Use a difference-in-differences design comparing sign-ups during the ad window to a synthetic control constructed from pre-ad periods and similar non-exposed markets, then adjust for QR code scans and deduplicate users across channels. Validate by triangulating with holdout tests, funnel analysis, and sensitivity checks on key assumptions.
Pro tip: Acknowledge that the Super Bowl creates a massive simultaneous shock, so a simple pre-post comparison is biased; instead, emphasize using a control group (e.g., regions with no ad exposure or historical baselines) and clearly state assumptions about parallel trends.
Establish what sign-ups would have occurred without the ad by using a difference-in-differences approach with a control group (e.g., similar time periods in previous years or markets not exposed to the ad). Clearly state the parallel trends assumption and how you'd test it.
List internal data (sign-up timestamps, acquisition channel, device ID, QR scan events) and external data (Super Bowl viewership ratings, social media mentions). Deduplicate by matching user IDs, emails, or device fingerprints across channels to avoid double-counting.
Estimate incremental sign-ups using specific numbers: e.g., 100M viewers, 1% QR scan rate = 1M scans, 20% conversion to sign-up = 200K sign-ups. Adjust for baseline sign-ups (e.g., 50K in 48 hours without ad) to get incremental lift of 150K.
Cross-check with A/B testing if a holdout group exists, funnel analysis to see drop-off from scan to sign-up, and sensitivity analysis on assumptions (e.g., scan rate, conversion rate). Compare with external benchmarks and historical campaign performance.
Present a range (e.g., 120K–180K incremental sign-ups) rather than a point estimate, and discuss potential confounders like concurrent promotions, media coverage, and seasonality. Recommend further experiments to refine the estimate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.