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Measure Impact of Onboarding Redesign

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Your question is Measure Impact of Onboarding Redesign. Take a moment with it on the right.

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Problem

Business Context

FinMate is a personal finance app with 2.4M monthly active users. Six weeks ago, the product team launched a redesigned onboarding flow and a new weekly insights email. The Head of Product asks you to explain the impact of this work using metrics, reporting, and any strategic changes you would recommend.

Metric Scenario

Before the launch, new-user activation was 41%, Week-4 retention was 24%, and free-to-paid conversion within 30 days was 6.8%. Since launch, activation increased to 47%, Week-4 retention moved to 26%, and 30-day conversion rose to 7.4%. However, MAU is flat at ~2.4M, and customer support tickets about account linking increased 18%. Marketing spend and traffic mix were stable over the same period.

Leadership wants a clear explanation of whether the launch created meaningful business impact, which metrics should be highlighted in reporting, and what follow-up actions should be prioritized.

Requirements

  1. Define the primary KPI you would use to explain the impact of the onboarding redesign and why.
  2. Decompose the KPI into leading and lagging indicators to determine where the improvement came from.
  3. Explain whether the observed metric changes suggest real product impact versus noise or mix shift.
  4. Propose an executive report structure that communicates the impact clearly to product, growth, and leadership stakeholders.
  5. Recommend 3-4 strategic next steps based on the metric movement, including any guardrails.

Data Available

  • user_signup_funnel: user_id, signup_date, onboarding_step, completed_flag, traffic_source, device_type
  • product_events: user_id, event_time, event_name, feature_name, session_id
  • subscriptions: user_id, trial_start_date, paid_start_date, plan_type, cancellation_date
  • weekly_retention: user_id, signup_week, retained_week_1, retained_week_4, retained_week_8
  • support_tickets: ticket_id, user_id, created_at, issue_type, resolution_time