Course Objective & Scope
Traditional product metrics like page views, session length or total messages sent can be deeply misleading in AI products: a user sending 15 messages in a row often signals frustration, not engagement. This course teaches an AI product metrics framework that links model quality, user behaviour and business value.
Participants define acceptance rate, edit distance, first-attempt resolution, abandonment signals and cost per successful task for their own product, leaving with a complete product metric hierarchy and measurement plan.
Who Should Attend?
- Product managers (PMs), product analysts and growth teams
- Data scientists and AI product leaders
- Digital transformation managers measuring ROI on internal AI tools
- No coding required; basic product analytics familiarity is sufficient
Key Takeaways
- Separate novelty-driven 'AI tourism' from habitual, value-generating retention
- Measure output acceptance rate, copy/export actions and edit distance before commit
- Extract implicit satisfaction and frustration signals from conversation and agent logs
- Connect offline/online model evals (faithfulness, latency) to business KPIs (conversion, CSAT, time saved)
- Track unit economics via cost per successfully completed task rather than raw cost per token
Curriculum
Why Traditional Product Analytics Mislead in AI
- The 'more messages = more engagement' fallacy and the novelty spike
- Structuring the hierarchy across product outcomes, user signals and model evals
Task Success and Implicit Behavioural Signals
- Acceptance rate, edit distance before commit and copy/export/share actions
- Prompt reformulation, immediate undo and session abandonment indicators
Conversation and Agent Funnel Analytics
- Clustering user intents, unresolved topics and multi-step agent drop-offs
- Correcting for selection bias in explicit thumbs-up/thumbs-down feedback
Linking Model Evals, Latency and Unit Economics
- Quantifying how hallucination rates and p95 latency impact cohort retention
- Calculating Cost per Successful Task and monitoring gross margin
Workshop: AI Metric Tree and Instrumentation Plan
- Defining North Star, driver and guardrail metrics for your product
- Delivering the product metrics specification and telemetry plan
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