Activation Analysis

Activation Analysis: Setup → Aha → Habit Framework

Quick Start

/activation-analysis

Then provide:

1. Your product and core value proposition (or I'll pull from business-info)
2. Current onboarding flow (what steps do new users take?)
3. 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 outputs/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:

Context Priority:

1. Internal context FIRST (business info, existing activation metrics, user research)
2. Analytics MCP SECOND (if connected - query activation funnel, D7/D30 by cohort)
3. 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:

• context-library/business-info-template.md for your product and target users
• context-library/metrics/ for existing activation metrics and onboarding data
• context-library/research/ for user research on onboarding struggles
• context-library/meetings/ for CS/support feedback on where users get stuck
• context-library/prds/ for past onboarding improvements

[If analytics MCP connected]: "Let me also query [Amplitude/Mixpanel] 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:

1. Setup: User configures the product to work for them
2. Aha: User experiences the core value (magic moment)
3. 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
• Figma: 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:

1. Look at retained vs churned users
2. What did retained users do that churned users didn't?
3. 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
• Figma: 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 context-library/business-info-template.md

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:

1. 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

2. 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...")

3. 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

4. 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:

1. Eliminate unnecessary steps

   • Question every field in signup
   • Can it happen later? Move it later.

2. Provide shortcuts

   • Sample data/templates
   • Import from competitors
   • AI-generated starting points

3. Progressive disclosure

   • Show advanced features AFTER Aha
   • Don't overwhelm new users

4. 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:

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: outputs/analyses/activation-analysis-[date].md
• When finalized, move to: context-library/research/activation-[product].md

Onboarding Improvements:

• Create PRD in context-library/prds/ for each onboarding change
• Link this activation analysis as context
• Track changes in the PRD's success metrics section

Activation Metrics:

• Update context-library/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 feedback
• experiment-decision - Test activation improvements and measure impact
• retention-analysis - Measure habit formation (Aha → habit stage)
• prd-draft - Build features to improve activation based on this analysis
• metrics-framework - Define leading indicators of activation success
• expansion-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:

• https://www.news.aakashg.com/p/ultimate-guide-activation
• https://www.news.aakashg.com/p/how-to-measure-onboarding-advanced