Determine Impact of Re-share Button on User Engagement

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 Determine Impact of Re-share Button on User Engagement states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Determine Impact of Re-share Button on User Engagement

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario A PM suspects the "Re-share" feature is harming overall user engagement on the platform. ##### Question How would you determine whether the Re-share button negatively impacts engagement? Which metrics or data sources would you analyze? How would you design and execute an experiment to test this hypothesis? ##### Hints List core and guardrail metrics, propose A/B or diff-in-diff design, discuss segmentation, statistical power, experiment duration, and potential confounders.

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 Determine Impact of Re-share Button on User Engagement states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025
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Determine Impact of Re-share Button on User Engagement

Assessing Whether the Re-share Button Hurts Engagement

Context

The platform has a "Re-share" button that lets users share existing posts to their own network (e.g., your friend reposts someone else’s content and it appears in your feed). A PM suspects this feature is reducing overall user engagement.

Task

Answer the following:

  1. How would you determine whether the Re-share button negatively impacts engagement?
  2. Which core, diagnostic, and guardrail metrics or data sources would you analyze?
  3. How would you design and execute an experiment (A/B or alternative) to test this hypothesis, including segmentation, power, duration, and confounders?

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?
  • Suppose the PM's claim runs the other way: the Re-share button is not driving engagement, so the PM proposes removing it. How would you test whether the button actually causes engagement, with removal as the treatment, and what evidence would lead you to support the removal or push back on it?

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