Measure Super Bowl ad impact with causal design
Super Bowl National TV Ad: Incrementality Measurement Plan
Your consumer application recently ran a single 30-second national television advertisement during the Super Bowl. Your task is to design a comprehensive plan to estimate the incremental impact of this ad on key metrics such as installs and revenue. A critical constraint is the absence of a clean, randomized control group for direct comparison.
Your plan must address the following specific areas:
(a) KPIs and Measurement Windows
- Define the primary and guardrail Key Performance Indicators (KPIs) relevant to ad impact.
- Specify precise measurement windows, including a pre-period, game day segmented by quarter, and a post-period.
(b) Identification Strategies
- Propose at least two credible causal identification strategies to isolate the ad's effect, given the lack of a randomized control. Examples include geo-level Difference-in-Differences (DiD) using heterogeneous DMA viewership, synthetic control methods leveraging similar apps or markets, matched markets analysis, or Marketing Mix Modeling (MMM) incorporating a spike regressor.
(c) DiD Specification and Assumptions
- If Difference-in-Differences (DiD) is one of your proposed strategies, write out the estimating equation.
- List the core assumptions required for a valid DiD analysis (e.g., parallel trends, Stable Unit Treatment Value Assumption (SUTVA), no differential shocks).
- Describe how you would empirically test these assumptions.
(d) Confounders
- Outline a plan to account for potential confounding factors that might co-occur with the Super Bowl ad. These include concurrent promotions, system outages, App Store featuring, and general game-related seasonal spikes in user activity.
(e) Uncertainty
- Detail how you would estimate and report the uncertainty associated with your impact estimates. Consider methods such as robust or cluster-robust standard errors, placebo tests, or randomization inference.
(f) Validation
- Describe falsification checks to validate the robustness of your findings (e.g., pre-trend analysis, placebo Super Bowl dates).
- Propose heterogeneity cuts to understand differential impacts across user segments (e.g., new vs. existing users, different platforms).
(g) Power
- Assuming a true lift of a 4% increase in daily installs on game day, and targeting a 5% alpha (significance level) and 80% power, specify the data required and compute an approximate minimum sample size (in terms of markets and days) needed to detect this effect.
AI interview prep
Core dilemma
Estimating causal impact of a national, single-shot event in the absence of a randomized control group, while accounting for numerous confounding factors and ensuring statistical rigor.
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 Super Bowl ad impact with causal design
Super Bowl National TV Ad: Incrementality Measurement Plan
Your consumer application recently ran a single 30-second national television advertisement during the Super Bowl. Your task is to design a comprehensive plan to estimate the incremental impact of this ad on key metrics such as installs and revenue. A critical constraint is the absence of a clean, randomized control group for direct comparison.
Your plan must address the following specific areas:
(a) KPIs and Measurement Windows
- Define the primary and guardrail Key Performance Indicators (KPIs) relevant to ad impact.
- Specify precise measurement windows, including a pre-period, game day segmented by quarter, and a post-period.
(b) Identification Strategies
- Propose at least two credible causal identification strategies to isolate the ad's effect, given the lack of a randomized control. Examples include geo-level Difference-in-Differences (DiD) using heterogeneous DMA viewership, synthetic control methods leveraging similar apps or markets, matched markets analysis, or Marketing Mix Modeling (MMM) incorporating a spike regressor.
(c) DiD Specification and Assumptions
- If Difference-in-Differences (DiD) is one of your proposed strategies, write out the estimating equation.
- List the core assumptions required for a valid DiD analysis (e.g., parallel trends, Stable Unit Treatment Value Assumption (SUTVA), no differential shocks).
- Describe how you would empirically test these assumptions.
(d) Confounders
- Outline a plan to account for potential confounding factors that might co-occur with the Super Bowl ad. These include concurrent promotions, system outages, App Store featuring, and general game-related seasonal spikes in user activity.
(e) Uncertainty
- Detail how you would estimate and report the uncertainty associated with your impact estimates. Consider methods such as robust or cluster-robust standard errors, placebo tests, or randomization inference.
(f) Validation
- Describe falsification checks to validate the robustness of your findings (e.g., pre-trend analysis, placebo Super Bowl dates).
- Propose heterogeneity cuts to understand differential impacts across user segments (e.g., new vs. existing users, different platforms).
(g) Power
- Assuming a true lift of a 4% increase in daily installs on game day, and targeting a 5% alpha (significance level) and 80% power, specify the data required and compute an approximate minimum sample size (in terms of markets and days) needed to detect this effect.