How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Airwallex.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Airwallex during technical interviews.

How would you test billboard effectiveness? | Airwallex Interview Question

Quick Overview

This question evaluates experimental design, causal inference, statistical power/sample-size calculation, and marketing measurement competencies relevant to offline advertising effectiveness within the Analytics & Experimentation domain for data scientists.

Pinterest is considering an offline billboard advertising campaign in selected cities to increase brand awareness and downstream user growth. As the data scientist, design the measurement plan.

Address the following:

How would you design the billboard campaign and define success metrics? Consider awareness, traffic, conversion, and guardrail metrics.
How would you test effectiveness? Discuss an ideal randomized geo experiment and what you would do if randomization is not feasible.
Explain the difference between Propensity Score Matching (PSM) and Propensity Score Weighting (PSW or IPW). What estimand does each target, and what does each method effectively optimize for?
How would you calculate sample size or minimum detectable effect for this study? State the experimental unit, the main formula, and any adjustments needed for clustered or geo-based experiments.
What tradeoffs and failure modes would you watch for, such as spillovers, seasonality, selection bias, delayed effects, and budget constraints?

How would you test billboard effectiveness?

Quick Overview

Solution

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