The agent assembles each deal's calls, tags them won or lost, finds the patterns that separate the two, and reports them with example moments and coaching notes.
The team knew which deals they won and lost, but the why was buried inside hundreds of call recordings. Understanding what actually separates a win from a loss meant manually reviewing calls, which nobody had time for. As a result, win/loss reasoning stayed anecdotal, inconsistent, and unbacked by data.
A batch-analysis agent that assembles each deal's full call history, tags it won or lost based on its CRM stage, and analyzes the entire corpus to surface the patterns that recur in wins versus losses. Each finding comes with its win-rate and loss-rate (with raw deal counts), a confidence level, paraphrased example moments from real calls, and a concrete coaching recommendation. It runs on a schedule over the growing body of calls, and reports patterns at the team level rather than scoring individual reps.
The team finally has data-backed answers to why deals are won and lost, instead of gut feel. The first version is live, and the client was pleased with the quality of the patterns it surfaced, turning a pile of call recordings into structured, coachable insight.