This one went deeper than I expected for a hiring manager conversation.
Structure your answer as a clear experimental design: start with a testable hypothesis, then explain how you'd randomize at the market level (e.g., DMAs) to avoid contamination, define primary and secondary KPIs, calculate sample size based on expected effect and baseline conversion, set a duration that captures the full campaign cycle, and finally define success criteria with statistical significance and practical lift. Emphasize trade-offs and assumptions throughout.
Pro tip: Acknowledge that TV is not a true individual-level randomized experiment; instead, use a geo-based holdout or matched market design, and consider using a difference-in-differences or synthetic control to isolate the TV effect from other factors.
State a clear, testable hypothesis: e.g., 'Exposing target DMAs to TV ads will increase website sign-ups by at least X% compared to control DMAs.' Specify the expected effect size and rationale.
Randomize at the DMA (or market) level to avoid spillover. Use matched pairs or stratified randomization based on historical sign-up rates and demographics to ensure comparable test and control groups.
Define primary KPI (e.g., sign-up rate per capita) and secondary KPIs (e.g., website visits, cost per acquisition). Calculate required sample size (number of DMAs or weeks) using power analysis, accounting for intra-cluster correlation.
Run the experiment for a full campaign cycle (e.g., 4-6 weeks) to capture lagged effects and avoid novelty. Ensure control DMAs are held out from TV ads during the test period.
Success: statistically significant lift in primary KPI with p<0.05 and practical significance (e.g., ROI positive). Use appropriate statistical methods (e.g., diff-in-diff) to account for pre-existing trends.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.