Measure Billboard Campaign Impact: Design, Bias, Test Strategy
Measure Billboard Campaign Impact: Design, Bias, Test Strategy
A marketing team at Pinterest has launched billboard ads in several cities and wants to estimate the campaign's causal impact on brand awareness compared to cities without billboards. 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.
Design an experiment and analysis plan that covers:
- Hypotheses and outcome definition.
- Sampling and randomization strategy across cities.
- How to handle potential biases (e.g., city population differences, pre-existing brand affinity, spillovers, seasonality, concurrent marketing).
- The statistical test/model you would use after data collection and why.
Consider randomization, stratification/blocking, pre–post matching, and difference-in-differences.
Constraints & Assumptions:
- Preserve the scope, facts, inputs, and requested outputs from the prompt.
- If a detail is unspecified, state a reasonable assumption before relying on it.
- Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
AI interview prep
Core dilemma
Estimating the true causal impact of a marketing campaign in a real-world setting, fraught with confounding factors and observational limitations, versus relying on a perfectly controlled experiment.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Measure Billboard Campaign Impact: Design, Bias, Test Strategy
Measure Billboard Campaign Impact: Design, Bias, Test Strategy
A marketing team at Pinterest has launched billboard ads in several cities and wants to estimate the campaign's causal impact on brand awareness compared to cities without billboards. 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.
Design an experiment and analysis plan that covers:
- Hypotheses and outcome definition.
- Sampling and randomization strategy across cities.
- How to handle potential biases (e.g., city population differences, pre-existing brand affinity, spillovers, seasonality, concurrent marketing).
- The statistical test/model you would use after data collection and why.
Consider randomization, stratification/blocking, pre–post matching, and difference-in-differences.
Constraints & Assumptions:
- Preserve the scope, facts, inputs, and requested outputs from the prompt.
- If a detail is unspecified, state a reasonable assumption before relying on it.
- Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.