growth-analytics

name	growth-analytics
description	Analytics setup, conversion tracking, growth metrics, and data-driven optimization for RunSmart. Covers event tracking, funnel analysis, cohort metrics, retention curves, and experiment frameworks. Use when setting up analytics, analyzing user behavior, or designing growth experiments.
metadata	{"short-description":"Track, measure, and optimize RunSmart growth with analytics and experiments."}

When Claude should use this skill

• Setting up analytics tracking for new features
• Analyzing user funnels (onboarding → plan → run → retention)
• Designing A/B tests or growth experiments
• Creating dashboards or metric reports
• User asks "how are we doing" or "what should we measure"

Core Metrics (North Star Framework)

North Star Metric

Weekly Active Runners — users who complete at least 1 tracked run per week.

Input Metrics

Metric	Definition	Target
Onboarding completion	% of new users who finish onboarding	70%
Plan generation rate	% of onboarded users who generate a plan	60%
First run rate	% of plan holders who record first run	50%
Weekly retention (W1)	% returning in week 1	40%
Monthly retention (M1)	% returning in month 1	25%

Revenue Metrics (Future)

Metric	Definition	Target
Free → Premium conversion	% upgrading to paid tier	5%
ARPU	Average revenue per user	$3/month
LTV	Lifetime value	$36
CAC	Customer acquisition cost	< $5

Event Tracking Schema

// Core events to track
interface AnalyticsEvent {
  event: string;
  properties: Record<string, string | number | boolean>;
  timestamp: Date;
  userId?: number;
  sessionId: string;
}

// Key events
const EVENTS = {
  // Onboarding
  'onboarding_started': {},
  'onboarding_step_completed': { step: number, stepName: string },
  'onboarding_completed': { goal: string, experience: string },

  // Plans
  'plan_generated': { planDuration: number, sessionCount: number },
  'plan_viewed': {},
  'workout_viewed': { sessionType: string },

  // Runs
  'run_started': {},
  'run_completed': { distanceKm: number, durationMin: number, avgPace: string },
  'run_abandoned': { :  },

  
  : {},
  : { :  },

  
  : { :  |  },
  : { :  },
  : { :  },

  
  : { :  },
  : {},
  : {},
};

Funnel Analysis

Activation Funnel

Visit landing page
  → Install PWA / Sign up
    → Complete onboarding
      → Generate first plan
        → Complete first run
          → Return in week 2

Engagement Loop

Open app → View today's workout → Record run → See insights →
Get next workout → (repeat)

Growth Experiment Framework

Experiment Template

## Experiment: [Name]
**Hypothesis:** If we [change], then [metric] will [improve/increase] by [X%]
**Metric:** [Primary metric to move]
**Audience:** [Who sees this]
**Duration:** [How long to run]
**Success criteria:** [Statistical significance threshold]
**Implementation:** [What to build]
**Rollback plan:** [How to revert]

High-Impact Experiment Ideas for RunSmart

1. Social proof on landing: Show "X runners trained this week"
2. Simplified onboarding: 3 questions vs 7 questions
3. Push notification timing: Morning of workout day vs evening before
4. AI coach personality: Encouraging vs data-driven tone
5. Streak mechanics: Daily streak badge vs weekly consistency score

Retention Strategies

• Day 1: Welcome push notification with first workout
• Day 3: "How was your first run?" prompt
• Day 7: Weekly summary with progress visualization
• Day 14: Plan check-in, offer adjustment
• Day 30: Achievement badge, share prompt
• Lapsed: Re-engagement email with updated plan offer

Agent Team Pattern: Analytics Sprint

Create an agent team for analytics setup:
- Tracking teammate: implement event tracking in codebase
- Dashboard teammate: design metric queries and visualization
- Experiment teammate: set up A/B test infrastructure
Lead synthesizes into analytics implementation plan.

Integration Points

• Events: Custom analytics module in V0/lib/analytics.ts
• Storage: IndexedDB for local event buffer
• Reporting: Vercel Analytics, custom dashboard
• A/B tests: Feature flags in app config