Monitor CSAT scores to see whether customers are satisfied with their interactions. Measure resolution rates to understand how many conversations end successfully without human intervention. Track deflection rates to prove how effectively your AI Agents handle queries that would otherwise require support staff. These metrics justify your automation investment. If you can't measure improvement over time, you can't prove ROI. The dashboard surfaces them prominently because they're what you're optimizing for.

Your Gambits display as visual flowcharts with response rates at every node. 73% clicked "Get a Quote." 18% selected "Talk to Support." 9% dropped off entirely. See exactly where users engage and where they exit. Click into any conversation and read the complete transcript. Track the path users took through Gambits, which tools were triggered, and where maximum drop-offs occur. Full visibility means you understand the complete user journey, not just summary statistics.

The system analyzes conversations and surfaces key user patterns. "Users asked about bulk pricing 47 times with no satisfactory answer." "Drop-off rate at the payment step increased 22% this week." Patterns emerge automatically from conversation data without manual analysis. Uncover data about your key demographic and user journey patterns to improve the AI Agent.

When users ask questions your content doesn't cover, analytics aggregates them by topic and frequency. "API rate limits" came up 21 times with incomplete responses. "Refund timelines for enterprise customers" appeared in 18 conversations. Each gap is quantified with exact counts. You know which content to create based on proven user demand. What's missing becomes clear through documented conversation patterns showing what users actually need coverage for.

The Tars AI dashboard doesn't just show you problems. It suggests solutions. Friction points identified in conversations turn into specific recommendations. "Add a Knowledge Base article on bulk pricing." "Simplify the payment verification step." "Expand coverage for international shipping queries." Suggestions are grounded in conversation data, not guesswork. You know exactly what to improve and where to expand next.

Compare time periods to validate which changes worked. Did refining your AI Agent's tone improve CSAT scores? Did adding Knowledge Base articles increase resolution rates? The data shows a measurable impact. Your optimization decisions are backed by historical performance. Scale your AI Agents based on proven improvements, documented user behavior, and clear evidence of what moves metrics. Every expansion is grounded in suggestions that come directly from analyzing real conversation patterns.