Retention Diagnosis

Retention Diagnosis

Use this skill when the user wants to understand why users or accounts churn, flatten retention curves, or identify behaviors correlated with retention.

What this skill must produce

Always produce:

1. Retention problem statement
2. Lifecycle framing
3. Diagnostic hypotheses
4. Behavioral drivers and detractors
5. Measurement plan
6. Intervention ideas
7. Experiment backlog
8. Risks and caveats

Inputs to gather

If present, extract:

• product type
• retention data by cohort
• D1/D7/D30 or weekly/monthly retention
• account retention vs user retention
• critical event
• activation event
• churn reasons
• product segments / personas
• pricing tier / ACV / plan differences
• existing hypotheses

If data is unavailable, define the diagnostic plan and the events needed.

Working rules

• Separate new-user retention from current-user retention and resurrected-user retention.
• Tie retention to the critical event, not arbitrary activity.
• Look for both drivers and detractors.
• Do not confuse correlation with causation.
• Identify where product quality issues may distort retention before recommending growth tactics.

Step-by-step method

Step 1: define the retention lens

State whether this is:

• user retention
• account retention
• revenue retention
• logo retention
• feature retention

Then define the time interval that matches the product’s natural usage rhythm.

Step 2: frame lifecycle stages

Split the problem into:

• new users
• current users
• dormant / resurrected users

Identify which stage appears most broken.

Step 3: define the critical event

Choose the action that best reflects value. Explain why generic activity is insufficient.

Step 4: generate diagnostic hypotheses

Examples:

• users do not reach the aha moment
• activation occurs but habit loop never forms
• retained accounts lack multi-user adoption
• feature adoption is shallow
• poor product quality causes hidden churn
• wrong segments are entering the funnel
• plan packaging mismatches true value

Step 5: identify likely drivers

Specify behaviors that may correlate with stronger retention. If relevant, separate:

• onboarding-phase drivers
• value-discovery drivers
• current-user stickiness drivers
• resurrection triggers

Step 6: identify likely detractors

Specify behaviors or product states that may depress retention:

• crashes / bugs
• empty states
• poor matching / inventory
• slow time-to-value
• over-complex workflow
• weak reminders / triggers
• poor collaboration adoption
• pricing mismatch

Step 7: create a measurement plan

Define:

• cohorts to compare
• retention intervals
• event sequences to inspect
• power-user behaviors to compare
• segments worth splitting

Step 8: create interventions and experiments

Convert the most likely causes into tests.

Output format

Retention problem statement

• What seems wrong:
• Who is affected:
• Time horizon:
• Primary business consequence:

Lifecycle framing

Critical event

• Event:
• Why it reflects value:
• Why generic DAU/WAU/MAU may be misleading:

Diagnostic hypotheses

Behavioral drivers and detractors

Likely drivers

Likely detractors

Measurement plan

• Cohorts:
• Intervals:
• Segment cuts:
• Key funnel comparisons:
• Product-quality checks:
• Needed events/properties:

Intervention ideas

Experiment backlog

Risks and caveats

• Correlation vs causation:
• Missing data:
• Segment distortion:
• Seasonality / lifecycle mismatch:
• Product-quality blockers:

Special instructions

If the user shares a retention curve or cohort chart:

• interpret the shape directly
• say whether it looks like no product-market fit, weak activation, or weak habit formation
• be explicit about what can and cannot be inferred

If the product is B2B:

• assess retention at both user and account levels
• inspect whether retained accounts have collaboration / seat / workflow depth
• consider gross and net revenue retention where relevant