Activation Analysis: Setup → Aha → Habit Framework
Quick Start
/activation-analysis
Then provide:
- Your product and core value proposition (or I'll pull from business-info)
- Current onboarding flow (what steps do new users take?)
- Any metrics you have (setup completion %, D7 retention, time-to-value)
I'll diagnose your activation funnel using Setup -> Aha -> Habit, identify the biggest bottleneck, and recommend specific fixes.
Output: Saved to thoughts/shared/pm/analyses/activation-analysis-[date].md
Time: ~15 min with data, ~25 min if defining stages from scratch
When to use: When diagnosing activation problems, improving onboarding, or measuring early product engagement
Framework source: Aakash Gupta's "Ultimate Guide to Activation" and "How to Measure Onboarding"
Context Routing Logic (Internal - for Claude)
Automatic Context Checks: When this skill is invoked, immediately check:
| Source | Files/Folders | Search Terms | What to Extract |
|---|---|---|---|
| Metrics/Analytics | thoughts/shared/pm/metrics/*.md |
"onboarding", "setup", "activation", D7, D30, "time to value", TTV | Current activation rates by stage, onboarding metrics, D7/D30 retention |
| User Research | thoughts/shared/pm/*.md |
"onboarding", "setup", "first time", "confused", "stuck", "struggle" | User feedback on onboarding, confusion points, success moments |
| Meeting Notes | thoughts/shared/product/meeting-notes/*.md |
"activation", "onboarding", "new users", "drop-off", "support tickets" | CS/support feedback on where users get stuck, win/loss reasons |
| PRDs | thoughts/shared/pm/prds/*.md |
"onboarding", "activation", "tutorial", "first-time user" | Past onboarding improvements, features to drive activation |
| Business Info | thoughts/shared/pm/context/business-info-template.md |
target user, customer segment, use case, primary value | Who you're activating, what value matters to them |
Context Priority:
- Internal context FIRST (business info, existing activation metrics, user research)
- Analytics MCP SECOND (if connected - query activation funnel, D7/D30 by cohort)
- Framework guidance LAST (generic activation tactics)
Cross-Skill Links:
- If retention issues mentioned → Link to
retention-analysis - If expansion opportunities found → Link to
expansion-strategy - If user struggles identified → Link to
user-research-synthesis
Step 0: Understanding Your Current Activation Landscape
Before measuring the Setup → Aha → Habit stages, let me check what data already exists...
Checking:
thoughts/shared/pm/context/business-info-template.mdfor your product and target usersthoughts/shared/pm/metrics/for existing activation metrics and onboarding datathoughts/shared/pm/for user research on onboarding strugglesthoughts/shared/product/meeting-notes/for CS/support feedback on where users get stuckthoughts/shared/pm/prds/for past onboarding improvements
[If analytics MCP connected]: "Let me also query [PostHog/PostHog] for your current activation funnel, setup completion rates, and D7/D30 retention by cohort."
Based on what I find, I'll show you:
Internal Intelligence Summary
From Business Info:
- [Your product and core value proposition]
- [Target user segments]
- [Primary use case]
- Example: "Help product: [description], target: small teams, primary value: [outcome]"
From Metrics/Analytics:
- [Current setup completion rate]
- [Current Aha rate (if defined)]
- [D7/D30 retention by cohort]
- [Time to Aha median]
- Example: "Setup: 65%, Aha: 40% (overall activation: 26%), D7 retention: 35%"
From User Research:
- [User feedback on onboarding friction]
- [What makes successful users different]
- Example: "Users who complete setup in <5 min have 3x higher D30 retention"
From Sales/CS Meetings:
- [Drop-off points where users get stuck]
- [Common confusion or support tickets]
- Example: "70% of support tickets in first week are about [feature]"
From PRDs:
- [Past onboarding improvements and their impact]
- Example: "PRD-2024-02 added templates, increased setup completion by 8%"
Gaps in Knowledge
Based on internal context, we don't yet know:
- [Gap 1]: Exact definition of your Aha moment (based on user behavior data)
- [Gap 2]: Drop-off points in your setup flow
- [Gap 3]: Why churned users didn't persist (need churn interview analysis)
Should I help define your three stages, or would you like to provide existing activation data first?
Step 1: Activation Diagnostic Questions
Instead of generic "what's your onboarding flow," I'll ask:
Question 1: The Biggest Leak
"Where do most new users drop off—in the first 5 minutes, or later in the week?"
This identifies whether the problem is setup friction, Aha not resonating, or habit formation.
Question 2: Aha Moment Evidence
"Among users who stuck around (D7+), what did they all do in their first session that churned users didn't?"
Your Aha must be defined by actual user behavior, not guesses.
Question 3: Onboarding Goals
"What specific actions should every new user complete in their first session to get value?"
This defines your Setup stage.
Question 4: Success Signal
"How do you know a user 'got it'—what behavior indicates they experienced the core value?"
This is your Aha moment definition.
Question 5: Current Metrics
"What % of new signups complete your onboarding, and what % come back on Day 7?"
These baselines inform where to focus.
Overview
Activation is the bridge between signup and retention.
The Setup → Aha → Habit framework breaks activation into three measurable stages:
- Setup: User configures the product to work for them
- Aha: User experiences the core value (magic moment)
- Habit: User turns that value into recurring behavior
The Framework
Stage 1: Setup
What it is: The initial configuration required before a user can experience value
Examples by product type:
- Slack: Create workspace, invite team members, set up channels
- Notion: Create first page, set up workspace structure
- design tool: Create first file, invite collaborators
- Stripe: Connect bank account, configure payment settings
- TaskFlow (example): Create first project, add team members, create first task
Key principle: Setup should be the MINIMUM required to reach Aha
Metrics to track:
- Setup completion rate
- Time to setup completion
- Drop-off points in setup flow
Stage 2: Aha
What it is: The moment when the user experiences your product's core value for the first time
How to find your Aha moment:
- Look at retained vs churned users
- What did retained users do that churned users didn't?
- That differentiating action is your Aha
Examples by product:
- Slack: Sent 2,000 messages (team-wide)
- Dropbox: Put at least 1 file in 1 folder on 1 device
- Facebook: Add 7 friends in 10 days
- Airbnb: Book 1 trip
- LinkedIn: Make 5 connections
- TaskFlow (example): Complete first task with team member
Aha characteristics:
- ✅ Directly tied to core product value
- ✅ Measurable and clear
- ✅ Achievable within first session (or first week)
- ✅ Predictive of retention
Metrics to track:
- Aha completion rate (% of setups → Aha)
- Time to Aha (from signup)
- Correlation between Aha and D7/D30 retention
Stage 3: Habit
What it is: Recurring behavior pattern that cements long-term retention
Why it matters:
- One-time Aha isn't enough
- Habit is what separates million-dollar companies from billion-dollar companies
- Habit = predictable retention
Examples by product:
- Slack: Daily active usage, multiple messages per day
- Notion: Weekly return to update docs
- design tool: Multiple files created per week
- TaskFlow (example): Daily task updates, weekly task creation
Habit = Frequency + Value Pattern
Metrics to track:
- % of Aha users who return (Day 7, Day 14, Day 30)
- Weekly Active Users (WAU) or Daily Active Users (DAU)
- Frequency of core action (e.g., tasks created per week)
- L28 (28-day retention cohort)
How to Use This Framework
Step 1: Define Your Three Stages
Use this prompt pattern:
Use /activation-analysis and reference [[business-info-template]]
Help me define the Setup → Aha → Habit stages for my product.
Our product: [describe your product]
Core value proposition: [what value do users get]
Current onboarding flow: [describe existing flow]
For each stage, help me identify:
1. What actions constitute this stage?
2. What should we measure?
3. Where are users dropping off?
Step 2: Measure Each Stage
Calculate these metrics:
Setup Rate = (Users who complete setup) / (Total signups) × 100
Aha Rate = (Users who hit Aha) / (Users who complete setup) × 100
Habit Rate = (Users who form habit) / (Users who hit Aha) × 100
Overall Activation = Setup Rate × Aha Rate × Habit Rate
Example:
- Setup: 75% (750 of 1000 signups)
- Aha: 60% (450 of 750 setups)
- Habit: 40% (180 of 450 Ahas)
- Overall Activation: 18% (180 of 1000 signups)
Where's the bottleneck? The biggest drop is your priority.
Step 3: Diagnose Drop-offs
If Setup is low (<70%):
- Too much friction in onboarding
- Asking for too much information upfront
- Unclear value proposition
- Technical issues
If Aha is low (<50%):
- Users don't understand how to get value
- Aha moment requires too much work
- Wrong users are signing up
- Product value isn't clear enough
If Habit is low (<30%):
- Product isn't sticky enough
- No triggers to bring users back
- Value isn't compelling enough for repeat use
- Competing with existing habits
Step 4: Improve Activation
Use this prioritization:
Fix the biggest drop first
- If 75% drop at Setup → fix Setup
- If 50% drop at Aha → fix Aha
- If 60% drop at Habit → fix Habit
For Setup improvements:
- Reduce required fields
- Add progress indicators
- Provide templates/examples
- Allow "skip" for non-essential items
- Use social proof ("Join 10,000 teams...")
For Aha improvements:
- Shorten time-to-value
- Add in-product guidance
- Provide sample data
- Create success paths for different user types
- Make the value obvious
For Habit improvements:
- Add email/push notifications
- Create daily/weekly rituals
- Build social loops (team activity)
- Gamification (streaks, achievements)
- Integration hooks (Slack, email)
Time-to-Value (TTV)
TTV = Time from signup to Aha moment
Why it matters:
- Faster TTV = Higher activation
- Faster TTV = Better retention
- Benchmark: Best products get users to Aha in <5 minutes
How to reduce TTV:
Eliminate unnecessary steps
- Question every field in signup
- Can it happen later? Move it later.
Provide shortcuts
- Sample data/templates
- Import from competitors
- AI-generated starting points
Progressive disclosure
- Show advanced features AFTER Aha
- Don't overwhelm new users
Different paths for different users
- Solo user vs team setup
- Technical vs non-technical
- Personal vs work use
Advanced: Cohort Analysis
Compare activation by cohort:
Use /activation-analysis
I have activation data for the past 3 months:
[paste your data or describe metrics]
Help me analyze:
1. Which cohorts have highest activation?
2. What changed between cohorts?
3. Where should we focus improvement efforts?
Look for:
- Seasonality (weekday vs weekend signups)
- Channel quality (organic vs paid)
- User segment differences (small vs large teams)
- Feature launch impact (before/after comparisons)
Activation Metrics Dashboard
Track these KPIs weekly:
| Metric | Definition | Target | Current |
|---|---|---|---|
| Signup → Setup | % who complete setup | 70%+ | ___ |
| Setup → Aha | % who reach Aha moment | 50%+ | ___ |
| Aha → Habit | % who form habit (D7 return) | 30%+ | ___ |
| Overall Activation | Signup → Habit | 15%+ | ___ |
| Time to Aha (median) | Minutes from signup to Aha | <10 min | ___ |
| D7 Retention | % active on Day 7 | 40%+ | ___ |
| D30 Retention | % active on Day 30 | 25%+ | ___ |
Common Mistakes
❌ Optimizing Aha without fixing Setup
- If users never complete setup, Aha doesn't matter
- Fix bottlenecks in order
❌ Defining Aha based on what you WANT users to do
- Aha must be based on DATA (retained vs churned behavior)
- Not your opinion of what's valuable
❌ Ignoring Habit formation
- One-time Aha doesn't predict retention
- Recurring behavior is what matters
❌ Same onboarding for all user types
- Solo vs team users have different needs
- Technical vs non-technical users need different paths
❌ Measuring activation without connecting to retention
- If your "activated" users don't retain, you defined it wrong
- Always validate activation metrics against retention data
Real-World Examples
Example 1: Slack
- Setup: Create workspace, invite 2+ team members
- Aha: Team sends 2,000 messages
- Habit: Daily active usage by team
- Result: 93% of teams that send 2,000 messages become retained customers
Example 2: Dropbox
- Setup: Install desktop app
- Aha: Add 1 file to 1 folder on 1 device
- Habit: Weekly file uploads/syncs
- TTV: <5 minutes
- Result: Simple, clear Aha moment drove explosive growth
Example 3: LinkedIn
- Setup: Complete profile
- Aha: Make 5 connections
- Habit: Weekly profile views, weekly connection activity
- Key insight: Social activation (connections) drove retention
Worksheet: Define Your Activation
Use this with your team:
1. Setup Stage
- Actions required: **___**
- Ideal time to complete: **___**
- Current completion rate: **___**
- Biggest drop-off point: **___**
2. Aha Stage
- Core value action: **___**
- How to measure: **___**
- Current Aha rate: **___**
- Time to Aha (median): **___**
3. Habit Stage
- Recurring behavior: **___**
- Target frequency: **___**
- Current habit formation rate: **___**
- D7/D30 retention: **___**
4. Priority Improvement
- Biggest bottleneck: **___**
- Hypothesis to test: **___**
- Success metric: **___**
Output Integration
Where to Save Your Activation Analysis
Research & Findings:
- Save to:
thoughts/shared/pm/analyses/activation-analysis-[date].md
Onboarding Improvements:
- Create PRD in
thoughts/shared/pm/prds/for each onboarding change - Link this activation analysis as context
- Track changes in the PRD's success metrics section
Activation Metrics:
- Update
thoughts/shared/pm/metrics/with your Setup, Aha, Habit definitions and rates - Track weekly changes as baseline for comparison
Cross-Skill Integration
Feeds into:
/retention-analysis- Activation rate by stage informs retention analysis (Aha users retain better)/prd-draft- Onboarding improvements become features in PRDs/experiment-decision- Test setup flow changes or Aha moment triggers/metrics-framework- Define leading indicators (setup rate, Aha rate as early signals)
Pulls from:
/user-research-synthesis- User feedback on onboarding struggles/retention-analysis- Understand habit formation patterns/competitor-analysis- How competitors handle onboarding/expansion-strategy- Activation enables expansion (activated users more likely to expand)
Key Questions to Revisit
After defining Setup → Aha → Habit, ask:
- Is our Aha moment definition based on DATA (retained vs churned behavior)?
- What's the time-to-Aha for our fastest 10% of users (that's the optimized path)?
- Do we have different Aha moments for different user segments (solo vs team)?
- Is our setup flow truly minimal, or are we collecting unnecessary info upfront?
Related Skills
user-research-synthesis- Understand user struggles in onboarding, synthesis of feedbackexperiment-decision- Test activation improvements and measure impactretention-analysis- Measure habit formation (Aha → habit stage)prd-draft- Build features to improve activation based on this analysismetrics-framework- Define leading indicators of activation successexpansion-strategy- Activation enables expansion (prerequisite)define-north-star- Align activation metrics to North Star
Structured Output Template
When delivering an activation analysis, use this consistent format:
# Activation Analysis: [Product/Feature Name]
**Date:** [Date]
**Analyst:** [PM Name]
---
## Executive Summary
[1-2 sentences: Current activation rate, biggest bottleneck, recommended action]
## Current Activation Funnel
| Stage | Definition | Rate | Benchmark | Gap |
| ---------------------- | -------------- | ----- | --------- | ------------------ |
| Signup → Setup | [actions] | \_\_% | 70%+ | [+/- vs benchmark] |
| Setup → Aha | [actions] | \_\_% | 50%+ | [+/- vs benchmark] |
| Aha → Habit | [actions] | \_\_% | 30%+ | [+/- vs benchmark] |
| **Overall Activation** | Signup → Habit | \_\_% | 15%+ | [+/- vs benchmark] |
**Time to Aha (median):** \_\_ minutes
**Biggest bottleneck:** [Stage with largest drop]
## Stage Definitions
- **Setup:** [Specific actions for your product]
- **Aha:** [Specific moment/action for your product]
- **Habit:** [Specific recurring behavior for your product]
## Bottleneck Diagnosis
[Root cause of biggest drop-off: friction, confusion, wrong users, missing value]
## Segment Differences
| Segment | Setup Rate | Aha Rate | Habit Rate | Insight |
| ----------- | ---------- | -------- | ---------- | --------- |
| [Segment 1] | \_\_% | \_\_% | \_\_% | [insight] |
| [Segment 2] | \_\_% | \_\_% | \_\_% | [insight] |
## Recommendations (Prioritized)
1. **[Fix 1]** - Expected impact: +\_\_% on [stage] rate
2. **[Fix 2]** - Expected impact: +\_\_% on [stage] rate
3. **[Fix 3]** - Expected impact: +\_\_% on [stage] rate
## Next Steps
- [ ] [Action 1] - Owner: [name] - Due: [date]
- [ ] [Action 2] - Owner: [name] - Due: [date]
Output Quality Self-Check
Before delivering the activation analysis, verify:
- All three stages (Setup, Aha, Habit) are defined with specific, measurable actions for this product
- Aha moment is based on data (retained vs churned behavior), not opinion
- Rates calculated for each stage with clear numerator/denominator
- Biggest bottleneck is identified and the diagnosis explains WHY (not just where)
- Time to Aha is measured or estimated
- Segment differences are analyzed (at least new vs existing, or by acquisition channel)
- Recommendations are specific, prioritized, and tied to the bottleneck
- Benchmarks are included for context (industry-appropriate, not generic)
- Connected to retention -- does activation actually predict D30 retention?
- No generic advice -- all recommendations reference this specific product and data
Framework credit: Adapted from Aakash Gupta's activation frameworks. Read the full articles: