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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 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