Estimate QR Code Scan Rate for Super Bowl Ad

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 QR Code Scan Rate for Super Bowl Ad states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Estimate QR Code Scan Rate for Super Bowl Ad

Company: Coinbase

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario A TV network airs a QR-code Super Bowl ad and wants to forecast the full acquisition funnel. ##### Question a) How would you estimate the percentage of viewers who will scan the QR code? b) What data sources would you use? c) If only prior TV campaign data (no ad-partner conversion data) is accessible, how would you approximate the scan rate? d) How would you estimate the conversion rate from scan to account sign-up? ##### Hints Think funnel math, historical benchmarks, uplift analysis during airing, and proxy metrics.

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 QR Code Scan Rate for Super Bowl Ad 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 QR Code Scan Rate for Super Bowl Ad

Forecasting a QR-Code Super Bowl Ad Funnel

Context

You are advising a brand running a QR-code Super Bowl TV spot. The goal is to forecast and then measure the acquisition funnel from TV viewership to QR scans and ultimately to new account sign-ups. Assume you can access first-party web/app analytics and standard TV delivery data; ad-partner conversion data may be limited.

Tasks

(a) How would you estimate the percentage of viewers who will scan the QR code?

(b) What data sources would you use?

(c) If only prior TV campaign data is available (no ad-partner conversion data), how would you approximate the scan rate?

(d) How would you estimate the conversion rate from scan to account sign-up?

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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