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Glossary

What Is Call Center QA?

DefinitionUpdated October 20266 min readunitQ Editorial

A definition of call center QA: what it measures, how it works, and why reviewing every call beats a 2 percent sample.

Call center QA (quality assurance) is the practice of reviewing and scoring customer calls, and increasingly chats, emails, and tickets, against a defined rubric that measures how accurate, compliant, on-tone, and effective each interaction was, then using those scores to coach agents and fix systemic problems. It is support QA applied to the contact center, where the primary channel is voice and the volume and compliance stakes tend to be highest.

What "QA" means in a call center

In a call center, “QA” is short for quality assurance, and it refers to both a function and, historically, a role. The function is evaluating interactions against a standard. The role is the QA analyst who does that evaluation. Modern call centers increasingly automate the scoring itself, so “QA” now describes the measurement program more than a single job. For the broader practice across all support channels, see support QA.


How call center QA works

A reviewer, or now an AI system, scores interactions against a scorecard: was the customer verified correctly, was the information accurate, were required disclosures given, was the tone right, and was the issue resolved. Traditionally QA teams sampled only a handful of calls per agent per month, reaching about 1 to 2 percent of interactions, enough to coach but far too few to be representative. 1 Speech-to-text and AI scoring now make it possible to evaluate up to 100 percent of calls, which removes the sampling bias that made old QA scores unreliable. 1


What call center QA measures

  1. 1

    Accuracy and policy. Correct information, correct handling.

  2. 2

    Compliance. Identity verification, mandatory disclosures, and regulated-industry rules, the reason QA is non-negotiable in finance, healthcare, and collections.

  3. 3

    Tone and empathy. Soft skills, de-escalation, and brand voice.

  4. 4

    Resolution. First-contact resolution and its link to CSAT.

  5. 5

    Process. Scripting, escalation, after-call work, and documentation.


Why it matters

Calls are high-stakes and, until recently, almost entirely unaudited. Consider a regulated example: an agent who skips identity verification on a single account-change call can create real compliance exposure, and under a 2 percent sampling model that call almost certainly never gets reviewed. A pattern of unresolved calls does quieter damage, driving repeat contacts and churn that never shows up in a QA score built on a tiny sample. Full-coverage QA makes both visible, and when the QA signal connects to the rest of the feedback stack, it points past agent performance to the product and policy issues generating the calls in the first place.

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FAQ

QA every call, not a 2% sample

Manual QA reaches only a small sample of calls, while AI scoring can cover up to 100 percent. Close the gap where compliance misses and unresolved patterns hide. See how unitQ Support works.

Related guides

Sources 1 references
  1. SQM Group, "Automated vs. Manual QA: How to Improve Accuracy, Insights, and Cost Efficiency." sqmgroup.com. Accessed October 2026.