Speech Analytics for Sales Calls

Most sales call recordings are never listened to

A B2B software company with eight account executives records every call. That is roughly 150 calls a week. The sales manager listens to perhaps three, usually the ones where a deal went wrong, and usually too late to help.

Speech analytics turns that archive into something searchable and measurable. Every call is transcribed, speakers are separated, and a model pulls out what matters: the prospect mentioned a competitor, raised pricing twice, agreed a follow-up on Thursday. Patterns across hundreds of calls become visible in a way they never are from one manager's listening.

It is also easy to build a system that produces a great deal of data and changes nothing. The difference is in choosing what to measure.

Signals worth extracting from sales calls

• Next steps and commitments. What was agreed, by whom, by when. Written back to the CRM, this alone saves admin and stops deals stalling quietly.
• Objections. Price, timing, integration, authority. Counted across calls, they show which objections lose deals and which the team handles well.
• Competitor mentions. Which names come up, at what stage, and in what context.
• Questions the prospect asked. A goldmine for marketing content and product priorities.
• Talk-to-listen ratio and monologue length. Crude but useful coaching indicators, especially for newer reps.
• Discovery coverage. Whether budget, timeline and decision process came up at all in a first call.

Six signals is plenty to start. Most teams that begin with a vendor's default dashboard of thirty metrics end up looking at the same four.

How the pipeline works

1. The call recording arrives from your dialler, meeting tool or phone system
2. Speech-to-text produces a transcript with timestamps
3. Diarisation labels which speaker said what, ideally matched to rep and prospect
4. A language model extracts the chosen signals into structured fields, with the quote that supports each
5. Results write to the CRM record for that deal and to a reporting store

Transcription quality has become very good for clear audio in major languages. Accents, crosstalk, poor headsets and domain jargon still cause errors, and a transcription error in a product name can flow through to a wrong competitor count. Supplying a glossary of your product and competitor names helps noticeably. Our post on getting value from AI transcription covers the transcript side in more detail.

Buy a platform or build around your stack?

For many teams, a platform is the right first step, and we say so. A custom build tends to make sense when you need signals specific to your sale, such as regulatory questions in financial services, when calls happen across tools no single platform covers, or when you want call data joined with product usage and revenue in your own data warehouse. That is the kind of work our AI integration projects usually cover.

Consent and recording rules

Before analysing calls, make sure you can lawfully record them. In the UK, businesses generally need to tell callers they are being recorded and have a lawful basis for keeping and analysing the recording. Some US states require every party's consent. Across the EU, data protection rules apply to voice recordings and transcripts just as to any personal data.

• State at the start of the call that it is recorded and why
• Set retention periods for audio and for transcripts separately
• Strip payment card details and other sensitive data from transcripts automatically
• Tell your own staff how call analysis will and will not be used

Where speech analytics goes wrong

The fastest way to kill a call analytics rollout is to put a single 'call score' next to each rep's name on a leaderboard.

Composite scores hide the reasoning, invite gaming, and make experienced reps distrust the whole system. Use signals for coaching conversations and deal reviews, with the transcript quote attached, rather than as a ranking.

Other common problems: transcripts that nobody checks for accuracy in the first month, signals that do not map to CRM fields so they live in a separate tool nobody opens, and treating correlation as advice. If won deals mention pricing less, that may be because good-fit prospects worry less about price, not because reps should avoid the topic.

A sensible first month

At SpiderHunts we would take a sample of 100 past calls with known outcomes, extract four or five signals, and have the sales manager check 20 of them against the recordings. If the extraction matches their judgement, turn on automatic next-step capture to the CRM first. It saves every rep time on day one, which buys goodwill for the coaching signals that follow.

Only after that would we add objection and competitor tracking, reviewed monthly with the whole team rather than rep by rep. If you already run a support or service desk, the same pipeline can later be pointed at those calls too; the quality-assurance angle is different, and our post on AI call centre quality assurance covers it.

Frequently asked questions