Guide
A practical guide to call center quality assurance
A strong QA program defines what good service looks like, measures calls consistently, and turns findings into better coaching and process improvement.
Built for
QA leaders building or improving a contact center quality assurance program.
India-first buyer context
Where this fits in a real call operation
A useful QA guide should help a founder or BPO owner build a practical review loop before the team gets buried in recordings.
Common call examples
- Sample calibration calls
- Training calls
- Escalation examples
- Calls from new campaigns
Rollout checks
- Write the scorecard in language reviewers can apply consistently.
- Keep calibration examples for future team leads.
- Tie QA findings to one clear coaching action.
Search intent
What teams want when they search for call center quality assurance guide
Define consistent QA criteria.
Use scorecards to reduce review ambiguity.
Review risk and compliance signals deliberately.
Connect QA findings to coaching and training.
Capabilities
A QA workflow that produces evidence, not just analytics
Start with measurable criteria
Scorecards should reflect customer outcomes, policy requirements, and agent behaviors that can be reviewed consistently.
Calibrate reviewers
QA managers should compare reviews regularly so scoring remains fair and useful.
Use AI for prioritization
AI helps find calls worth reviewing, but human QA teams should own final decisions.
Workflow
From call recording to QA action
Define the rubric
Turn service expectations into scorecard criteria and script checks.
Review representative calls
Use AI signals to find samples that reveal trends and risks.
Coach and recalibrate
Close the loop with feedback, training, and scorecard updates.
Example evidence
A reviewable signal a manager can act on
KnownSense is designed to keep AI output reviewable: the manager sees the summary, score, transcript evidence, and the call record before taking action.
Signal to inspect
A calibration sample shows one reviewer penalizing tone while another focuses on missed resolution steps.
Decision it supports
The QA owner can refine the rubric so reviewers score observable behavior consistently.
Operating fit
Built around real QA jobs
Supports the full QA loop from call analysis to coaching.
Helps teams move from manual samples to evidence-driven prioritization.
Designed around QA manager and supervisor workflows.
FAQ
Questions buyers ask before a demo
What are the main parts of call center QA?
Call center QA usually includes scorecards, call monitoring, compliance checks, calibration, agent feedback, and performance tracking.
How often should QA scorecards be updated?
Scorecards should be reviewed whenever policies, customer expectations, products, or call types change, and they should be calibrated regularly.
Keep exploring
Related pages
Resources
Manual QA vs AI call quality monitoring
Compare manual call QA with AI call quality monitoring, including sampling bias, reviewer time, calibration, false positives, and human review.
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Sample AI call quality report
See what a practical AI call quality report should include: score summary, transcript evidence, QA flags, coaching notes, and reviewer decisions.
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Call monitoring scorecard template
A practical call monitoring scorecard template for evaluating greeting, discovery, resolution, compliance, empathy, and closure.
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Call center quality assurance software
KnownSense is call center quality assurance software for scoring calls, detecting risk, monitoring script adherence, and coaching agents.
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