User Analytics for AI Native Sites: A Comprehensive Guide

7th Jun 2025

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:

Essential Analytics Framework

1. User Engagement

Track users through clear lifecycle stages:

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:

3. Technical Performance

Monitor the AI's performance:

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

Technical Metrics

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:

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:

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.