Define Success Metrics for Euro-Chat Customer-Service Chatbot

Q: Define Success Metrics for Euro-Chat Customer-Service Chatbot

This question evaluates a data scientist's competency in defining product success metrics, selecting guardrail metrics, and designing A/B experiments for a customer-service chatbot, covering metric definition, statistical power, randomization, and monitoring.

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Question

Scenario

An e-commerce company has deployed a customer-service chatbot ("euro-chat") to handle B2C support inquiries across web/app chat. The bot can answer questions and escalate to human agents when needed.

Task

Define how you would measure success for euro-chat, what guardrail metrics you would track, and how you would design an experiment to test whether the chatbot improves customer experience.

Requirements

Propose a single primary success metric (clear definition and measurement window).
List guardrail metrics and why they matter.
Describe an A/B test design including: control vs treatment, randomization unit, success threshold, sample size approach, and monitoring/stop criteria.

Hints to consider

Resolution/containment rate, CSAT, handle time.
Control/treatment, randomization unit, success threshold, sample sizing, and monitoring.

Define Success Metrics for Euro-Chat Customer-Service Chatbot

Scenario

Task

Requirements

Hints to consider

Solution

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