Call center quality metrics: which KPIs actually matter and how to measure them
FCR, AHT, CSAT, NPS and QA score explained: definitions, formulas and the traps of eight key metrics — and why numbers built on a 1–2% sample can’t be trusted.
CallSea Blog
How to measure call center quality, build agent scoring criteria and deploy AI call analysis in line with GDPR and the EU AI Act — no marketing fluff.
FCR, AHT, CSAT, NPS and QA score explained: definitions, formulas and the traps of eight key metrics — and why numbers built on a 1–2% sample can’t be trusted.
Customer sentiment from transcript text is lawful — inferring agent emotions from voice is not. How to read customer mood across 100% of calls and act before an escalation grows.
Coaching on a random sample is a debate about anecdotes. How to build a 1:1 rhythm on data from 100% of calls: session agenda, per-criterion progress and pitfalls to avoid.
A complete call center scoring sheet: 14 criteria in three formats, weights summing to 100 points and a separate critical-error section — downloadable as a free XLSX template.
Article 5(1)(f) has banned inferring employees’ emotions from voice since February 2025 — with the AI Act’s highest fines attached. What call analytics remains legal, and six questions to vet your vendor.
AI evaluation of agents is a high-risk system. Employer duties, red lines and a vendor checklist — practical, no panic.
If your quality team already has a scorecard, most of the work is done — it just needs translating into formats AI can score unambiguously: checklists, point scales and critical-error rules.
A recorded customer call is personal data end to end — voice, transcript and AI score alike. The legal bases for recording, the DPIA triggers and 10 questions to ask your vendor.
What a deployment really looks like: connect recordings via SFTP or API, move your scorecard into criteria, calibrate on 30–50 historical calls and go live — typically within 14 days.
A random sample doesn’t tell the truth about team quality. Do the math on how much your QA team really hears — and what it means for catching critical errors.
Sample-based scoring turns 1:1s into a fight over three recordings. How to coach on trends, transcript quotes and human–AI calibration instead — with a 15-minute agenda.