Estimate Super Bowl QR Code Scan Rate Using Historical Data

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Estimate Super Bowl QR Code Scan Rate Using Historical Data states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Estimate Super Bowl QR Code Scan Rate Using Historical Data

Company: Coinbase

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario Super Bowl TV ad featuring a QR code; company wants to project funnel performance from view to sign-up. ##### Question Estimate what percentage of viewers will scan the on-screen QR code during the Super Bowl broadcast. Describe how you would leverage historical data to build that estimate. If conversion-rate data from past QR campaigns is owned by the ad agency and only TV-company data are available, how would you still approximate the scan rate? After a user scans the code, how would you estimate the conversion rate from landing-page visit to sign-up? ##### Hints Lay out a funnel, justify priors with comparable events, adjust for audience size and engagement, and state key assumptions.

Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Estimate Super Bowl QR Code Scan Rate Using Historical Data states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025, 10:55 AM
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Estimate Super Bowl QR Code Scan Rate Using Historical Data

Estimating QR Scan and Sign-up Conversion for a Super Bowl TV Ad

Scenario

A Super Bowl TV ad prominently features a QR code and a clear call-to-action (CTA). The company wants to forecast funnel performance from TV view to app/website sign-up.

Tasks

  1. Estimate the percentage of viewers who will scan the on-screen QR code during the Super Bowl broadcast.
  2. Explain how you would leverage historical data to build that estimate and justify your assumptions.
  3. If conversion-rate data from past QR campaigns is owned by the ad agency and only TV-company data are available, describe how you would still approximate the scan rate.
  4. After a user scans the code, estimate the conversion rate from landing-page visit to sign-up, stating your assumptions and approach.

Guidance

  • Lay out a funnel and define key metrics.
  • Use comparable events to justify priors; adjust for audience size, attention, and engagement.
  • Make assumptions explicit and provide ranges.
  • Include a small numeric example to illustrate your method.

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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