Cohort retention analysis — triangle chart construction, curve shape analysis for PMF detection (flattening/declining/rising), layer cake chart, PMF-driven priority shifting, 5 retention anti-patterns, 4 improvement levers, three definitions setup. Use when measuring PMF, analyzing user retention, or deciding whether to scale or improve product.

Retention & Cohort Analysis

When to Apply

Measuring product-market fit
Deciding whether to invest in growth vs product improvement
When the founder asks "do we have PMF?"
When retention data is available from integrations
Quarterly PMF reassessment

Core Framework

Three Definitions (Set During Onboarding)

Before any retention measurement, guide the founder to define:

Cohort grouping — How new users are grouped
- Weekly: daily-use products
- Monthly: utility products
- Quarterly: infrequent-use products (travel, tax)
Active action — What counts as "active." Must reflect genuine value delivery.
- Ask: "Imagine watching a customer use your product. What moment tells you they're genuinely getting value?"
- B2B SaaS: "completed a core workflow"
- Consumer: "engaged with 3+ pieces of content"
- Marketplace: "completed a transaction"
Time granularity — How often users should ideally use the product. Cross-check against chosen action for consistency.

Triangle Chart (Cohort Retention Table)

         Week 0  Week 1  Week 2  Week 3  Week 4  Week 5
Jan      100%    62%     45%     38%     35%     34%   <- flattening
Feb      100%    58%     41%     33%     31%     ...
Mar      100%    65%     50%     42%     ...
Apr      100%    71%     55%     ...                    <- improving

Curve Shape Analysis (Most Important Insight)

Analyze SHAPE, not absolute numbers:

Curve Shape	PMF Signal	Agent Action
Flattening (stabilizes at any level)	PMF detected	Shift to growth Drivers. "Retention flattening at ~34%. Even Google Photos flattened at 20-40%."
Declining to zero (no stabilization)	No PMF	Shift to product/activation Drivers. Trigger WHY analysis. "Users not sticking. Understand why before investing in growth."
Rising (curves go up over time)	Strong PMF + network effects	Propose aggressive scaling. "Retention increasing — extremely strong signal."
Newer cohorts better	Product improving	"Product improvements working — newer cohorts retain better."
Newer cohorts worse	Product or acquisition degrading	"Warning: newer cohorts retain worse. Investigate product quality or acquisition quality."

Layer Cake Chart

Cohorts stacked over absolute calendar time. Shows whether active user base grows from retained cohorts (healthy) or just cycles through new users (treadmill).

PMF-Driven Priority Shifting

Situation	Agent Focus
No PMF + Vitamin product	Repositioning and pain discovery before growth
No PMF + Painkiller product	Product improvement, onboarding, user research
PMF signal but no "Locally Famous" group	Deepen penetration in one specific group
PMF + Locally Famous confirmed	Shift to scaling: growth Drivers get higher impact_weight

This directly feeds into the KPI tree's dynamic impact_weight system.

5 Retention Anti-Patterns

Anti-Pattern	Detection	Response
Time period too large	Quarterly cohorts for daily-use product	"Your product is daily-use. Weekly cohorts give more honest signal."
Action too easy	"Opened app" or "visited site"	"Doesn't reflect real value. Consider [specific recommendation]."
Payment-only tracking	Only tracking subscription status	"Users stop using before they stop paying. Add usage-based action metric."
Cherry-picking	Citing single favorable retention number	"Which period? Show the complete triangle chart."
Curve misinterpretation	High initial retention that declines	"50% that declines to zero is worse than 20% that flattens. Shape > number."

4 Improvement Levers

When retention curves don't flatten:

Product improvement — New use cases, speed, simpler flows
Better user acquisition — Targeting users who are a better fit. "Paid cohorts retain worse than organic. Consider shifting budget."
Onboarding/activation — Help users reach "aha moment" faster. Often cheapest lever. "What was the user doing yesterday? What should they do differently today?"
Network effects — If applicable, user-to-user value. "Each new user could make your product better for existing users."

Decision Rules

Three definitions before measurement — no retention analysis without explicit cohort, action, and time definitions
Shape over numbers — 20% that flattens beats 50% that declines
PMF drives priorities — no scaling without retention flattening
Newer vs older cohorts — compare across time to detect product trajectory
Layer cake for growth truth — reveals treadmill vs genuine growth
Cheapest lever first — onboarding improvement often has highest ROI

Anti-Patterns to Detect

Anti-Pattern	Signal	Response
Scaling before PMF	Growing acquisition with declining retention	"Retention curve hasn't flattened. Fix retention before scaling."
Wrong granularity	Mismatched time period and product usage	"Adjust cohort grouping to match actual product usage frequency."
Vanity retention	Tracking logins instead of value delivery	"Redefine 'active' to reflect genuine value delivery."
Single-number fixation	"Our retention is 40%"	"40% when? Show the full curve over time."

retention-cohorts