Scoring sales calls with AI, against your own scorecard
A sales-coaching client had a scorecard proven by hand. We taught a product to score real calls against it, with evidence quoted from every call. What made the scores worth trusting.
A sales-coaching expert had a method that worked: a scorecard of their own, proven by scoring calls by hand. The question was whether software could apply the same method to hundreds of calls, and whether a manager would believe the result.
Per call
1.6 s
To score a call with the fast scorer, down from 120–180 seconds
Evidence
Every score
Comes with a quote from the call that earned it
Method
The client’s
Scored against their own scorecard, criterion by criterion
The client’s method, not ours
The product does not invent a definition of a good call. It scores against the client’s own scorecard, criterion by criterion. Two methods run side by side: a fast rules-based one, and AI for the judgment the rules cannot make.
Every score shows its evidence
A score on its own invites an argument. A score with the sentence from the call that earned it invites a coaching conversation. Every criterion comes with quoted evidence from the call.
Clean data before clever scoring
Two problems had nothing to do with AI quality, and both would have misled a manager:
- Calls credited to the wrong person. An AI classifier now credits each call to the salesperson who actually made it.
- Training calls counted as sales calls. Coaching and training recordings were dragging the team’s average down. Excluding them changed the team’s average substantially. That is a data fix, not a change in performance, and we said so.
Fast enough to use
In our own first cloud version, scoring a call took 120–180 seconds. We found the cause and fixed it within the week: 1.6 seconds per call, about 75 times faster.
What comes next
Next on the roadmap: scoring every new call automatically as it arrives, and a calibration session where the client’s own scores are compared side by side with the product’s.
The examples in this article come from real engagements. Client details withheld; every figure comes from the client’s own data.
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