Turning Client Conversations into Revenue Intelligence
Built a custom Gen AI platform to analyze client conversations, identify booking opportunities, and surface the reasons appointments were missed or not scheduled.
The Challenge
A healthcare organization operating hundreds of clinics across the United States received thousands of inbound calls each day. Leadership suspected that booking opportunities were being missed at the front line, but identifying the root causes required managers to manually review call recordings and transcripts one by one. This process was slow, inconsistent, and impossible to scale across a large clinic network. Without a systematic way to analyze call outcomes, leadership lacked visibility into where prospective clients were dropping off and how much revenue was being lost.
Our Approach
We designed and implemented a Gen AI call analytics platform that automatically analyzed call transcripts and surfaced booking insights across the organization's clinic network. The system processed large volumes of call data through Python-based pipelines and classified each interaction across several operational dimensions, including: • answer status • caller type (new vs. existing client) • booking opportunity identification • booking outcome • reasons booking opportunities did not convert The model achieved 86%+ classification accuracy, enabling reliable large-scale analysis across thousands of daily interactions. Insights were integrated directly into executive dashboards and refreshed hourly, allowing operators to quickly identify missed calls and booking opportunities while there was still time to act. Leadership could monitor booking performance across the network while also drilling into call-level detail when deeper investigation was needed.
The Result
The platform provided the organization with its first scalable view of call conversion performance across hundreds of clinics. Analysis revealed more than $30M in annual revenue opportunity across the network by identifying missed booking conversions and unrealized client demand. Operators could now: • identify missed booking opportunities in near real time • respond quickly to missed calls and high-intent clients • diagnose operational causes of missed conversions • prioritize process improvements across clinics For the first time, leadership had a clear and actionable view of how inbound calls translated into revenue across the network.