How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Evaluate Success of B2C Chat App with Key Metrics | Meta Interview Question

Evaluate Success of B2C Chat App with Key Metrics

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in product analytics, metric design, and experimentation within the Analytics & Experimentation domain, emphasizing practical application of metric selection alongside conceptual reasoning about north-star, guardrail, and diagnostic metrics.

Scenario

A data scientist is asked to define how to evaluate the overall success of a business-to-consumer (B2C) chat application.

Task

Identify the core (north-star and guardrail) and secondary/diagnostic metrics you would track to measure product success. Briefly explain the rationale behind your choices.

Hints (from interviewer)

Response time
Conversation completion
Retention
Revenue
NPS

Solution

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