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

Active Users

Daily, weekly, and monthly active users Track user growth and retention trends

Session Analytics

Session length, depth, and engagement Understand conversation patterns

Usage Patterns

Request frequency, timing, and features used Identify most valuable use cases

User Satisfaction

Feedback scores, retry rates, and completion rates Measure AI experience quality

​
User Identification & Tracking

​
Setting User IDs

Track users across sessions and requests: 
await client.chat.completions.create({
  model: "gpt-4o/openai",
  messages: [{ role: "user", content: "Hello!" }],
  headers: {
    "Helicone-User-Id": "user-12345"
  }
});

​
User Properties

Enrich user data with additional context: 
// Add user segmentation data
{
  headers: {
    "Helicone-User-Id": "user-12345",
    "Helicone-Property-UserTier": "premium",
    "Helicone-Property-UserType": "business",
    "Helicone-Property-SignupDate": "2024-01-15",
    "Helicone-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/openai", "claude-3.5-sonnet-v2/anthropic"]
  }
}

​
Engagement Metrics

Track how engaged users are with your AI features:
  • Session duration - Time spent in conversations
  • Messages per session - Conversation depth
  • Return sessions - Users coming back within 24h
  • Session completion rate - Conversations finished vs abandoned

​
User Segmentation

​
Automatic Segmentation

Helicone automatically groups users based on behavior:

Power Users

High request volume, long sessions Top 10% of users by usage

Casual Users

Moderate usage, shorter sessions Majority of user base

New Users

Recent signups, learning patterns First 30 days of usage

At-Risk Users

Declining usage, potential churn Require retention efforts

​
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:
CohortWeek 1Week 2Week 4Week 8Week 12
Jan 2024100%78%65%52%48%
Feb 2024100%82%71%58%54%
Mar 2024100%85%74%61%-

​
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:

Success Rate

Percentage of requests that achieve user goals Track by user segment and feature

Response Quality

User ratings and feedback scores Automated quality assessments

Task Completion

Rate of successful task completion Multi-step workflow success rates

User Satisfaction

Overall satisfaction scores Net Promoter Score (NPS) tracking

​
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/openai,gemini-flash/google";
  }
  
  if (userMetrics.prefers_quality) {
    return "claude-3.5-sonnet-v2/anthropic,gpt-4o/openai";
  }
  
  return "gpt-4o-mini/openai,claude-3.5-haiku/anthropic";
};

​
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 />;
}

​
User Lifecycle Management

​
Lifecycle Stages

Track users through their journey:
  • Source tracking - How users discovered your AI
  • First interaction - Initial experience quality
  • Onboarding completion - Setup and first success

​
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:

Product Dashboard

User engagement, feature adoption, satisfaction Focus on product-market fit metrics

Growth Dashboard

Acquisition, activation, retention metrics Track growth funnel performance

Support Dashboard

User issues, friction points, satisfaction Optimize user support and experience

Business Dashboard

Revenue per user, lifetime value, churn Business impact and financial metrics

​
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

Set Up User Tracking

Implement user IDs and session tracking

Custom Properties

Add user segmentation and metadata

Feedback Collection

Gather user satisfaction data

A/B Testing

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