Chatbot Analytics and Performance Flashcards

What does 'average handling time' (AHT) measure when applied to chatbot performance?

Answer: The average duration of a complete chatbot conversation from start to resolution

AHT helps evaluate efficiency — shorter handling times often indicate clearer conversation flows and better NLP accuracy.

Why is 'session abandonment rate' an important chatbot metric?

Answer: It shows where in the conversation flow users give up, revealing pain points in UX or NLP coverage

Abandonment analysis pinpoints which conversation steps cause drop-off so designers can fix friction points.

What tool is commonly used to visualize chatbot conversation flow data for analytics?

Answer: Sankey diagrams or funnel charts that show how users flow through conversation paths

Sankey diagrams visualize drop-off and flow between conversation steps, making it easy to spot where users get stuck or leave.

What is 'A/B testing' used for in chatbot optimization?

Answer: Comparing two versions of a bot response to determine which drives better user outcomes

A/B testing lets teams make data-driven improvements by measuring user behavior differences between response variants.

Which analytics practice helps identify the most common user intents to prioritize training improvements?

Answer: Analyzing fallback logs and intent frequency distributions to find high-volume unhandled topics

Reviewing intent frequency and fallback logs reveals which user needs are most common and underserved by current training.

What does 'CSAT score' measure in the context of chatbot analytics?

Answer: Customer Satisfaction — a user-reported rating of their chatbot experience, usually collected post-conversation

CSAT surveys give direct user feedback on perceived experience quality, complementing behavioral metrics with subjective satisfaction data.