I went straight to viewership numbers and tried to layer in attention decay, second-screen behavior, that kind of thing.
Break the problem into a funnel: total Super Bowl viewers → those who noticed the ad → those who have the app/ability to scan → those who actually scan. Use a mix of known benchmarks (e.g., Super Bowl viewership, ad recall rates, QR scan rates) and reasonable assumptions to estimate each stage, then multiply to get a percentage. Sanity-check the final number against real-world examples (e.g., Coinbase's 2022 QR code ad) and discuss sources of uncertainty.
Pro tip: Anchor your estimate to a known real-world event (like Coinbase's 2022 Super Bowl ad) and use it to validate your assumptions; this shows you can connect theory to practice and understand the business context.
Start with the total number of Super Bowl viewers (e.g., ~100 million in the US) and consider segmentation (e.g., live vs. streaming, domestic vs. international).
Determine what fraction of viewers actually saw the ad (e.g., not all watch ads; some may leave the room) and paid attention to it (ad recall rates).
Estimate the percentage of viewers who have a smartphone with a QR scanner, are Coinbase users or interested, and are motivated to scan immediately.
Use benchmarks for QR code scan rates from TV ads (often low, e.g., 0.1-1%) or analogous digital campaigns to estimate the final conversion.
Multiply the fractions to get an overall percentage, then compare with real-world data (e.g., Coinbase reported ~20 million scans from their 2022 ad) to validate or adjust.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying what 'scan rate' means in this context—likely the rate at which users scan QR codes for payments or wallet interactions—and then outline a layered data strategy. Emphasize combining internal product telemetry (e.g., event logs, transaction data) with external market data and experimentation results to triangulate the estimate. Structure your answer around data source categories, validation methods, and potential biases.
Pro tip: Demonstrate awareness of Coinbase's data ecosystem by mentioning specific internal sources like the event stream from the mobile app, on-chain data for wallet scans, and A/B test results—showing you understand both product analytics and crypto-specific nuances.
Define 'scan rate' precisely: is it scans per user, per session, or per transaction? Identify the product surface (e.g., QR code payments, wallet address scanning) and the time window.
List sources like mobile/web event logs (e.g., button clicks, camera opens), transaction records, user session data, and backend service logs that capture scan events.
Consider market research, industry benchmarks, app store analytics, and on-chain data (for wallet scans) to supplement internal data and fill gaps.
Use A/B test results, feature flag data, and user surveys to validate scan behavior and understand intent behind scans.
Cross-check estimates from different sources, assess data quality (e.g., sampling bias, logging gaps), and reconcile discrepancies to produce a robust estimate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the data limitation and proposing a triangulation approach that combines TV campaign metrics with external benchmarks and statistical modeling. Use techniques like media mix modeling (MMM) or Bayesian inference to estimate scan rates, leveraging proxies such as website traffic or app downloads. Emphasize validation and sensitivity analysis to ensure robustness.
Pro tip: Leverage industry benchmarks for QR/scan rates from similar campaigns or platforms, but adjust for Coinbase's unique audience and campaign context. Clearly state assumptions and propose a pilot test to calibrate the model, showing you balance pragmatism with rigor.
Clarify what 'scan rate' means (e.g., scans per impression) and acknowledge the missing ad-partner conversion data. Identify available TV campaign data (impressions, GRPs, reach) and any other internal data (website visits, app installs).
Use external benchmarks for scan rates from similar industries or QR code campaigns. Consider internal proxies like direct traffic spikes or branded search volume that correlate with scans.
Apply media mix modeling (MMM) or regression techniques to link TV exposure to proxy metrics. Use Bayesian methods to incorporate prior knowledge and quantify uncertainty.
Derive an approximate scan rate from the model, then validate via sensitivity analysis and, if possible, a small-scale pilot or holdout test to calibrate.
Present the estimate with confidence intervals and clearly state assumptions. Recommend next steps to collect better data, such as implementing unique QR codes or partnering with ad platforms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Break down the funnel into distinct stages (QR scan → landing page → sign-up start → completion) and estimate conversion at each stage using benchmarks, assumptions, and available data. Then combine these estimates to get an overall conversion rate, while acknowledging uncertainties and suggesting ways to validate with real data.
Pro tip: Emphasize that the estimate should be actionable: identify which stage has the lowest conversion and propose experiments to improve it, showing you think beyond just the number.
Map out the user journey from scanning the QR code to completing sign-up, including intermediate steps like landing on a webpage, starting the sign-up form, and submitting it.
Use internal data if available (e.g., from similar campaigns) or industry benchmarks for each stage, such as QR scan-to-landing page conversion, landing page-to-sign-up start, and sign-up completion rates.
Assign reasonable conversion rates for each stage based on data or assumptions, considering factors like user intent, friction, and device type.
Multiply the stage-wise conversion rates to get the overall conversion rate from QR scan to completed sign-up.
Suggest methods to validate the estimate, such as A/B testing, tracking actual funnel data, or using probabilistic models to account for uncertainty.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.