This source did not publish a separate summary. Review SKILL.md before using the skill.
SKILL.md
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:
Steepness of initial drop (D1–D7) — onboarding problem
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
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)
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
