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This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Google.

What role is this question designed for?

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

Boost Google Workspace Chat Usage with Strategic A/B Testing

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in product analytics, growth measurement, hypothesis-driven experimentation, segmentation, and A/B testing for an enterprise messaging or collaboration product.

Google

Aug 4, 2025, 10:55 AM

Data Scientist

Technical Screen

Analytics & Experimentation

Scenario

Google Workspace Chat adoption is low, and leadership asks for a data-driven plan to grow monthly active users (MAU).

Task

Design a plan to drive user growth. Include:

North-star metric and supporting metrics to track.
Acquisition/activation funnel and key segmentation.
Hypotheses (with rationale) to increase growth.
An A/B-testing roadmap: design, cadence, powering, and guardrails.
Success criteria and how you will validate results.

Hints: North-star metric, acquisition funnel, segmentation, experiment cadence, guardrail metrics.

Solution

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