimated impact."
}
Churn Analysis Skill
Produce a structured churn analysis that goes beyond the headline rate — identifying why customers leave, which segments are most at risk, and what interventions will have the highest impact on retention.
Reads from / Writes to the Brain
If a professional-brain (brain/) exists, ground in it instead of re-asking for what you already know:
- Read first:
context.md(metric definitions — what "churn" means here),knowledge/, and related segmententities/. Runpython3 ../professional-brain/scripts/brain_query.py ./brain "churn"and carry each fact's provenance tag through. - 📥 Propose to the Brain: after producing, propose recording the headline retention finding to
knowledge/([data]), any retention decision todecisions/, and at-risk drivers ashypotheses/. Show them, get a yes, then write with../professional-brain/scripts/brain_write.py … --commit(append-only, dry-run by default).
Required Inputs
Ask for these if not already provided:
- Time period being analysed (e.g. Q1, last 12 months)
- Total customers at start of period and customers churned
- ARR or revenue lost to churn
- Churn reasons data — exit survey results, CSM notes, support data, or sales loss reasons
- Customer segments — by tier, industry, cohort, or product line
- Current retention rate if known
- Any recent changes — pricing, product, support model — that may have affected churn
Churn Categories
Always classify churn before analysing it:
| Category | Definition |
|---|---|
| Voluntary — avoidable | Customer left due to a problem we could have addressed (product gaps, poor onboarding, relationship failures) |
| Voluntary — unavoidable | Customer left for reasons outside our control (budget cuts, acquisition, company shutdown) |
| Involuntary | Payment failure, contract non-renewal by mistake, admin error |
The interventions for each category are different. Conflating them leads to wrong conclusions.
Output Format
Churn Analysis: [Product / Segment / Company]
Period: [Start date] — [End date] Prepared by: [Name] | Date: [Date]
Headline Numbers
| Metric | Value |
|---|---|
| Customers at start of period | [N] |
| Customers churned | [N] |
| Customer churn rate | [X]% |
| ARR at start of period | £/$/€[X] |
| ARR lost to churn | £/$/€[X] |
| Revenue churn rate (gross) | [X]% |
| ARR from expansions (same period) | £/$/€[X] |
| Net revenue retention (NRR) | [X]% |
Benchmark context:
- Customer churn rate: [X]% vs. industry benchmark [Y]% — [above / below / in line]
- NRR: [X]% — [What this means: above 100% = expansion offsets churn; below 100% = shrinking base]
Churn Breakdown by Category
| Category | Customers | % of churn | ARR lost |
|---|---|---|---|
| Voluntary — avoidable | [N] | [X]% | £/$/€[X] |
| Voluntary — unavoidable | [N] | [X]% | £/$/€[X] |
| Involuntary | [N] | [X]% | £/$/€[X] |
| Total | [N] | 100% | £/$/€[X] |
Avoidable churn as % of total churn: [X]% — this is the number we can actually influence.
Churn Reasons — Avoidable Churn Only
Rank by frequency. Include ARR weight where data allows.
| Reason | Count | % of avoidable churn | ARR lost | Representative quote |
|---|---|---|---|---|
| [Reason 1 — e.g. "Product missing key feature"] | [N] | [X]% | £/$/€[X] | "[Quote]" |
| [Reason 2] | [N] | [X]% | £/$/€[X] | "[Quote]" |
| [Reason 3] | [N] | [X]% | £/$/€[X] | "[Quote]" |
| [Reason 4] | [N] | [X]% | £/$/€[X] | "[Quote]" |
| Other | [N] | [X]% | £/$/€[X] | — |
Theme synthesis: [2–3 sentences grouping the top reasons into 2–3 themes. E.g. "The top three reasons cluster around two themes: product gaps in [area] (affecting X% of avoidable churn) and onboarding failures where customers never achieved value (Y%)."]
Churn by Segment
Identify which segments over- or under-index for churn.
By Tier
| Tier | Churn rate | vs. Overall | Notes |
|---|---|---|---|
| Enterprise | [X]% | +/-[X]pp | |
| Mid-Market | [X]% | +/-[X]pp | |
| SMB | [X]% | +/-[X]pp |
By Cohort (Acquisition Year)
| Cohort | Churn rate | Notes |
|---|---|---|
| [Year 1] | [X]% | |
| [Year 2] | [X]% | |
| [Year 3] | [X]% |
By Industry / Use Case (if data available)
| Segment | Churn rate | Notes |
|---|---|---|
| [Segment 1] | [X]% | |
| [Segment 2] | [X]% |
Key pattern: [Which segment has the highest churn rate and what likely explains it]
Timing Analysis
- Average contract length before churn: [X months]
- Highest-risk moment: [e.g. "Month 3 — when trial value has worn off but full adoption hasn't happened"]
- Churn timing distribution:
| When churn occurred | % of churned accounts |
|---|---|
| 0–3 months | [X]% |
| 3–6 months | [X]% |
| 6–12 months | [X]% |
| 12+ months | [X]% |
Early Warning Signals
Based on the churned accounts, identify the signals that preceded churn (and could have triggered earlier intervention):
| Signal | Lead time before churn | How to detect |
|---|---|---|
| [Signal 1 — e.g. "DAU/MAU dropped below 15%"] | [~X weeks] | [Usage dashboard / alert] |
| [Signal 2 — e.g. "No QBR in 90+ days"] | [~X weeks] | [CRM flag] |
| [Signal 3 — e.g. "Champion left the account"] | [~X weeks] | [LinkedIn alert / CSM tracking] |
| [Signal 4] | [~X weeks] | [Detection method] |
Intervention Recommendations
Ranked by estimated impact × feasibility.
| Intervention | Addresses | Est. churn reduction | Effort | Owner |
|---|---|---|---|---|
| [Intervention 1 — e.g. "Improve onboarding for [segment] with dedicated 30-day check-in"] | [Reason 1] | [X accounts / £X ARR] | Low / Med / High | [Team] |
| [Intervention 2] | [Reason 2] | [X accounts / £X ARR] | Low / Med / High | [Team] |
| [Intervention 3] | [Reason 3] | [X accounts / £X ARR] | Low / Med / High | [Team] |
Priority call: [Which one intervention, if implemented this quarter, would have the biggest impact and why]
What We Don't Know (Data Gaps)
- [Data gap 1 — e.g. "Exit survey response rate is only 30% — the reasons data may not be representative"]
- [Data gap 2 — e.g. "No product usage data for SMB tier — can't confirm usage signal correlation"]
- [Data gap 3]
Anti-Patterns
- Do not mix avoidable and unavoidable churn in intervention plans — recommending product fixes for customers who churned due to company shutdown wastes resources
- Do not calculate churn rate using end-of-period customer count as the denominator — this understates churn; always divide churned customers by the starting cohort
- Do not rely solely on exit survey data for churn reasons — response rates are typically low and self-selection biases the sample toward customers who are engaged enough to complete a survey
- Do not recommend interventions without linking them to a specific churn reason — interventions disconnected from root causes will not move retention
- Do not report only gross revenue churn — without net revenue retention (NRR), a healthy-looking retention number can hide a shrinking revenue base
Deeper Materials
This skill ships with support files — use them when they are available:
references/avoidability-calls.md— Avoidable or Not? The Judgment Calls in Churn Classification. Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses.templates/churn-report.md— a fill-in version of the deliverable with the quality gates inline. Offer it when the user wants to work the document themselves rather than have it generated.
Quality Checks
- Churn rate is correctly calculated (churned ÷ starting cohort, not end-of-period total)
- Avoidable and unavoidable churn are separated — interventions target avoidable churn only
- Churn reasons are customer-reported, not internally assumed
- Segment analysis identifies which segments over-index — not just averages
- Early warning signals are specific and detectable, not generic ("low engagement")
- Interventions link directly to the top churn reasons — no recommendations without a root cause match
版本历史
- a38bc30 当前 2026-07-05 11:14
