The starting point: QA with insufficient sampling
The call center handled 3,500 monthly calls across 45 agents in three queues: technical support, billing, and sales. The 2-analyst QA team could manually audit 80-110 calls per month — a 2.5-3% coverage rate.
The consequences: quality issues took 3-4 weeks to detect, agent feedback was sporadic and inconsistent, and agents with systematic problems could go months without specific training. Customer satisfaction (CSAT) had been stuck at 3.6/5 for over a year.
Project objectives
Increase QA coverage to maximum possible without headcount increase, reduce time-to-detection of systematic issues from 4 weeks to 1 week, and improve CSAT to 4.2/5 within 6 months.
The CallsIQ implementation
Implementation completed in 3 days: telephony platform integration for automatic call capture, QA criteria configuration (8 dimensions, weights by queue importance), and 4-hour QA team training.
From day 4, 100% of calls were automatically transcribed and received a provisional QA score. Analysts shifted from listening to full calls to reviewing automatic reports for lowest-scoring calls — confirming or adjusting the automatic evaluation and adding specific feedback notes.
Results after 6 months
QA coverage went from 3% to 100%. The QA team processed the equivalent of 50x more calls with the same headcount, because each call took 5 minutes instead of 25. Time-to-detection of problematic patterns dropped from 4 weeks to 5 days.
CSAT rose from 3.6 to 4.2 in 6 months, exceeding the target. The team attributes 60% of that improvement to the more frequent and specific feedback agents began receiving, and 40% to process improvements identified through full-call analysis.
Unexpected success factor: Agents themselves started using call transcripts to prepare briefs for complex cases. Transcription became an agent work tool, not just a QA team tool — something management hadn't anticipated.