Build Data-Driven Decision Metrics

Business Context

NotionFlow is a B2B SaaS workflow product used by 45,000 weekly active teams. The COO believes product, sales, and customer success teams are making decisions based on anecdotes because each function uses different dashboards and success definitions.

Metric Scenario

In the last quarter, revenue grew 6%, but logo churn increased from 2.8% to 3.6% monthly, activation fell from 58% to 51%, and weekly active teams were flat at ~45,000 despite a 20% increase in new signups. Product argues feature adoption is improving, Sales points to strong pipeline conversion, and Customer Success says low-usage accounts are expanding support volume. Leadership wants a company-wide framework to ensure decisions are consistently data-driven rather than opinion-driven.

Requirements

1. Define a primary company-level metric and 3-5 supporting KPIs that should guide decision-making across teams.
2. Explain how you would distinguish leading vs lagging indicators and how each team should use them.
3. Show how you would decompose the primary metric to diagnose whether the current problem is acquisition quality, activation, engagement, or retention.
4. Propose a decision-making cadence: who reviews which metrics, how often, and what thresholds trigger action.
5. Identify common failure modes that make teams think they are data-driven when they are not.

Data Available

• accounts: account_id, signup_date, plan_type, company_size, acquisition_channel, region
• workspace_events: account_id, user_id, event_date, event_name, project_created, task_completed, integration_connected
• subscriptions: account_id, mrr, contract_start_date, renewal_date, cancellation_date, expansion_mrr
• support_tickets: account_id, created_at, ticket_type, resolution_time_hours, csat_score
• sales_funnel: lead_id, account_id, source, demo_booked_at, opportunity_created_at, closed_won_at