Investigate Pop-up Impact on Partner Referral Conversions

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 Investigate Pop-up Impact on Partner Referral Conversions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Investigate Pop-up Impact on Partner Referral Conversions

Company: DoorDash

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario Partner-referral channel suddenly shows a sharp fall in first-time purchasers after a new splash pop-up asking visitors to download the mobile app was launched. ##### Question How would you investigate and confirm that the pop-up caused the drop in conversions from partner links? Assuming online traffic is unchanged, how would you quantify the trade-off between app-download uplift and lost first-purchase conversions? What metrics and experiment design would you propose to decide whether to keep, modify, or remove the pop-up? ##### Hints Map user funnel, build pre/post or A/B test, measure incremental value of app installs vs. lost orders, control for seasonality and partner mix.

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 Investigate Pop-up Impact on Partner Referral Conversions 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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Investigate Pop-up Impact on Partner Referral Conversions

Partner-Referral Conversions Fell After App Pop-up: Diagnose, Quantify, and Decide

Context

You are an analytics data scientist at a consumer marketplace. A blocking splash pop-up prompting visitors to download the mobile app was launched on mobile web. Soon after, the partner-referral channel shows a sharp drop in first-time purchaser conversions. Overall online traffic volume is unchanged.

Tasks

  1. Causal diagnosis: How would you investigate and confirm the pop-up caused the conversion drop from partner links?
  2. Trade-off quantification: Assuming traffic is unchanged, how would you quantify the trade-off between app-download uplift and lost first-purchase conversions?
  3. Decision framework: Which metrics and experiment design would you propose to decide whether to keep, modify, or remove the pop-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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