+27% to sales conversion in 2 months
An industrial-equipment seller: 15 reps, ~100 calls a day, quality control done by spot-checking recordings for 5–10 hours a week. AI analysis of 100% of conversations lifted conversion by 27% and cut the manager's QA time to 30 minutes.
| Sheet | 3.3 · “Cases” series |
| Project | AI call-quality control |
| Industry | Industrial equipment |
| Author | EDEMIUM |
| Status | Live |
What the client came with
Where conversion was leaking
The team ran ~100 calls a day, but control rested on one person: the manager spent 5–10 hours a week spot-checking recordings. Whatever made it into the sample got judged by ear; everything else stayed a black box. Profit slipped 19% over 4 months — and there was simply no one to dig for the cause inside the calls by hand.
The scoring was subjective: two of the same rep's calls could get opposite verdicts depending on the reviewer's mood and fatigue. Systematic analytics on reps and on customer objections stood at zero.
Objections like “too expensive,” “not for us,” and “let me think” came up on the line every day — but no one counted them, no one coached the reps on them, and no one could see which of the 15 people kept losing deals at the exact same point.
What we built
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01 · INTEGRATION
CRM and telephony with auto-loading
CRM and telephony integration: every call recording lands in the analysis system automatically. No manual exports, no “I'll listen to it over the weekend.”
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02 · ANALYSIS
Transcription + AI review of 100% of calls
Speech is transcribed to text automatically, AI analyzes every conversation and flags “warning signals”: phrases like “too expensive,” “not for us,” “let me think.”
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03 · RANKINGS
Tables with rankings and recommendations
The system compiles tables of rep rankings and per-rep recommendations on its own. The manager is left with 30 minutes a week instead of 5–10 hours of listening.
Results in numbers
- Sales conversion
- +27% in 2 months of the system running
- QA time
- −80% was 5–10 h/wk → 30 min
- Call coverage
- 100% was: spot-check listening
- Reps ranked
- 15 objective scores for each
| Metric | Before | After |
|---|---|---|
| Sales conversion | — | +27% in 2 months |
| Manager's QA time | 5–10 h/wk | 30 min/wk (−80%) |
| Call-analysis coverage | spot-check | 100% of calls |
| Rep scoring | subjective, by ear | objective rankings |
| Warning signals (“too expensive,” “let me think”) | manual, by ear | auto-detected across 100% of calls |
| Analytics on reps and objections | — | automated tables |
| Profit over the 4 months before implementation | −19% | — |
Timeline and status
The system was built on top of the existing CRM-and-telephony stack: auto-loaded recordings, transcription and an AI review of every conversation. A measurement 2 months in confirmed it: conversion was up 27%.
The system is live now: it analyzes all ~100 calls a day across 15 reps. The manager reviews ready-made rankings and recommendations — 30 minutes instead of 5–10 hours of listening a week.
| Measurement | First 2 months of the system running |
| Status | Live |
| Capacity | ~100 calls/day · 15 reps |
| Exit point | Measure KPIs on the pilot: target hit → scale |
I want the same in my sales team
Start with an automation roadmap: in 14 days we'll show you which calls your reps lose deals on and where conversion is leaking.