Retention analysis

Retention Analysis

Before starting

Confirm (ask or infer) before running the analysis:

• Cohort data format — is stage-level cohort data available (D1/D7/D30 or W1/W4/W12 per cohort)? Aggregate retention rate alone is insufficient for diagnosis.
• Product type — consumer app, B2B SaaS, developer tool, enterprise, PLG? (determines which retention window and benchmark applies)
• Activation event definition — what is the FVM (First Value Moment)? If undefined, retention windows may be measuring the wrong thing.
• North star metric — what is the product's primary measure of user value?
• Recent changes — any major product, acquisition channel, or pricing changes in the last 60–90 days that could explain cohort diagonal shifts?
• ICP card — is one available? (required if churn type is fit churn or cohort diagonal is declining — ICP may be drifting)

If cohort data is unavailable: block and return guidance on constructing a proxy from available data (see Inputs section).

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Contract

This skill guarantees:

• Correct retention window is always selected for the product type before any benchmark comparison
• Curve shape is named and interpreted before any intervention is recommended
• Churn type is classified with evidence — intervention recommendations are matched to churn type
• "What this analysis cannot answer" section is always included
• No lifecycle re-engagement campaign is recommended when churn type is activation churn

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Role: Cohort Diagnostician. You read retention curves the way a doctor reads an EKG — you know what the shapes mean, what caused them, and which interventions match which pathology. You never guess. You find the evidence that lets you act with confidence.

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Inputs

Required before proceeding:

• Cohort retention data (ideally: cohort table with retention at D1/D7/D30 or W1/W4/W12)
• Product type (consumer app / B2B SaaS / developer tool / enterprise / PLG self-serve)
• Current north star metric or activation event definition
• Any recent product or acquisition changes (the cohort diagonal will reveal if something changed)

If cohort data is unavailable:

BLOCK. Return:
"Retention analysis requires cohort data — not aggregate retention rate.
An aggregate retention rate tells you retention is low. A cohort table tells
you whether it's getting better, who retains, and where the drop happens.

What data do you have available?
  - Weekly/monthly active user counts? (Better than nothing; still limited.)
  - User-level event logs? (We can construct a cohort table from these.)
  - Only an aggregate D30 number? (We can start with curve shape hypothesis
    but cannot diagnose with confidence.)

Describe your available data and I will tell you what analysis is possible."

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Step 1 — Select the right retention window for the product

Using the wrong window is the most common retention analysis mistake. A B2B SaaS product with a monthly workflow will look terrible at D7 — but that's expected, not a problem.

Product type	Key retention windows	Healthy benchmark
Consumer / high-frequency SaaS	D1, D7, D30	D7 > 25%, D30 > 10%
B2B / developer tools	W1, W4, W12	W4 > 40%, W12 > 25%
Annual-contract enterprise	M1, M3, M6, M12	80–90% annual renewal
PLG self-serve	D7, D30, D90	D30 > 30% for high-fit signups

⚠ Benchmark context: These thresholds are industry medians for the named product types under normal conditions. They do not apply to: low-frequency / seasonal products (weekly task managers, tax software), enterprise pilots with intentional limited rollout, regulated workflows with mandatory cooling-off periods, background/API-only products where "return" is the wrong metric, or any product in the first 90 days before a stable cohort exists. Establish your own baseline before comparing against these numbers. A product below these benchmarks is not necessarily broken — it may be using the wrong window or the wrong definition of "return."

Confirm the right window before reading the curve. Then compare actual retention to these benchmarks only if the product type matches.

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Step 2 — Read the curve shape

A retention curve is the percentage of users from a cohort who return at each time point. The shape of the curve tells you the category of the problem before you look at any other data.

Five curve shapes:

Shape 1: FLATTENS TO A NON-ZERO BASELINE
   100%  ╔════╗
         ║    ║
    25%  ║    ╚════════════════════════════ ← flattens here
     0%  ╚────────────────────────────────→ time
   
   Diagnosis: A loyal core exists. PMF is real for a subset of users.
   Problem: Not growing fast enough, or the baseline is too small.
   Next action: Study the retained cohort. What do they have in common?
   Invest in acquisition of users who look like them.

Shape 2: CONTINUOUS DECLINE TOWARD ZERO
   100%  ╔════╗
         ║    ╚╗
         ║     ╚╗
         ║      ╚════→ 0%
     0%  ╚────────────→ time
   
   Diagnosis: No retained user segment has formed. PMF is not yet proven.
   Next action: Fix the product before scaling acquisition. More traffic
   into a leaky bucket makes the problem harder to see, not easier to solve.

Shape 3: SHARP EARLY DROP THEN FLATTENS
   100%  ╔╗
         ╚╗╚╗
          ╚═╚══════════════════════════════ ← flattens here
     0%   ─────────────────────────────────→ time
   
   Diagnosis: Large activation failure. Users who survive onboarding
   are retained. The problem is getting users to survival.
   Next action: FVM is the lever. Run /funnel-audit on the activation flow.
   How many users who sign up actually reach FVM? Fix that first.

Shape 4: BUMP IN THE MIDDLE
   100%  ╔════╗
         ║    ╚╗         ╔╗
         ║     ╚═════════╝╚══ ← bump here
     0%  ╚────────────────────→ time
   
   Diagnosis: A re-engagement effort worked, OR there is a seasonal pattern.
   Next action: Identify what caused the bump. Is it a specific campaign?
   A seasonal event? If identifiable, operationalize it (make it recurring).
   If it was a campaign, run it again with a control group.

Shape 5: DECLINING ACROSS COHORTS (newer cohorts worse than older ones)
   100%  Cohort Jan: ════════════ → retains at 30%
         Cohort Mar: ══════════ → retains at 24%
         Cohort May: ════════ → retains at 18%
     0%  ─────────────────────────────────→ time
   
   Diagnosis: Recent product changes degraded the experience, OR the ICP
   is drifting (new acquisition is reaching the wrong segment).
   Next action: Compare the January cohort to the May cohort. What changed
   about the product, acquisition channels, or messaging? Run /icp-research
   if the cohort profile has changed.

The key diagnostic question: does the curve flatten? If the curve never flattens and declines toward zero, there is no retained user segment yet. This must be addressed before any acquisition scaling.

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Step 3 — Read the cohort diagonal

Most teams read cohort tables horizontally (how does one cohort behave over time?). The most useful read is diagonal (how does retention at the same time-point compare across cohorts?).

Example cohort table:

Cohort    | D7  | D14 | D30 | D60 | D90
----------|-----|-----|-----|-----|-----
Jan       | 35% | 28% | 22% | 18% | 16%
Feb       | 38% | 31% | 25% | 20% | —
Mar       | 42% | 34% | 28% | —   | —
Apr       | 40% | 33% | —   | —   | —

The D7 diagonal: Jan=35% → Feb=38% → Mar=42% → Apr=40%
→ The product improved from Jan to Mar; Apr is slightly below Mar.

The D30 diagonal: Jan=22% → Feb=25% → Mar=28%
→ D30 is consistently improving — product changes are working.

Three things to read on the diagonal:

1. Is the product improving? If newer cohorts retain better at the same time-point, improvements are working.
2. Before/after a product change: split cohorts at the date of a major change. Did the diagonal improve or decline afterward?
3. The segment that retains best: filter cohorts by acquisition channel, use case, company size, or first feature used. Which segment has the highest diagonal retention? That is your Core ICP signal.

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Step 4 — Classify the churn type

Each churn type has a different cause and a different intervention. Using the wrong intervention for the churn type is the most common retention strategy mistake.

Churn type	When it happens	Signal	Intervention
Activation churn	D1–D7	Users who sign up but never complete the onboarding flow or never reach FVM	Run /funnel-audit on the activation flow. The problem is onboarding, not retention.
Evaluation churn	D7–D30	Users who activated but didn't find enough value to return	Product didn't prove value in the evaluation window. Experiment on post-activation return triggers; improve the second-session trigger.
Fit churn	D30–D90	Users who engaged regularly but disengaged over weeks	Wrong ICP. This user came with a use case the product doesn't serve well. Run /icp-research to update the ICP definition.
Lifecycle churn	Months after activation	A previously strong user suddenly leaves	Competitive displacement, champion left the company, or role changed. Experiment on champion retention and competitive alerts.
Involuntary churn	Random	Payment failure notices; subscription lapses	Billing and dunning mechanics. Mechanical fix, not a growth experiment.

Do not run lifecycle re-engagement campaigns when the problem is activation churn. If users are churning in D1–D7, a "we miss you" email at D60 is not the solution. Match intervention timing and type to the churn type.

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Step 5 — Formulate the next experiment

After classification, output one clear next experiment direction:

Retention diagnosis summary:
  Curve shape: [Shape name]
  Churn type: [Churn type name]
  Evidence: [2-3 specific observations]
  
Recommended next experiment direction:
  "If we [action], then [metric] will [direction] by [amount] because [reason]."
  
  Effort: [Low / Medium / High]
  Signal window: [Days or cohorts needed to read the result]
  
  Next step: /growth-experiment

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Output format

## Retention Analysis

### Context
Product type: [Product type]
Retention window used: [Window]
Data quality: [Freshness, any gaps or caveats]

### Benchmark comparison
[Current retention] vs [Benchmark] — [above / at / below benchmark]

### Curve shape
[Shape name]: [1-2 sentence interpretation of what this shape means for this product]

### Cohort diagonal reading
[What the diagonal shows — improving/declining/stable, and when/why it shifted]

### Churn type
[Type name]
Evidence: [2-3 specific data observations]

### Next experiment direction
[Hypothesis in "If/then/because" format]
Effort: [Low/Medium/High]
Signal window: [Time or cohort count]

### What this analysis cannot answer
[Limitations of the available data; what additional data would sharpen the diagnosis]

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Brain reads / writes

If a companion aether-growth-brain repo is connected:

Before analyzing:

• Read experiments/experiment-log.md — check if prior retention experiments have run; avoid recommending experiments that already produced LOSS or CONFOUNDED verdicts for this churn type
• Read knowledge/icp-map.md — if churn type is fit churn or cohort diagonal is declining, compare current ICP definition against which cohorts are churning

Brain write (after analysis):

• Write retention analysis summary to experiments/experiment-log.md: curve shape, churn type, recommended next experiment direction
• If ICP drift detected (declining diagonal, fit churn concentration in new cohorts): flag in knowledge/icp-map.md with recommendation to re-run /icp-research

Brain not connected: proceed; recommend documenting cohort diagnoses locally for trending over time.

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Anti-patterns

Anti-pattern	Why it fails	Fix
Using aggregate D30 retention without cohort table	Cannot distinguish improving trend from declining trend; cannot segment which users retain	Require cohort table; return BLOCK if unavailable
Wrong retention window for product type	B2B SaaS at D7 looks terrible by design; producing panic where there should be patience	Step 1 forces window selection before any benchmark comparison
Re-engagement campaign for activation churn	"We miss you" at Day 60 doesn't help users who churned at Day 3 because onboarding failed	Match intervention to churn type; activation churn → fix onboarding, not re-engagement
Fixing retention before activation is solved	Curve Shape 3 (sharp early drop then flatten) means most users never reach retention	Shape 3 diagnosis routes to /funnel-audit on activation; don't invest in retention programs for users who never activated
Scaling acquisition during Shape 2 (continuous decline toward zero)	More traffic into a no-PMF product accelerates burn, not learning	Diagnose and fix product/PMF before scaling acquisition
Confusing cohort diagonal decline with bad retention	Decline means recent cohorts are worse — this is a product or ICP drift signal, not just a retention issue	Read the diagonal, not just one cohort's curve
Involuntary churn treated as engagement problem	Payment failures are mechanical, not behavioral	Fix billing mechanics (dunning, payment retry, update flows) before running engagement experiments

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NRR and GRR benchmarks (2025–2026)

Metric	Median	Best-in-class	Source
NRR (all B2B SaaS)	~106%	>120%	OpenView 2025, Bessemer Cloud Index 2025
NRR ($1–3M ARR)	~90%	94%	OpenView 2025
NRR ($15–30M ARR)	~100%	105%+	OpenView 2025
NRR ($100M+ ARR)	~115%	130%+	OpenView 2025
GRR (target)	>90%	>94% at scale	OpenView 2025
Logo churn (SMB SaaS)	<2% monthly	<1%	Recurly 2025
Logo churn (Mid-market)	<1% monthly	<0.5%	Recurly 2025
D7 retention (high-frequency SaaS)	>25%	>35%	Reforge 2025
D30 retention (PLG high-fit users)	>30%	>45%	Reforge 2025
W4 retention (B2B / developer tools)	>40%	>55%	Reforge 2025
Annual renewal rate (enterprise)	80–90%	>90%	Bain 2025

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Related skills

Skill	When to use
growth/funnel-audit/SKILL.md	Curve Shape 3 detected: activation failure requires funnel audit
pmm/icp-research/SKILL.md	Fit churn or declining diagonal: ICP may be drifting
growth/growth-experiment/SKILL.md	After classification: next experiment hypothesis to test
growth/DOMAIN.md	Lifecycle design, re-engagement triggers, PLG motion definitions

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Common mistakes this workflow prevents

Mistake	What this workflow does instead
Using D7 to evaluate a B2B tool (wrong window)	Step 1 selects the right window first
Seeing "retention is bad" without knowing why	Steps 2-4 classify the type before recommending action
Recommending re-engagement emails for activation churn	Churn type classification rules this out explicitly
Running aggregate retention instead of cohort	Input block requires cohort data
Fixing the retention problem before the activation problem	Shape 3 (early drop) explicitly routes to /funnel-audit first

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Connects to

Upstream: /icp-research — if churn type is fit churn or the cohort diagonal shows declining new cohorts, ICP may be drifting. Run /icp-research to update.

Parallel: /funnel-audit — if curve shape is Shape 3 (sharp early drop), activation is the problem and funnel audit is the right tool.

Downstream: /growth-experiment — the hypothesis at the end of this analysis becomes the next experiment.

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Validation criteria

• Correct retention window selected for product type
• Benchmark comparison included
• Curve shape named and interpreted
• Cohort diagonal read
• Churn type named with evidence
• Next experiment direction written in "If/then/because" format
• "What this analysis cannot answer" section included

References & Sources

Tier 1:

• Reforge retention frameworks: curve shape interpretation, cohort reading technique, PMF diagnosis via retention
• growth-retention-analysis (growth-skills v1.0, score 9/10): churn type classification with interventions, five curve shape system, cohort diagonal technique