growth-retention-diagnostics

name	growth-retention-diagnostics
description	Diagnose churn, retention, or growth problems with data. Use when metrics are declining and you need to find the root cause, not just report the numbers.

Growth & Retention Diagnostics

Move from "churn is up" to "here's why and here's what to do about it" in half a day. Claude runs the cohort math, generates diagnostic hypotheses, and structures the intervention design. You validate against your user knowledge and decide what to pursue.

When to Use

• Churn or revenue retention is trending the wrong direction
• Activation or onboarding metrics have dropped
• A specific cohort or segment is underperforming
• You need to build a retention case for leadership
• Growth has stalled and you need to find the lever

When NOT to Use

• You're setting up initial metrics and instrumentation — use KPI definition in CLAUDE.md
• You're running a specific experiment — use the Experiment Brief template
• The problem is clearly a bug or outage — that's incident response, not diagnostics

The AI-Native Approach

Step	Time	Claude Does	You Do
Define the problem	30 min	Structure the data picture, flag anomalies	Confirm what's real vs. noise
Run cohort analysis	1-2 hrs	Run cohort math, generate retention curves, segment breakdowns	Validate data quality, add context
Diagnose root cause	1-2 hrs	Generate hypotheses ranked by evidence, map to user journey	Apply user knowledge, eliminate false leads
Design interventions	1 hr	Structure intervention options with expected impact	Choose what to pursue, validate with eng/design
Build the case	30 min	Draft retention plan for stakeholders	Add strategic context and commitment

Process

Step 1: Define the Problem with Data (30 minutes)

Here's what we're seeing: [paste your metrics — churn rate, NRR, retention
curves, activation rates, whatever you have].

Help me structure the problem:
- What exactly is declining, by how much, and since when?
- Is this across all users or specific segments?
- Is this a sudden change or a gradual trend?
- What changed around the time the metric shifted? (releases, pricing, competitors, seasonality)
- What's the revenue impact of this trend continuing?

Step 2: Run Cohort Analysis (1-2 hours)

Here's our user/revenue data: [export from analytics tool].

Run cohort analysis:
- Monthly cohorts for the last 12 months
- Retention at Day 1, Day 7, Day 30, Day 60, Day 90
- Segment by: [plan type, acquisition channel, use case, geography — whatever matters]
- Identify which cohorts are underperforming vs. your baseline
- Flag the biggest drop-off point in the user journey

For revenue retention (NRR), break down:
- Expansion MRR by cohort
- Contraction MRR by cohort
- Churn MRR by cohort
- Net: are newer cohorts healthier or sicker than older ones?

Step 3: Diagnose Root Cause (1-2 hours)

Based on the cohort analysis, generate diagnostic hypotheses.

For each hypothesis:
- What the data suggests
- Where in the user journey the problem occurs
- Which user segment is most affected
- What additional data would confirm or reject this hypothesis
- Confidence level: high (data clearly points here), medium (pattern exists),
  low (possible but thin evidence)

Map hypotheses to these common root cause categories:
1. Onboarding failure (users never reach value)
2. Value gap (product doesn't solve the job well enough)
3. Competitive loss (users found a better alternative)
4. Pricing mismatch (value delivered doesn't justify cost)
5. Engagement decay (users engaged initially, then stopped)
6. Segment mismatch (acquiring users who aren't a good fit)

Validate against what you know: Which hypotheses match what users are telling you? Which are surprising? What context does Claude not have?

Step 4: Design Interventions (1 hour)

For the top 2-3 hypotheses, design interventions:

For each:
- What changes (product, onboarding, messaging, pricing, support)
- Expected impact on the target metric
- Effort to implement (days/weeks, who's involved)
- How to test it (experiment design, success criteria)
- Time to measurable result

Rank by: impact × confidence ÷ effort.

Step 5: Build the Retention Case (30 minutes)

Draft a retention plan for [audience — leadership, board, team]:

Structure:
- The problem: what's happening and the revenue impact
- Root cause: what our analysis found (lead with the strongest evidence)
- Plan: 2-3 interventions with expected impact and timeline
- Investment needed: effort, resources, trade-offs
- What happens if we don't act: projected impact at current trend
- How we'll measure: success metrics and review cadence

Output

• Structured problem definition with revenue impact
• Cohort analysis with segment breakdowns
• Ranked diagnostic hypotheses with evidence ratings
• Intervention designs with experiment briefs
• Retention plan for stakeholder presentation

Common Pitfalls

1. Treating symptoms, not causes. "Add a re-engagement email" is a tactic, not a diagnosis. Find the root cause first.
2. Averaging across segments. Overall churn can look stable while one segment hemorrhages and another grows. Always segment.
3. Confusing correlation with causation. "Users who use Feature X retain better" might mean Feature X drives retention, or it might mean engaged users discover Feature X. Dig deeper.
4. Analysis paralysis. The point of this skill is a half-day diagnostic, not a 6-month research project. Get to hypotheses fast, design experiments, and test.
5. Ignoring qualitative data. Cohort math tells you where the problem is. Exit surveys, support tickets, and cancellation reasons tell you why. You need both.

Related Skills

• Discovery Process — when the diagnostic reveals a problem worth a deeper investigation
• Pricing & Packaging — when pricing mismatch is the root cause
• User Research Methods — when you need qualitative data to complement the quantitative diagnosis
• Reference: FRAMEWORKS.md — SaaS Metrics, NRR benchmarks