I started with CSAT as the primary metric because it felt obvious, but then second-guessed myself mid-answer and pivoted to resolution rate, which I think was the right call.
Start by defining a primary success metric that directly measures the chatbot's impact on customer experience, such as resolution rate or CSAT, and then outline guardrail metrics to monitor unintended consequences. Next, describe a rigorous A/B test design with proper randomization, control, and sample size calculation, and finally explain how you would analyze the results to make a data-driven decision.
Pro tip: Emphasize the importance of aligning the primary metric with business goals and ensuring guardrail metrics are leading indicators of long-term customer satisfaction, not just short-term operational metrics.
Choose a metric that directly reflects improved customer experience, such as issue resolution rate or customer satisfaction (CSAT) score, and justify why it's the most relevant for the chatbot's purpose.
Select metrics to ensure the chatbot doesn't harm other aspects of the customer experience, such as escalation rate, average handling time, or repeat contact rate, and explain how you would monitor them.
Outline an A/B test where users are randomly assigned to either interact with the chatbot or a control group (e.g., human agents or no chatbot), ensuring proper randomization, sufficient sample size, and duration to detect meaningful effects.
Describe how you would analyze the data, including statistical tests, confidence intervals, and segment analysis, and how you would balance primary and guardrail metrics to make a recommendation.
Mention the need for long-term tracking and qualitative feedback (e.g., user surveys) to complement the experiment and ensure the chatbot truly enhances customer experience over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.