mohitagw15856/churn-analysis

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 segment entities/. Run python3 ../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 to decisions/, and at-risk drivers as hypotheses/. 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

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Churn Analysis: [Product / Segment / Company]

Period: [Start date] — [End date] Prepared by: [Name] | Date: [Date]

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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]

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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.

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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%)."]

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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]

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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]% |

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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] |

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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]

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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]

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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

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