Data Review Template for Product Analytics

Weekly for teams actively iterating on growth or engagement metrics. Biweekly for teams in a maintenance or infrastructure phase where metrics move slowly. The [RICE framework](/frameworks/rice-framework) can help you prioritize which investigations and experiments to focus on when your team has limited capacity.

Rotate the facilitator role monthly across the team. This builds data literacy: engineers who facilitate learn to read dashboards, designers who facilitate learn to ask metric-informed questions. The PM or analyst should be the default when nobody else is available. Avoid having the same person facilitate every week because it creates a single point of failure and keeps the rest of the team passive.

The PM can fill this role for small teams. Use self-serve analytics tools (Amplitude, Mixpanel, PostHog) to pull metrics without writing SQL. Pre-build the dashboard once and refresh the data before each meeting. For deeper investigations, schedule focused analysis time in your sprint. The [Product Analytics Handbook](/analytics-guide) has practical guidance on self-serve analytics for teams without dedicated analysts.

Set a decision threshold before investigating. "If the onboarding drop is greater than 5 pp and persists for 2 weeks, we will redesign step 2." This prevents infinite investigation loops. Time-box every investigation to 7 minutes in the meeting. If you cannot reach a conclusion, the action item is to gather more data, not to discuss for another 20 minutes. Use the [glossary definition of statistical significance](/glossary/prioritization) to set clear stopping criteria for experiments.

No. Keep the meeting to the core team (PM, eng lead, designer, analyst). Stakeholders should receive the post-meeting summary. Stakeholder presence changes the dynamic: the team starts performing instead of problem-solving. If a stakeholder needs to be in the loop, schedule a separate 15-minute briefing. ---