Behavioral Analytics Template for PMs

Product analytics is the broader discipline that includes behavioral analytics. Behavioral analytics specifically focuses on what users do inside the product: their actions, sequences, and patterns. Product analytics also covers acquisition metrics, revenue metrics, and infrastructure metrics that may not involve direct user behavior.

Most early-stage SaaS products need 15-30 core events. Mid-stage products (Series A-B) typically track 40-80 events. Enterprise products may track 200+. The key is not the count but the coverage: you need at least one event per stage of your core user journey (activation, engagement, monetization, retention). Start lean and add events when you have a specific question they would answer.

Run a correlation analysis between early user actions (within the first 7 days) and 30-day or 60-day retention. The [correlation analysis template](/templates/correlation-analysis-template) provides a structured framework for this. Look for actions with a Pearson correlation coefficient above 0.5 with your retention metric. Then validate with a controlled experiment before redesigning your product around the finding.

No. Track actions that represent meaningful user intent, not every UI interaction. A button click that creates a report is meaningful. A button click that opens a dropdown menu is noise. If you are unsure whether an interaction is meaningful, ask: "Would knowing the count and frequency of this action change any product decision?" If not, skip it.

Refresh segments daily if you use them for in-app messaging or triggered emails. Refresh weekly if you use them only for analysis and reporting. The key constraint is your data pipeline's latency and cost. Real-time segmentation matters for intervention-based use cases (churn prevention nudges). Batch segmentation is fine for strategic planning.