retention-analysis

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)

Scoring Rubric (0–40)

Score any output of this skill before handing it over; 32+ is ship-quality.

| Dimension | 0 | 5 | 10 |

|---|---|---|---|

| Curve diagnosis | Reports a retention number without curve shape | Shape shown but not interpreted | Flattening vs trending-to-zero explicitly diagnosed and tied to what it means (PMF vs onboarding problem) |

| Cohort discipline | All users lumped into one blended rate | Cohorts split but read as a table dump | Cohorts segmented before analysis, with the divergent cohort called out and explained |

| Aha-moment linkage | Activation never connects to retention | Correlation claimed without data or caveat | The behavior separating retained from churned users identified with evidence, or honestly flagged unknown with a plan to find it |

| Intervention specificity | "Improve onboarding"-grade advice | Specific actions but no measurement plan | Interventions name the user moment they target, plus a monitoring plan with an alert threshold and churned-user interviews |

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