---

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.

---

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

---

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:

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]

---

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.

---

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]

---

Required Inputs

Work from what is given. If any of these is missing, still produce the complete output: fill each gap with a reasonable assumption marked (assumed, confirm) and list those assumptions at the end. Ask a question first only when the core subject itself is missing (there is nothing to write about).

• 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)

Deeper Materials

This skill ships with support files — use them when they are available:

• references/curve-reading.md — Reading Retention Curves Without Fooling Yourself. Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses.
• templates/retention-readout.md — a fill-in version of the deliverable with the quality gates inline. Offer it when the user wants to work the document themselves rather than have it generated.

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

Example Trigger Phrases

• "Analyse user retention."
• "Investigate churn."
• "Build a retention improvement plan."