retention-analysis

name	retention-analysis
description	Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.

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.

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Retention Metrics Definitions

Metric	Formula	What It Tells You
D1 Retention	Users who return on day 2 ÷ new users day 1	Quality of first experience
D7 Retention	Users active on day 8 ÷ users who joined 7 days ago	Early habit formation
D30 Retention	Users active on day 31 ÷ users who joined 30 days ago	Product-market fit signal
DAU/MAU Ratio	Daily active users ÷ monthly active users	Stickiness (>20% good, >50% excellent)
Churn Rate	Users lost in period ÷ users at start of period	Monthly or annual
Net Revenue Retention	MRR at end of period ÷ MRR at start (same cohort)	Revenue health including expansion

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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?"

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Output Format

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

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

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Current Retention Snapshot:

Metric	Current	Industry Benchmark	Status
D1 Retention	[X%]	25–40%	🔴/🟡/🟢
D7 Retention	[X%]	10–25%	🔴/🟡/🟢
D30 Retention	[X%]	5–15%	🔴/🟡/🟢
DAU/MAU	[X%]	10–20% typical	🔴/🟡/🟢

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

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Root Cause Hypotheses:

Hypothesis	Evidence	Confidence	Test
[Cause]	[Data point]	H/M/L	[How to validate]

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

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Recommended Interventions:

Intervention	Target Drop	Expected Lift	Effort	Priority
[Specific change]	D1 / D7 / D30	[X%]	S/M/L	1/2/3

Monitoring Plan:

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

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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

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