--- name: activation-analysis description: Analyze user activation using Setup → Aha → Habit framework. Identifies activation bottlenecks. disable-model-invocation: false user-invocable: true --- # 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: | Source | Files/Folders | Search Terms | What to Extract | |--------|---------------|--------------|-----------------| | Metrics/Analytics | `context-library/metrics/*.md` | "onboarding", "setup", "activation", D7, D30, "time to value", TTV | Current activation rates by stage, onboarding metrics, D7/D30 retention | | User Research | `context-library/research/*.md` | "onboarding", "setup", "first time", "confused", "stuck", "struggle" | User feedback on onboarding, confusion points, success moments | | Meeting Notes | `context-library/meetings/*.md` | "activation", "onboarding", "new users", "drop-off", "support tickets" | CS/support feedback on where users get stuck, win/loss reasons | | PRDs | `context-library/prds/*.md` | "onboarding", "activation", "tutorial", "first-time user" | Past onboarding improvements, features to drive activation | | Business Info | `context-library/business-info-template.md` | target user, customer segment, use case, primary value | Who you're activating, what value matters to them | **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: | 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: `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: ```markdown # 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