Design A/B Test to Isolate Product Usage Drop Causes

Q: Design A/B Test to Isolate Product Usage Drop Causes

This question evaluates a data scientist's competency in causal inference, experimentation design, and product analytics, focusing on identifying confounders, designing A/B tests, selecting control variables, and communicating causal lift estimates.

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Question

Investigating a 10–11% Product Usage Drop via Experimentation

Context

You observe that product usage fell by 10% in the U.S. and 11% in Mexico over the same recent period (e.g., last 1–2 weeks vs. prior baseline). No planned outages were announced. You are tasked with diagnosing causes and proposing an experiment to isolate them.

Tasks

Identify plausible confounders that could explain simultaneous decline across both markets.
Design an A/B test (or tests) to isolate the causal driver(s).
Specify variables you would control for in the analysis and setup.
Explain how you would report findings, including causal lift estimates, to stakeholders.

Design A/B Test to Isolate Product Usage Drop Causes

Investigating a 10–11% Product Usage Drop via Experimentation

Context

Tasks

Solution

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