User Metrics & Analytics

Analyze how users interact with your AI features through comprehensive user metrics. Track engagement patterns, identify power users, understand usage trends, and optimize experiences based on real user behavior data.

Key User Metrics

User Identification & Tracking

Setting User IDs

Track users across sessions and requests:

await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Hello!" }],
  headers: {
    "Cova-User-Id": "user-12345"
  }
});

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
    extra_headers={
        "Cova-User-Id": "user-12345"
    }
)

curl https://gateway.corevalue.dev/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COVA_API_KEY" \
  -H "Cova-User-Id: user-12345" \
  -d '{"model": "gpt-4o", "messages": [...]}'

User Properties

Enrich user data with additional context:

// Add user segmentation data
{
  headers: {
    "Cova-User-Id": "user-12345",
    "Cova-Property-UserTier": "premium",
    "Cova-Property-UserType": "business",
    "Cova-Property-SignupDate": "2024-01-15",
    "Cova-Property-Industry": "healthcare"
  }
}

User Behavior Analytics

Usage Patterns

Understand how users interact with your AI:

{
  "user_behavior": {
    "avg_requests_per_day": 24,
    "peak_usage_hours": [9, 14, 16],
    "session_length_avg": "12 minutes",
    "favorite_features": ["chat", "summary", "analysis"],
    "model_preferences": ["gpt-4o", "anthropic/claude-3.5-sonnet-v2"]
  }
}

Engagement Metrics

Track how engaged users are with your AI features:

User Segmentation

Automatic Segmentation

CoreValue automatically groups users based on behavior:

Custom Segmentation

Create segments based on your business logic:

// Business tier segmentation
{
  "free_tier": {
    "monthly_request_limit": 1000,
    "features": ["basic_chat"],
    "support_level": "community"
  },
  "pro_tier": {
    "monthly_request_limit": 10000,
    "features": ["chat", "analysis", "summaries"],
    "support_level": "email"
  },
  "enterprise_tier": {
    "monthly_request_limit": "unlimited",
    "features": ["all"],
    "support_level": "priority"
  }
}

User Journey Analysis

Onboarding Analytics

Track how new users adopt your AI features:

Usage Evolution

Track how user behavior changes over time:

{
  "user_evolution": {
    "week_1": {
      "requests_per_day": 3,
      "avg_session_length": "5 min",
      "features_used": ["chat"],
      "satisfaction_score": 7.2
    },
    "week_4": {
      "requests_per_day": 12,
      "avg_session_length": "15 min", 
      "features_used": ["chat", "analysis", "summary"],
      "satisfaction_score": 8.7
    },
    "week_12": {
      "requests_per_day": 28,
      "avg_session_length": "22 min",
      "features_used": ["all_features"],
      "satisfaction_score": 9.1
    }
  }
}

Cohort Analysis

User Cohorts

Group users by signup date to track retention:

Retention Insights

Understand what drives long-term usage:

• High retention features - Features that keep users coming back
• Churn indicators - Behaviors that predict user departure
• Activation thresholds - Usage levels that predict retention
• Seasonal patterns - How retention varies by time of year

User Experience Metrics

Quality Indicators

Measure the quality of AI interactions:

Friction Points

Identify where users struggle:

{
  "friction_analysis": {
    "high_retry_requests": {
      "feature": "document_analysis",
      "retry_rate": 23,
      "common_issues": ["format_errors", "timeout"]
    },
    "abandoned_sessions": {
      "avg_abandonment_point": "4th message",
      "common_patterns": ["long_wait_time", "unclear_response"]
    },
    "error_hotspots": {
      "rate_limits": "15% of power users affected",
      "model_errors": "2.3% of requests fail"
    }
  }
}

Personalization Insights

User Preferences

Track individual user preferences:

• Preferred models - Which models users choose most often
• Communication style - Formal vs casual interaction patterns
• Feature usage - Which features each user finds valuable
• Session timing - When users are most active

Adaptive Experiences

Use metrics to personalize experiences:

// Personalized model selection based on user history
const getUserPreferredModel = (userId: string) => {
  const userMetrics = getUserMetrics(userId);
  
  if (userMetrics.prefers_speed) {
    return "gpt-4o-mini,google/gemini-flash";
  }
  
  if (userMetrics.prefers_quality) {
    return "anthropic/claude-3.5-sonnet-v2,gpt-4o";
  }
  
  return "gpt-4o-mini,anthropic/claude-3.5-haiku";
};

Comparative Analytics

User Benchmarking

Compare user performance against benchmarks:

• Usage vs peers - How users compare to similar cohorts
• Efficiency metrics - Requests per goal achieved
• Feature adoption - Adoption rate vs typical users
• Satisfaction vs average - Experience quality comparison

A/B Testing

Test improvements with user metrics:

// A/B test new feature with user segments
const experimentVariant = getUserExperiment(userId, 'new_chat_ui');

if (experimentVariant === 'variant_a') {
  // Show improved chat interface
  return <NewChatUI />;
} else {
  // Show current interface  
  return <CurrentChatUI />;
}

// Test model preference by user segment
const modelTest = getUserExperiment(userId, 'model_selection');

const model = modelTest === 'claude_first' 
  ? "anthropic/claude-3.5-sonnet-v2,gpt-4o"
  : "gpt-4o,anthropic/claude-3.5-sonnet-v2";

await client.chat.completions.create({ model, messages });

User Lifecycle Management

Lifecycle Stages

Track users through their journey:

Reporting & Insights

Automated Reports

Receive regular user analytics:

• Daily user activity - Active users and key metrics
• Weekly trends - User behavior patterns and changes
• Monthly insights - Deep analysis and recommendations
• Quarterly reviews - Strategic insights and planning

Custom Dashboards

Create views tailored to your needs:

Privacy & Compliance

Data Privacy

Protect user privacy while gathering insights:

• Anonymized analytics - Remove personally identifiable information
• Consent management - Respect user privacy preferences
• Data retention - Automatic cleanup of old user data
• Compliance reporting - GDPR, CCPA, and other regulations

Ethical Considerations

Responsible user analytics practices:

• Transparent data usage - Clear communication about data collection
• User benefit focus - Use insights to improve user experience
• Bias detection - Monitor for unfair treatment of user segments
• Opt-out options - Allow users to limit data collection

Next Steps

User metrics provide crucial insights for building successful AI products. Use this data to understand user needs, optimize experiences, and drive product growth.