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/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:

User Segmentation#

Automatic Segmentation#

Helicone 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 historyconst 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 segmentsconst 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:

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.