mohitagw15856
bfdbec17a3
Two star milestones shipped together: Customer Success bundle (pm-cs) — 250-star milestone: - cs-health-scorecard: weighted RAG health score across 5 dimensions with renewal forecast - qbr-deck: slide-by-slide QBR structure with value narrative and mutual commitments - cs-escalation-brief: 4-level escalation framework with root cause, impact, and decision required - churn-analysis: voluntary/unavoidable churn split, early warning signals, prioritised interventions Engineering expansion (pm-engineering) — 500-star milestone: - cicd-playbook: full pipeline playbook from build through post-deploy checks and rollback - slo-error-budget: SLI definitions, burn rate alerts, and error budget policy - developer-onboarding-doc: first-week guide covering architecture, setup, testing, and contacts - oncall-runbook: per-alert response procedures, escalation matrix, and handoff template Also: - Added pm-cs plugin to marketplace.json - Updated pm-engineering plugin.json to v3.0.0 (14 skills) - Updated marketplace.json to v10.0.0 (114 skills, 23 bundles, 16 professions) - README updated with new CS section, corrected skill numbering (106 → 114) - Added bug report link to Contributing section - Star milestones updated to show 250 and 500 as unlocked
180 lines
6.5 KiB
Markdown
180 lines
6.5 KiB
Markdown
---
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name: churn-analysis
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description: "Analyse customer churn for a product or cohort and produce a structured churn report. Use when asked to analyse churn, understand why customers are leaving, identify churn patterns, calculate churn rate, or build a churn reduction plan. Produces a churn analysis with rate calculations, categorised reasons, early warning signals, and prioritised interventions."
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---
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# Churn Analysis Skill
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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.
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## Required Inputs
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Ask for these if not already provided:
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- **Time period** being analysed (e.g. Q1, last 12 months)
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- **Total customers at start of period** and **customers churned**
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- **ARR or revenue lost** to churn
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- **Churn reasons data** — exit survey results, CSM notes, support data, or sales loss reasons
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- **Customer segments** — by tier, industry, cohort, or product line
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- **Current retention rate** if known
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- **Any recent changes** — pricing, product, support model — that may have affected churn
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## Churn Categories
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Always classify churn before analysing it:
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| Category | Definition |
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|---|---|
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| **Voluntary — avoidable** | Customer left due to a problem we could have addressed (product gaps, poor onboarding, relationship failures) |
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| **Voluntary — unavoidable** | Customer left for reasons outside our control (budget cuts, acquisition, company shutdown) |
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| **Involuntary** | Payment failure, contract non-renewal by mistake, admin error |
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The interventions for each category are different. Conflating them leads to wrong conclusions.
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## Output Format
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---
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# Churn Analysis: [Product / Segment / Company]
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**Period:** [Start date] — [End date]
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**Prepared by:** [Name] | **Date:** [Date]
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---
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## Headline Numbers
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| Metric | Value |
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|---|---|
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| Customers at start of period | [N] |
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| Customers churned | [N] |
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| **Customer churn rate** | **[X]%** |
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| ARR at start of period | £/$/€[X] |
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| ARR lost to churn | £/$/€[X] |
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| **Revenue churn rate (gross)** | **[X]%** |
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| ARR from expansions (same period) | £/$/€[X] |
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| **Net revenue retention (NRR)** | **[X]%** |
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**Benchmark context:**
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- Customer churn rate: [X]% vs. industry benchmark [Y]% — [above / below / in line]
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- NRR: [X]% — [What this means: above 100% = expansion offsets churn; below 100% = shrinking base]
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---
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## Churn Breakdown by Category
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| Category | Customers | % of churn | ARR lost |
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|---|---|---|---|
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| Voluntary — avoidable | [N] | [X]% | £/$/€[X] |
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| Voluntary — unavoidable | [N] | [X]% | £/$/€[X] |
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| Involuntary | [N] | [X]% | £/$/€[X] |
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| **Total** | **[N]** | **100%** | **£/$/€[X]** |
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**Avoidable churn as % of total churn:** [X]% — this is the number we can actually influence.
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---
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## Churn Reasons — Avoidable Churn Only
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Rank by frequency. Include ARR weight where data allows.
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| Reason | Count | % of avoidable churn | ARR lost | Representative quote |
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|---|---|---|---|---|
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| [Reason 1 — e.g. "Product missing key feature"] | [N] | [X]% | £/$/€[X] | "[Quote]" |
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| [Reason 2] | [N] | [X]% | £/$/€[X] | "[Quote]" |
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| [Reason 3] | [N] | [X]% | £/$/€[X] | "[Quote]" |
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| [Reason 4] | [N] | [X]% | £/$/€[X] | "[Quote]" |
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| Other | [N] | [X]% | £/$/€[X] | — |
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**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%)."]
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---
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## Churn by Segment
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Identify which segments over- or under-index for churn.
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### By Tier
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| Tier | Churn rate | vs. Overall | Notes |
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|---|---|---|---|
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| Enterprise | [X]% | +/-[X]pp | |
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| Mid-Market | [X]% | +/-[X]pp | |
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| SMB | [X]% | +/-[X]pp | |
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### By Cohort (Acquisition Year)
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| Cohort | Churn rate | Notes |
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|---|---|---|
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| [Year 1] | [X]% | |
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| [Year 2] | [X]% | |
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| [Year 3] | [X]% | |
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### By Industry / Use Case (if data available)
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| Segment | Churn rate | Notes |
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|---|---|---|
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| [Segment 1] | [X]% | |
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| [Segment 2] | [X]% | |
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**Key pattern:** [Which segment has the highest churn rate and what likely explains it]
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---
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## Timing Analysis
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- **Average contract length before churn:** [X months]
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- **Highest-risk moment:** [e.g. "Month 3 — when trial value has worn off but full adoption hasn't happened"]
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- **Churn timing distribution:**
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| When churn occurred | % of churned accounts |
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|---|---|
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| 0–3 months | [X]% |
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| 3–6 months | [X]% |
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| 6–12 months | [X]% |
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| 12+ months | [X]% |
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---
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## Early Warning Signals
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Based on the churned accounts, identify the signals that preceded churn (and could have triggered earlier intervention):
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| Signal | Lead time before churn | How to detect |
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|---|---|---|
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| [Signal 1 — e.g. "DAU/MAU dropped below 15%"] | [~X weeks] | [Usage dashboard / alert] |
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| [Signal 2 — e.g. "No QBR in 90+ days"] | [~X weeks] | [CRM flag] |
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| [Signal 3 — e.g. "Champion left the account"] | [~X weeks] | [LinkedIn alert / CSM tracking] |
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| [Signal 4] | [~X weeks] | [Detection method] |
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---
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## Intervention Recommendations
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Ranked by estimated impact × feasibility.
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| Intervention | Addresses | Est. churn reduction | Effort | Owner |
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|---|---|---|---|---|
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| [Intervention 1 — e.g. "Improve onboarding for [segment] with dedicated 30-day check-in"] | [Reason 1] | [X accounts / £X ARR] | Low / Med / High | [Team] |
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| [Intervention 2] | [Reason 2] | [X accounts / £X ARR] | Low / Med / High | [Team] |
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| [Intervention 3] | [Reason 3] | [X accounts / £X ARR] | Low / Med / High | [Team] |
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**Priority call:** [Which one intervention, if implemented this quarter, would have the biggest impact and why]
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---
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## What We Don't Know (Data Gaps)
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- [Data gap 1 — e.g. "Exit survey response rate is only 30% — the reasons data may not be representative"]
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- [Data gap 2 — e.g. "No product usage data for SMB tier — can't confirm usage signal correlation"]
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- [Data gap 3]
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---
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## Quality Checks
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- [ ] Churn rate is correctly calculated (churned ÷ starting cohort, not end-of-period total)
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- [ ] Avoidable and unavoidable churn are separated — interventions target avoidable churn only
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- [ ] Churn reasons are customer-reported, not internally assumed
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- [ ] Segment analysis identifies which segments over-index — not just averages
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- [ ] Early warning signals are specific and detectable, not generic ("low engagement")
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- [ ] Interventions link directly to the top churn reasons — no recommendations without a root cause match
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