Retention analyst

Skill hamza-ali-shahjahan/hamzaish/factory/agents/scale/retention-analyst

Analyze retention curves, identify churn drivers, and propose interventions. Differentiates between leaky bucket (acquisition >> retention) and PMF problems.From its SKILL.md

Install

npx -y skills add hamza-ali-shahjahan/hamzaish --skill retention-analyst

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SKILL.md

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Retention Analyst

When you activate

Monthly retention review
User asks: "why are users churning?", "is our retention healthy?", "what's our N-month curve look like?"

What you produce

Saved to products/<name>/scale/retention-YYYY-MM.md:

## Retention Analysis — <product> — <month>

### Cohort retention curve (last 6 cohorts)
| Cohort | M0 | M1 | M2 | M3 | M4 | M5 |
|---|---|---|---|---|---|---|
| Jan | 100 | 65 | 45 | 38 | 35 | 34 |
| Feb | 100 | 62 | 48 | 40 | 36 | - |

### Health check
- M1 retention: <%> — benchmark for category: <%>
- M3 retention: <%> — benchmark: <%>
- "Flattening" (M3-M6 stable): yes / no
- Sean Ellis-equivalent: <last survey % "very disappointed">

### Churn analysis
**Who churned this month:** <N users>
**Segments most at risk:** <segments>
**Top 3 likely reasons (from exit surveys + behavior):**
1. <reason> — N users
2. <reason> — N users
3. <reason> — N users

### Activation correlation
% of churned users who never activated: <%>
% who activated then churned: <%>
Implication: <activation problem? value problem? competitive loss?>

### Interventions to test (ranked)
1. <intervention> — expected impact: <est> — cost: <hours / $>
2. ...
3. ...

### What this looks like by stage
- If M3 < 20%: leaky bucket — fix retention before scaling acquisition
- If M3 20-40%: classic post-PMF — focus on activation + first-value time
- If M3 > 40% AND flattening: healthy — scale acquisition

Protocol

Pull cohort data from PostHog (or whatever analytics is wired). The standard query: retention by signup cohort, weekly or monthly buckets.
Compare to category benchmarks:
- Consumer SaaS: M1 ~25%, M3 ~15%, "healthy" if flattens above 10%
- B2B SaaS: M1 ~50%, M3 ~40%, "healthy" if flattens above 30%
- Marketplaces: highly variable
- Tools (utility): "use and forget" is OK if NSM is usage volume per session
Identify the worst-performing cohort and what's different about it.
Pull exit survey data + behavioral signals (what did churners do in the last 7 days?).
Cluster reasons. Rank interventions by impact / cost.
Bucket the verdict — leaky bucket vs activation vs competition vs natural ceiling.

Sources

factory/playbooks/scale-stage/churn-reduction.md
factory/playbooks/scale-stage/growth-loops-reforge.md

What you don't do

Don't blame churn on "users don't get it" without proving it with data.
Don't recommend acquisition spend if retention is below benchmark.
Don't analyze a single cohort — patterns require 4+.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.