User Metrics & Analytics

Understand user behavior, track engagement patterns, and optimize AI experiences with detailed user 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

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:

TypeScript

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 Metrics

• 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:

First Session Analysis

Key Metrics:

• Time to first request
• First request success rate
• Features discovered in first session
• Session length and engagement

Optimization Goals:

• Reduce time to value
• Increase first-session success
• Guide feature discovery

Activation Milestones

Milestone Tracking:

• First successful request
• First multi-turn conversation
• First use of advanced features
• First week retention

Success Indicators:

• Users reaching activation milestones
• Time to reach each milestone
• Drop-off points in journey

Feature Adoption

Adoption Funnel:

• Users aware of feature
• Users who try feature
• Users who adopt feature regularly
• Users who become power users

Insights:

• Which features drive retention
• Barriers to feature adoption
• Optimal feature introduction timing

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:

Cohort	Week 1	Week 2	Week 4	Week 8	Week 12
Jan 2024	100%	78%	65%	52%	48%
Feb 2024	100%	82%	71%	58%	54%
Mar 2024	100%	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:

Feature Test

// 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:

Acquisition

• 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.