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Measure Billboard Campaign Impact: Design, Bias, Test Strategy

Last updated: Mar 29, 2026

Quick Overview

This question evaluates experimental design, causal inference, sampling and randomization strategies, bias identification and mitigation, and the choice of statistical tests for estimating marketing campaign effects.

  • medium
  • Pinterest
  • Analytics & Experimentation
  • Data Scientist

Measure Billboard Campaign Impact: Design, Bias, Test Strategy

Company: Pinterest

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario A marketing team put up billboards in several cities and now wants to know whether the campaign increased brand awareness versus cities without billboards. ##### Question Design an experiment to measure the billboard campaign’s effect, starting from hypothesis definition and sampling strategy. How would you handle potential biases such as city-level differences in population or existing brand affinity? After data collection, what statistical test or model would you use and why? ##### Hints Think randomization, stratification, pre-post matching, and difference-in-differences.

Quick Answer: This question evaluates experimental design, causal inference, sampling and randomization strategies, bias identification and mitigation, and the choice of statistical tests for estimating marketing campaign effects.

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Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Analytics & Experimentation
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Measuring Billboard Impact on Brand Awareness

Scenario

A marketing team launched billboard ads in several cities and wants to estimate the campaign's causal impact on brand awareness compared to cities without billboards.

Assume brand awareness is measured via a short, consistent online survey (e.g., aided awareness: "Have you heard of Brand X?" yes/no) fielded in each city before and after the campaign. The campaign runs for 6–8 weeks, and you can choose which cities receive billboards.

Task

Design an experiment and analysis plan that covers:

  1. Hypotheses and outcome definition.
  2. Sampling and randomization strategy across cities.
  3. How to handle potential biases (e.g., city population differences, pre-existing brand affinity, spillovers, seasonality, concurrent marketing).
  4. The statistical test/model you would use after data collection and why.

Hints: Consider randomization, stratification/blocking, pre–post matching, and difference-in-differences.

Solution

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