PracHub
QuestionsPremiumCoachesLearningGuidesInterview Prep
|Home/Analytics & Experimentation/Meta

Evaluating the Facebook ‘Memory’ feature

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's proficiency in product analytics, experimentation design, and impact measurement by probing framework design, metric selection (retention, engagement depth, share rate, cannibalization, safety/quality guardrails) and CTR benchmarking.

  • medium
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Evaluating the Facebook ‘Memory’ feature

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

Scenario: The Memories feature resurfaces old posts to spark nostalgia and engagement. Recent data shows mixed click‑through rates and questions about whether it inflates time‑spent or simply reshuffles attention. You must propose an evaluation framework, decide acceptable CTR thresholds, and ensure content is not resurfaced too frequently. ​ Question 1: Outline a framework to evaluate the performance of the Facebook Memories feature. (Hint: retention, depth of interaction, share rate) Question 2: Is a 20 % click‑through rate acceptable for Memories? Explain how you would benchmark it. (Hint: industry norms, impression volume, user intent) Question 3: Is total time‑spent a good metric for Memories’ impact? Why or why not? (Hint: attribution challenges, alternative metrics) Question 4: After resurfacing a 2019 post in 2020, how would you prevent it appearing again in 2021? (Hint: deduping strategy, cool‑down windows)

Quick Answer: This question evaluates a data scientist's proficiency in product analytics, experimentation design, and impact measurement by probing framework design, metric selection (retention, engagement depth, share rate, cannibalization, safety/quality guardrails) and CTR benchmarking.

Related Interview Questions

  • Measure scheduled posts feature success - Meta (medium)
  • Estimate ads ranking revenue impact - Meta (medium)
  • How should you evaluate unconnected content? - Meta (medium)
  • Should WhatsApp launch group calls? - Meta (medium)
  • How would you grow Meta products? - Meta (medium)
Meta logo
Meta
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Analytics & Experimentation
93
0

Evaluating the Facebook Memories Feature

Context

You are a Data Scientist asked to assess whether the Memories feature (which resurfaces users' past posts) delivers real user value without simply reshuffling attention from other content. You must design an evaluation plan, set acceptable CTR expectations, choose appropriate impact metrics, and avoid over‑resurfacing the same content.

Questions

  1. Framework: Outline how you would evaluate Memories performance. Consider retention, depth of interaction, share rate, cannibalization, and safety/quality guardrails.
  2. CTR Benchmark: Is a 20% click‑through rate acceptable for Memories? Explain how you would benchmark it against internal and external norms, impression volume, and user intent.
  3. Time‑Spent Metric: Is total time‑spent a good metric for Memories’ impact? Why or why not, and what should you use instead?
  4. Dedupe/Frequency: After resurfacing a 2019 post in 2020, how would you prevent it appearing again in 2021? Propose a deduping and cool‑down strategy.

Solution

Show

Submit Your Answer to Earn 20XP

Sign in to leave a comment

Loading comments...

Browse More Questions

More Analytics & Experimentation•More Meta•More Data Scientist•Meta Data Scientist•Meta Analytics & Experimentation•Data Scientist Analytics & Experimentation
PracHub

Master your tech interviews with 8,000+ real questions from top companies.

Product

  • Questions
  • Learning Tracks
  • Interview Guides
  • Resources
  • Premium
  • For Universities
  • Student Access

Browse

  • By Company
  • By Role
  • By Category
  • Topic Hubs
  • SQL Questions
  • Compare Platforms
  • Discord Community

Support

  • support@prachub.com
  • (916) 541-4762

Legal

  • Privacy Policy
  • Terms of Service
  • About Us

© 2026 PracHub. All rights reserved.