Call QA is how contact centres know whether their agents are doing their job well — not just guessing based on conversion numbers. Here is everything you need to know, from the basics to how AI is changing the practice.
Call QA (Call Quality Assurance) is the systematic process of evaluating customer-facing calls to verify that agents meet defined standards for communication quality, compliance, and customer experience — and then using those evaluations to improve performance.
These terms are often used interchangeably, but they mean slightly different things. Call monitoring is the act of observing calls — live or recorded. Call QA is the broader programme that includes monitoring, scoring against a rubric, tracking results over time, and feeding findings back to agents through coaching.
You can monitor calls without a QA programme (just listening in occasionally). But a true call QA programme requires defined criteria, consistent scoring, and a feedback loop. One is surveillance; the other is a system for improvement.
What does "good" look like on this type of call? Documented criteria covering communication, compliance, knowledge, and process — before a single call is reviewed.
Listening to or reading transcripts of calls and scoring them against the defined criteria. Can be done by QA analysts, team managers, or AI tools.
Aggregating scores across agents, teams, and time periods to identify patterns. Individual scores mean little; trends across many calls reveal systemic issues.
Using evaluation data to drive coaching conversations that change agent behaviour. Without this step, QA is just a monitoring exercise with no impact on outcomes.
The specific criteria vary by industry and call type, but most call QA programmes evaluate some or all of the following:
In small teams (under 20 agents), call QA is typically done by the team manager — listening to a handful of calls per agent per week and giving informal feedback. This works at small scale but is not systematic enough to produce reliable data.
In mid-size contact centres (20 to 200 agents), a dedicated QA analyst or small QA team handles evaluations. They score calls against a scorecard and pass results to managers for coaching. The challenge here is coverage — a QA team of 3 people reviewing 2 to 5% of calls across 100 agents produces thin, often biased data.
In large operations (200+ agents), QA is often a department with formal calibration sessions, dispute resolution processes, and monthly reporting. These operations are also most likely to adopt AI tooling to scale their coverage.
Traditional call QA has one fundamental problem: it is slow and expensive relative to the volume of calls most teams handle. A QA analyst reviewing 6 calls per hour can process 50 calls per day — a tiny fraction of what a 50-agent team generates.
AI call QA tools solve this by transcribing every call and automatically scoring it against predefined criteria. The AI does not replace the coaching conversation — that still requires a human — but it replaces the manual listening and scoring step, turning 15 minutes per call into seconds per call.
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