User Analytics for AI Native Sites: A Comprehensive Guide

Introduction

Traditional web analytics focus on passive metrics like page views and session duration. But AI-powered applications are different - they're about creation, not consumption. Using chefgptapp.com as our example, let's explore how to measure what really matters in AI-native applications.

The New Analytics Mindset

The core metric for AI sites is simple: generations. Every time a user prompts the AI and gets a response, that's value being created. This shift from passive browsing to active creation needs different metrics:

• Generation Frequency: How often users create with AI
• Quality Engagement: Depth and complexity of interactions
• Return Patterns: Are users getting enough value to come back?
• Feature Adoption: Which AI capabilities resonate most?

Essential Analytics Framework

1. User Engagement

Track users through clear lifecycle stages:

• New users (first generation)
• Active users (regular generations)
• Power users (high frequency)
• Churned users (stopped generating)

The key is measuring movement between these stages to understand product health.

2. User Behavior Patterns

enum UserSignInType {
  EMAIL_VERIFICATION    // Verified email users
  ANONYMOUS_CREATION    // New anonymous users
  ANONYMOUS_RETURN      // Returning anonymous users
  TOKEN_REFRESH         // Session maintenance
  SESSION_RESTORATION   // Returning verified users
}

Track key behavioral indicators:

• First-time vs returning users
• Days between visits
• Sign-in streaks
• Cross-device usage

3. Technical Performance

Monitor the AI's performance:

• Generation success rates
• Response times
• Error patterns
• User satisfaction
• Infrastructure costs

Anonymous-First Strategy

For B2C AI sites, anonymous users are crucial. Let them experience value instantly, without signup barriers. Track them using device fingerprinting:

model User {
  isAnonymous  Boolean  @default(false)
  deviceId     String?  @unique
  createdAt    DateTime @default(now())
  generations  Generation[]
}

Focus on converting anonymous users to verified accounts through value demonstration, not friction.

Bot Management Made Simple

Instead of complex bot blocking, redirect them to marketing pages:

if (isBotOrScraper(request)) {
  redirect('/marketing-landing');
} else {
  serveAIInterface();
}

This preserves SEO while protecting your AI endpoints.

Key Performance Indicators (KPIs)

Primary Metrics

• Monthly Active Generators (MAG)
• Average Generations Per User (AGPU)
• Generation Success Rate
• Time to First Generation (TTFG)
• Day 1, 7, 30 retention rates

Technical Metrics

• Generation latency (P95, P99)
• Error rates
• Cost per generation
• User capacity

Implementation Architecture

Track everything with a solid data model:

model Generation {
  id           String   @id @default(cuid())
  userId       String
  recipe       String   @db.Text
  createdAt    DateTime @default(now())
  calories     Int?
  prepTime     Int?
}

model UserSignInActivity {
  id                    String         @id @default(cuid())
  userId                String
  signInType            UserSignInType
  deviceInfo            Json?
  isFirstTimeSignIn     Boolean        @default(false)
  daysSinceLastSignIn   Int?
  signInStreak          Int?
}

Advanced Analytics

Cohort Analysis

Focus on generation-based cohorts instead of registration dates. Group users by:

• First generation date
• Usage frequency
• Feature adoption
• Cross-device patterns

Predictive Analytics

Identify patterns to prevent churn and forecast growth:

SELECT userId, daysSinceLastSignIn, 
       COUNT(generation_id) as total_generations
FROM users u
LEFT JOIN generations g ON u.id = g.userId
WHERE daysSinceLastSignIn > 7
GROUP BY userId

Real-World Success Metrics

For chefgptapp.com, success looks like:

• 2-3 generations per session
• 40%+ users return within 7 days
• 95%+ successful recipe generations
• Users engaging with multiple features

Conclusion

AI-native analytics is about measuring creation, not consumption. Focus on:

1. Generations over page views
2. Value creation before identity
3. Smart bot management
4. Proactive pattern recognition

Build your analytics to understand and optimize AI-human collaboration. That's where the real value lies.