Measure Success of New B2B Product
Overview
This question evaluates a data scientist's product analytics and experimentation skills, including designing a comprehensive metric framework, analyzing metric distributions and anomalies, measuring value at both account and seat levels, and identifying data-driven growth opportunities.
LinkedIn
Jul 12, 2025, 6:59 PM
Data Scientist
Onsite
Analytics & Experimentation
67
0
Scenario
A new LinkedIn B2B product has launched. Leadership wants to understand whether it adds value and what its growth potential is. Assume a typical B2B SaaS setup with multi-seat accounts (companies purchase seats for users), a free trial/onboarding flow, and usage events that can be instrumented. Success should be assessed at both account and seat levels.
Questions
- Propose a comprehensive metric framework to define product success.
- Describe how you would analyze metric distributions and investigate anomalies (for example, when metric A goes up while metric B goes down).
- Explain how you would evaluate whether the product delivers tangible value to users.
- Outline steps to identify and prioritize future growth opportunities based on data insights.
Solution
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