Analytics
Use answer quality, visitor context, response time, usage, cost estimates, and training gaps to improve chatbot performance.
Metrics
Metrics
Conversation trends, message totals, leads, countries, devices, domains, and top pages.
Answer quality signals such as knowledge answers, fallback answers, blocked answers, and greeting turns.
Response time, AI token metadata, estimated cost, and whether a real AI call happened.
Training health, failed sources, top unanswered questions, contacts, and visitor context.
Quality
Answer quality
Answer kinds include knowledge, fallback, greeting, and blocked. Fallback heavy behavior usually means the training set needs better coverage.
Visitors
Visitor context
Visitor analytics use coarse context such as country, region, city, device, browser, OS, language, timezone, screen, viewport, referrer, and page URL.
Credits
Credit usage
Analytics are available with the product. Token and cost tracking are reporting signals, while AI work is debited from workspace credits.