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Quantify Latent Demand for Group Video Calling Feature

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in product analytics, causal experimentation, metric definition, and trade-off analysis for product launch decisions, with emphasis on network-aware thinking and estimating latent demand from observational signals.

  • hard
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Quantify Latent Demand for Group Video Calling Feature

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

##### Scenario The team plans to launch a group video-call feature and needs analytic guidance. ##### Question Given access to richer data (messages, friend graphs, surveys), how would you quantify latent demand for group video calling? Define the primary success metrics and experimental design you would use to evaluate a group-call launch. Using data, how would you select an initial cap on the number of participants allowed in a group call? ##### Hints Discuss interaction clustering, A/B tests, retention, percentile analysis, engineering cost versus user coverage.

Quick Answer: This question evaluates a data scientist's competency in product analytics, causal experimentation, metric definition, and trade-off analysis for product launch decisions, with emphasis on network-aware thinking and estimating latent demand from observational signals.

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Meta
Aug 4, 2025, 10:55 AM
Data Scientist
Technical Screen
Analytics & Experimentation
1
0

Scenario

A consumer messaging app is preparing to launch group video calling. You have access to rich data: message logs (timestamps, thread IDs), friend/interaction graphs, 1:1 call logs, and user surveys.

Task

Design an analytics plan that:

  1. Quantifies latent demand for group video calling using existing data (messages, friend graphs, surveys).
  2. Defines the primary success metrics for launch.
  3. Proposes an experimental design to evaluate impact while handling network interference.
  4. Recommends a data-driven initial cap on the number of participants allowed in a group call, considering user coverage versus engineering/quality costs.

Hints

  • Consider interaction clustering (e.g., communities, group threads), A/B tests with guardrails, retention/attach rate, percentile analysis of desired group sizes, and the trade-off between engineering cost/quality and user coverage.

Solution

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