AI usage analytics are harder than SaaS analytics and more consequential for product decisions. Feature engagement, user value, cohort behavior — each requires different measurement approaches. This post is the patterns we use to track AI usage, extract useful signal from noisy data, and drive product decisions from the analytics.
Engagement layer
DAU, WAU, MAU by feature. Engagement trends by AI feature — which are sticky, which are tried and abandoned?
Session depth. How many AI interactions per session? How long do sessions last? Deep sessions indicate real value.
Feature adoption curves. Day 1 to Day 30 adoption trajectories. Features that require learning show distinct adoption curves.
Repeat usage patterns. Users who return weekly, daily. Retention by cohort, by feature, by persona.
Value layer
Task completion rate. For action-oriented AI features, what percent of attempts succeed? Lower completion = quality problems or UX problems.
User-reported satisfaction. Thumbs up/down, NPS, targeted surveys. Periodic calibration — users under-report issues, over-report major wins.
Outcome metrics. Did the user accomplish what they came for? Sometimes inferred (didn't retry, moved on to next task) rather than reported.
Quality correlation. Does engagement with feature X correlate with broader product retention? Some features drive retention; others don't.
Cohort layer
Retention curves. What percent of users who used feature X week 1 still use it month 3? Sharp dropoffs signal problems.
Persona segmentation. Heavy users, light users, lapsed users. Behavior patterns differ. Treatment should too.
Expansion signals. Users trying more features, deeper workflows, inviting teammates. Precede revenue expansion.
Churn signals. Declining engagement, fewer features used, support tickets. Predicts cancellation weeks out.
Analytics stack
Event tracking. Segment, Amplitude, Mixpanel, or custom. Capture every meaningful user interaction.
Warehouse. Snowflake, BigQuery, Databricks. Store events for flexible analysis.
Visualization. Looker, Mode, Hex, custom dashboards. Different personas need different views.
Reverse ETL to ops tools. Pipe analytics insights back into CRM, support, email tools. Action follows insight.
Privacy and compliance
PII handling. Don't log raw user inputs or outputs if they contain PII. Hash or anonymize where appropriate.
GDPR and CCPA. User data access, deletion requests, data localization. Build for compliance from day one.
Enterprise customers. Zero data retention options for sensitive data. Configure accordingly.
Common analytics pitfalls
Vanity metrics. Pageviews, signups, registered users. Meaningful only if tied to retention and revenue. See quality monitoring post.
Aggregated metrics hiding segments. Averages mask dramatic variation. Segment before drawing conclusions.
Correlation vs causation. Feature X use correlates with retention; doesn't mean feature X causes retention. A/B testing required for causal claims.
Data drift. Instrumentation changes; metrics definitions shift. Historical comparisons become unreliable. Document changes.
Acting on analytics
Weekly business review. Key metrics, trends, anomalies. Cross-functional attendance.
Monthly deep dive. Cohort analysis, feature performance, retention analysis. Surface insights for product decisions.
Quarterly strategic review. Bigger picture patterns inform roadmap. Where is the product heading?
Analytics doesn't produce decisions. People do. Analytics reduces uncertainty; judgment makes the call.