Guide Product Decisions With Metrics

Business Context

NotionFlow is a mobile productivity app with 2.4M monthly active users. The product team launched a redesigned onboarding flow and a new habit-reminder feature six weeks ago. Since then, installs have increased 18%, but leadership is unsure whether the changes improved product health because key outcomes moved in different directions.

Metric Scenario

In the last full month, visitor-to-signup conversion increased from 24% to 28%, Day 1 activation increased from 61% to 66%, and average weekly sessions per active user rose from 5.2 to 5.8. However, Day 30 retention fell from 34% to 29%, paid conversion from active user to subscriber stayed flat at 4.1%, and support tickets per 1,000 new users increased from 22 to 31.

The Head of Product asks how you would use data to decide whether to keep investing in the new onboarding and reminder experience, roll back parts of it, or prioritize fixes elsewhere.

Requirements

1. Define the primary KPI you would use to guide the product decision and explain why.
2. Identify 3-5 supporting metrics and classify them as leading indicators, lagging indicators, or guardrails.
3. Decompose the KPI movement to determine whether the launch created real value or only improved top-of-funnel activity.
4. Explain what user segments, funnel stages, and time windows you would analyze first.
5. Recommend a decision framework for whether to iterate, scale, or roll back the changes.

Data Available

• web_visits: visitor_id, landing_page, traffic_source, visit_timestamp
• signups: user_id, signup_timestamp, acquisition_channel, device_type, country
• onboarding_events: user_id, step_name, step_timestamp, completed_flag
• app_sessions: user_id, session_start, session_end, platform, feature_used
• subscriptions: user_id, plan_type, trial_start, paid_start, cancellation_date
• support_tickets: user_id, created_at, ticket_type, severity