##### Scenario Marketing campaign evaluation and retail performance diagnostics ##### Question Estimate how many users will register after a Super Bowl advertisement. Walk through your funnel assumptions and justify every number. Retail revenue has fallen. Use a structured framework to diagnose the drop and give concrete example cases that could explain it. ##### Hints Think top-down funnel math, conversion rates, and retail KPIs like traffic × AOV × CVR.

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 Diagnose Retail Revenue Drop and Predict Ad Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Coinbase.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Coinbase during technical interviews.

Diagnose Retail Revenue Drop and Predict Ad Impact

Scenario

You are a data scientist for a consumer fintech app preparing to run a Super Bowl TV ad and investigating a recent revenue decline.

Part 1 — Super Bowl Ad Lift Estimation

Estimate how many users will register as a result of a single Super Bowl advertisement. Build a clear, top-down funnel and justify every assumption you use. Provide a base case and a sensitivity range (e.g., low/base/high) and explain how you would validate the estimate after the event.

Hints:

Think reach → attention → site/app visit → install → registration (and optionally KYC).
Use realistic conversion rates and call out any tail effects (same day vs. 7–14 day follow-on).

Part 2 — Revenue Drop Diagnosis

Retail revenue has fallen. Use a structured framework to diagnose the drop and give concrete example cases that could explain it.

Hints:

Decompose revenue. For general retail: Revenue ≈ Traffic × Conversion Rate (CVR) × Average Order Value (AOV).
For a consumer trading/fintech app, tailor the decomposition (e.g., active users × funding/deposit rate × trades per user × average trade size × take rate).
Show how you would quantify contributions, identify root causes, and propose next steps.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

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

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

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?