AI Product Metrics

Why AI Products Need Different Metrics#

1. Interaction Complexity

   • Multi-turn conversations vs. single actions
   • Quality assessment challenges
   • User dependency and habit formation
   • Value realization over time

2. Engagement Patterns

   • Conversational depth and frequency
   • Task completion rates
   • User learning curves
   • Feature discovery and adoption

3. Value Measurement

   • Problem-solving effectiveness
   • Time savings and productivity gains
   • Knowledge acquisition and skill development
   • Creative and analytical assistance

Traditional vs. AI Metrics#

Traditional Software Metrics:#

• Daily Active Users (DAU)
• Monthly Active Users (MAU)
• Session Duration
• Feature Adoption Rate
• Conversion Rate

AI-Specific Metrics:#

• Conversation Quality Score
• Task Completion Rate
• User Satisfaction Index
• Retention by Usage Pattern
• AI Dependency Ratio