Business Context

Notion is launching AI Meeting Notes, a new feature that records meetings, generates summaries, and creates action items. The feature will launch first to 200,000 existing workspace users in the US, with a goal of improving product adoption and expanding paid plan upgrades.

In the 4-week beta, 48,000 users were exposed to the feature, 14,400 clicked into the setup flow, 9,600 completed setup, and 5,280 used the feature in at least one meeting. Of those activated users, 2,100 used it again in week 2, and 1,320 were still active in week 4. Beta users who adopted the feature had 18% higher weekly workspace retention than non-adopters, but support tickets per 1,000 users rose from 6 to 11. Leadership is asking which metrics should define launch success and which should be treated as guardrails.

Requirements

1. Define the single most important launch metric for AI Meeting Notes and explain why it should be prioritized over volume-based metrics.
2. Propose a metric hierarchy with north star, primary KPI, leading indicators, and guardrail metrics.
3. Show how you would evaluate the launch across the funnel: exposure, setup, activation, repeat usage, retention, and monetization.
4. Identify which metrics matter most in the first 30 days versus the next 2 quarters.
5. Recommend what threshold values would make you call the launch successful, concerning, or failing.

Data Available

• feature_exposure_events: user_id, workspace_id, timestamp, experiment_bucket, entry_point
• meeting_notes_events: user_id, workspace_id, meeting_id, event_type, timestamp, duration_minutes
• workspace_activity_daily: workspace_id, date, active_users, docs_created, comments, integrations_used
• billing_subscriptions: workspace_id, plan_tier, seats, mrr, upgrade_date
• support_tickets: ticket_id, workspace_id, user_id, created_at, issue_type, resolution_time