Growth & Retention Diagnosis

Run a retention analysis and diagnose the leak

You are a retention analyst diagnosing {{product_name}}'s retention. Target retention bar: {{retention_target}}.

## Step 1 — Define the retention metric
- Unit: daily, weekly, monthly active?
- Event: logged in, performed core action, completed outcome?
- Cohort boundary: signup date, first-use date, purchase date?

## Step 2 — Segment the leak
Three cuts:
1. By cohort (signup month → seasonality or onboarding change?)
2. By acquisition channel (paid vs. organic — channel quality?)
3. By first-week behavior (activated vs. not — onboarding flow?)

## Step 3 — Funnel analysis
For the day 1 → day 7 drop-off:
- Event 1: {{first_core_event}}
- Event 2: {{second_core_event}}
- Drop-off rate per step

The biggest % drop is the leak candidate.

## Step 4 — Hypothesis generation
For the leak candidate:
- Why might users drop here? (value prop unclear, friction, misaligned expectation)
- What evidence would confirm each hypothesis?
- What intervention could move the metric?

## Step 5 — Intervention priorities
Rank interventions by:
- Expected lift (rough)
- Time to ship
- Evidence required to validate
- Reversibility

## Output
1. Retention metric definition
2. Top 2 cohort patterns
3. Biggest funnel drop-off with hypotheses
4. Top 3 interventions with expected lift

Design a behavioral cohort diagnosis

You are a product analyst diagnosing retention on {{product_name}}. Aggregate retention curve: {{aggregate_curve}}.

## Cohort dimensions
Slice the base into cohorts on:
1. Acquisition channel (organic, paid, referral, etc.)
2. First week behavior (activated core feature vs. not)
3. User segment (role, company size, use case)
4. Signup month (time cohorts to separate seasonality)

## For each cohort
- 7-day retention
- 28-day retention
- 90-day retention
- Feature usage distribution
- Cohort size

## Pattern surfacing
1. Which cohorts have outlier high retention (and why)?
2. Which cohorts have outlier low retention (and why)?
3. What share of total retained users comes from the top cohort?
4. If we only kept the top 3 cohorts, what would our aggregate look like?

## Intervention options
- Acquisition targeting change (more of the winning cohort)
- Activation redesign (fix the onboarding path for leaky cohorts)
- Product scoping (serve a different segment decisively)

## Output
1. Cohort retention table
2. 2-3 insights with evidence
3. The one cohort with highest growth potential
4. The one cohort we might explicitly stop acquiring

Design a feature adoption funnel audit

You are a product analyst auditing {{feature_name}}'s adoption funnel. Launched: {{launch_date}}. Current aggregate adoption: {{current_adoption}}.

## Funnel stages
| Stage | Metric | Current | Target | Gap |
|-------|--------|---------|--------|-----|
| Aware | % seen feature entry point | | >80% | |
| Interested | % clicked feature CTA | | >30% of aware | |
| Trial | % completed first action | | >50% of interested | |
| Retained | % returned in week 2 | | >60% of trial | |

## Leak identification
The worst ratio stage-to-stage is the leak.

## Hypotheses per stage
- **Low awareness**: entry point visibility, placement, triggers
- **Low click-through**: CTA copy, perceived value, timing
- **Low trial completion**: first-time friction, unclear next step, expectation mismatch
- **Low retention**: one-time utility vs. habit-forming value

## Interventions per hypothesis
- Awareness: in-product nudge, email, changelog
- Click-through: copy A/B, visual hierarchy
- Trial: reduce steps, improve empty states, inline guidance
- Retention: follow-up cue, notification, habit anchor

## Output
1. Filled funnel table
2. The biggest leak stage with 2-3 hypotheses
3. Top 2 interventions to test
4. The next measurement checkpoint and success criteria

Churn Analysis & Prevention Strategy

You are a retention & growth PM. Help me analyze churn and build a prevention strategy.

## Product Context
- **Product:** {{product_name}}
- **Current churn rate:** {{monthly_churn_%}}
- **Benchmark for industry:** {{industry_avg_%}}
- **Billing model:** {{monthly | annual | usage-based}}
- **Top churn reasons (if known):** {{reasons}}

## Churn Analysis Framework

### 1. Churn Segmentation
| Segment | Churn Rate | % of Churners | Behavior Pattern |
|---------|-----------|---------------|-----------------|
| New users (0-30 days) | | | |
| Established users (30-90 days) | | | |
| Power users (90+ days) | | | |
| Downgraders | | | |

### 2. Root Cause Analysis
| Cause Category | % of Churn | Key Signal | Preventable? |
|---------------|-----------|-----------|-------------|
| Never activated | | | Yes / Partially |
| Found alternative | | | |
| Outgrew product | | | |
| Price sensitivity | | | |
| Bad experience / bug | | | |
| Lack of engagement | | | |

### 3. Leading Indicators (Churn Predictors)
| Indicator | Threshold | Days Before Churn | Reliability |
|-----------|-----------|------------------|-------------|
| Login frequency drops |

Build a retention-focused onboarding optimization plan

Create an onboarding optimization plan for {{product_name}} that maximizes the percentage of new users who reach the activation milestone.

## Context
- Product: {{product_name}}
- Activation milestone: {{activation_milestone}} (e.g., "user completes first project," "user invites a teammate," "user generates first report")
- Current signup-to-activation rate: {{activation_rate}}
- Current onboarding steps: {{onboarding_steps}}
- Time to activation (median): {{time_to_activation}}
- 30-day retention rate: {{retention_rate}}
- Primary user persona: {{user_persona}}

## Step 1: Funnel Mapping
1. List every step from signup to activation milestone
2. For each step, estimate or provide the completion rate
3. Identify the biggest drop-off point (where are you losing the most users?)
4. Calculate the "time to value" — how long until the user has their first "aha moment"?

## Step 2: Drop-off Diagnosis
For each major drop-off point:
1. What is the user trying to accomplish at this step?
2. What friction exists? (too many fields, unclear CTA, requires external data, loading time)
3. What do users who complete this step have in common vs. those who don't?
4. Is this a motivation problem (they don't see why) or an ability problem (they don't know how)?

## Step 3: Optimization Ideas
Generate 3-5 optimization ideas for each drop-off point:
- Reduce friction: can you remove or defer this step?
- Add motivation: can you show the value of completing this step?
- Improve guidance: can you add contextual help, tooltips, or a wizard?
- Social proof: can you show what other users achieved after this step?
- Personalization: can you tailor the experience based on the user's role or goal?

## Step 4: Prioritize and Plan
1. Score each optimization by: expected impact on activation rate, implementation effort, and confidence level
2. Select the top 5 optimizations for the next sprint cycle
3. For each, define:
   - Success metric and target
   - A/B test or staged rollout plan
   - Rollback criteria
4. Set a review date to measure aggregate impact on activation and 30-day retention