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:
- 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:
- Generations over page views
- Value creation before identity
- Smart bot management
- Proactive pattern recognition
Build your analytics to understand and optimize AI-human collaboration. That's where the real value lies.