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.

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 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, 10:55 AM
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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?

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 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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