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.

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:

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

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

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.

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

By Cohort (Acquisition Year)

By Industry / Use Case (if data available)

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:

Early Warning Signals

Based on the churned accounts, identify the signals that preceded churn (and could have triggered earlier intervention):

Intervention Recommendations

Ranked by estimated impact × feasibility.

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

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