I started by trying to anchor on something concrete: what's the baseline attention rate for a Super Bowl ad, then layer on the friction of grabbing your phone and scanning mid-broadcast.
Start by clarifying the context and assumptions (e.g., Super Bowl viewership, ad placement, QR code prominence) and then propose a structured estimation method using historical benchmarks and analogous data. Walk through a funnel-based calculation (impressions → scans → conversions) and emphasize how you would validate and refine the estimate with A/B tests or post-campaign analysis.
Pro tip: Acknowledge that the actual Coinbase Super Bowl QR code campaign saw a massive spike in traffic, but avoid quoting exact numbers unless you can cite them; instead, focus on the methodology and how you'd use historical data like past QR code campaigns, CTRs, and scan rates to build a defensible estimate.
Ask clarifying questions about the ad (e.g., duration, placement, call-to-action) and define the target audience (e.g., US viewers, demographics). State assumptions explicitly, such as Super Bowl viewership (~100M) and the percentage of viewers with a smartphone ready.
List internal and external data sources: past QR code campaigns (e.g., other Super Bowl ads, Snapchat codes), industry benchmarks for CTR on TV ads, and website traffic spikes from similar events. Consider Coinbase's own historical campaign data if available.
Break down the estimation into stages: total viewers → viewers who see the ad → viewers with a smartphone → viewers motivated to scan → successful scans. Assign plausible conversion rates at each stage based on historical data and adjust for novelty/context.
Propose methods to validate the estimate: A/B testing (e.g., different QR code placements), post-campaign analysis of actual scan data, and comparing with control periods. Discuss how to use real-time data to adjust the estimate during the campaign.
Present the estimate as a range (e.g., 0.5%–2%) with confidence intervals, and tie it to business metrics like sign-ups or revenue. Highlight the importance of adaptability and learning from the actual outcome to improve future estimates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge the data limitation and propose a triangulation approach using TV-side data and external benchmarks to estimate scan rate. Focus on deriving a defensible range rather than a precise number, and validate assumptions with sensitivity analysis.
Pro tip: Emphasize that scan rate is a function of both ad exposure and user intent; use TV-side proxies like reach and frequency to model exposure, and clearly state assumptions to build credibility. Show awareness that the agency's data might be accessible via negotiation or privacy-preserving methods like aggregated reporting.
Confirm whether the goal is a rough estimate for planning or a precise metric for optimization, and identify what TV-side data is available (e.g., impressions, GRPs, reach, frequency).
Use industry benchmarks for QR scan rates from similar campaigns (e.g., TV ads with QR codes) and adjust for Coinbase's brand, audience, and campaign specifics.
Estimate total scans by applying a benchmark scan rate to TV impressions, then derive scan rate as scans divided by impressions; alternatively, model scans as a function of reach and frequency.
Cross-check estimates using other internal signals like app downloads, website traffic spikes during TV airings, or promo code redemptions to ensure plausibility.
Provide a range with confidence intervals based on sensitivity analysis of key assumptions, and recommend a plan to obtain better data (e.g., negotiate with agency or run a test).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the conversion event and the denominator (unique users who land on the page after scanning the QR code). Then outline how you would measure it using event tracking and funnel analysis, and discuss how to estimate it if direct data is unavailable (e.g., via proxy metrics or experimentation).
Pro tip: Emphasize the importance of segmenting by acquisition source (QR code location, campaign) and device type, as conversion rates can vary significantly and this insight can drive product improvements.
Clearly specify what constitutes a 'completed sign-up' (e.g., account created and verified) and the denominator: unique users who land on the page after scanning the QR code.
Determine what event data is available (e.g., page views, sign-up completions) and ensure proper tracking is in place to attribute the landing page visit to the QR code scan.
Compute the conversion rate as the number of completed sign-ups divided by the number of unique landing page visitors, over a defined time period.
If direct tracking is missing, use proxy metrics (e.g., click-through rates from QR code, historical conversion rates from similar campaigns) or run a controlled experiment to estimate.
Check for data quality issues, segment the conversion rate by relevant dimensions (e.g., device, location), and consider A/B testing to improve the rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.