Conversation intelligence

Conversation intelligence platforms record and transcribe sales calls, meetings or support interactions, then apply natural-language processing to extract structured signals: talk-to-listen ratio, competitor mentions, pricing objections, next steps, and sentiment shifts during the call. The output is searchable across an entire team's calls, not just the one a manager happened to sit in on.

This differs from general speech analytics, which is often built for contact-center volume metrics like average handle time, in that conversation intelligence is oriented toward deal coaching and deal risk: flagging when a deal has gone quiet on pricing, or when a rep is talking too much relative to the prospect. Many tools layer sentiment analysis on top of the transcript to flag calls that went poorly even when no explicit objection was raised.

Sales leaders use conversation intelligence to standardize coaching, since it lets managers review patterns across many reps instead of relying on occasional call shadowing, and some tie transcript signals into win rate models to predict deal risk earlier. A pitfall is over-trusting automated sentiment or objection tags without spot-checking transcripts, since accents, jargon and crosstalk can degrade transcription quality and produce misleading signals.