Determine Key Metrics for Spend-Tracker Launch Decision
Company: Chime
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Technical Screen
##### Scenario
A fintech app A/B-tested a new Spend-Tracker feature. Test and Control data are split into High-Income and Non-High-Income segments, with metrics such as average revenue, total revenue, profit, and acquisition cost.
##### Question
Given the T/C metrics for each segment, how would you decide whether to launch the Spend-Tracker? State the factors, calculations, and thresholds you’d use. Which two or three metrics would you prioritize and why? How would you interpret divergent results between High-Income and Non-High-Income users? What additional analyses or data would you request before final launch?
##### Hints
Compare relative lift vs. cost, test for statistical significance, check segment interaction effects, and weigh long-term LTV against acquisition spend.
Quick Answer: This question evaluates the candidate's competency in A/B testing, experiment analysis, segmentation, metric selection, and business-oriented cost–benefit reasoning, and it sits in the Analytics & Experimentation domain of Data Science while testing practical application supported by conceptual understanding.