# Churn analysis Human Guide

## What This Is For
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. It gives the agent a clearer input/output frame for go-to-market work: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Churn analysis agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for churn analysis.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Churn analysis guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- **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
- 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]
- **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"]
- [Data gap 1 — e.g. "Exit survey response rate is only 30% — the reasons data may not be representative"]

## Decision Points And Nuance
The original skill emphasizes: Reads from / Writes to the Brain, Required Inputs, Churn Categories, Output Format, Headline Numbers, Churn Breakdown by Category, Churn Reasons — Avoidable Churn Only, Churn by Segment, By Tier, By Cohort (Acquisition Year).

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- | **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) |
- | Voluntary — avoidable | [N] | [X]% | £/$/€[X] |
- | Voluntary — unavoidable | [N] | [X]% | £/$/€[X] |
- **Avoidable churn as % of total churn:** [X]% — this is the number we can actually influence.
- Churn Reasons — Avoidable Churn Only
- | Reason | Count | % of avoidable churn | ARR lost | Representative quote |
- What We Don't Know (Data Gaps)

## Copy-And-Paste Prompt
```text
Use the Churn analysis human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the mohitagw15856/pm-claude-skills skill entry for `churn-analysis`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# 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`](../professional-brain/SKILL.md) (`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

---

# 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.