Retention Analysis Skill

Diagnose why users leave, identify what keeps them, and recommend specific, testable interventions — not vague "improve onboarding" suggestions.

Retention Fundamentals

The retention curve has two components:

1. Steepness of initial drop (D1–D7) — onboarding problem
2. Long-term floor level — product-market fit indicator

A product with PMF has a retention curve that flattens. If it trends to zero, you have a PMF problem, not an onboarding problem. Name this distinction explicitly.

Retention Metrics Definitions

Retention Investigation Framework

Step 1: Segment the problem

Don't analyse "retention" — analyse retention for specific cohorts:

• New vs returning users
• Paid vs free
• Acquisition channel (organic vs paid vs referral)
• Onboarding path completed vs not
• Feature usage (power users vs lurkers)

Step 2: Find the inflection points

Where does the drop happen? D1? D7? Month 3?

• D1 drop → First session experience
• D7 drop → Habit loop not formed
• D30 drop → Value not delivered at depth
• Month 3+ drop → Boredom, competition, or lifecycle event

Step 3: Identify the "aha moment" correlation

Which early behaviour predicts long-term retention?

• Run correlation: users who did [X] in first 7 days vs 30-day retention
• Common patterns: connected an integration, invited a teammate, completed a core action N times

Step 4: Qualify the churn

Interview churned users — never skip this. Survey data alone is insufficient.

• "What was the trigger that led you to cancel/stop?"
• "What were you trying to accomplish that you couldn't?"
• "What would need to change for you to come back?"

Output Format

Retention Analysis — [Product/Segment] — [Date]

Question: [Specific retention question being answered] Period Analysed: [Date range] Segment: [Which users]

Current Retention Snapshot:

Retention Curve Shape: [Flattening / Still declining / Trending to zero] PMF Signal: [Strong / Weak / Absent — based on curve shape]

Root Cause Hypotheses:

"Aha Moment" Correlation: Users who [specific action] in first [N] days retain at [X%] vs [Y%] for those who don't.

Recommended Interventions:

Monitoring Plan:

• Metric to track: [X]
• Review cadence: [Weekly / Monthly]
• Alert threshold: [If X drops below Y, investigate immediately]

Required Inputs

Ask the user for these if not provided:

• Product and business model (SaaS / consumer app / marketplace / other)
• Current retention metrics (D1, D7, D30 if available)
• Segment to analyse (all users / paid / free / a specific cohort)
• Key question to answer (why is retention dropping? what drives retention?)
• Available data (analytics events, churn surveys, interview notes)

Quality Checks

• [ ] Retention curve shape is diagnosed (flattening vs trending to zero = PMF vs onboarding)
• [ ] Cohorts are segmented before analysis (not all users lumped together)
• [ ] "Aha moment" correlation is identified or flagged as unknown
• [ ] Interventions are specific (not "improve onboarding")
• [ ] Churned user interviews are recommended (not just data analysis)
• [ ] Monitoring plan includes an alert threshold

Anti-Patterns

• [ ] Do not recommend "improve onboarding" without specifying what specific step to change and why
• [ ] Do not analyse retention without segmenting by cohort — aggregate retention curves hide cohort-specific patterns
• [ ] Do not treat DAU/MAU below 5% as a retention problem — at that level, it is a product-market fit problem
• [ ] Do not skip qualitative research — churned user interviews reveal reasons that quantitative data cannot
• [ ] Do not set a monitoring alert without specifying the threshold that triggers it

Guidelines

• Never recommend "improve onboarding" without specifying what to change and why
• Benchmark against industry — consumer apps, SaaS, and marketplaces have very different retention norms
• If DAU/MAU is below 5%, that's a PMF conversation, not a retention tactics conversation
• Always recommend talking to churned users — no amount of data replaces understanding the reason