Call scoring
Automated call scoring for contact center QA
KnownSense helps QA teams apply consistent scoring criteria to calls so supervisors can spend less time searching and more time coaching.
Built for
Quality analysts and supervisors who need scorecard consistency across agents and teams.
India-first buyer context
Where this fits in a real call operation
Automated scoring is valuable for teams that already have a quality checklist but cannot apply it consistently across daily call volume.
Common call examples
- New-agent calls
- Low-CSAT calls
- Random QA samples
- Campaign-specific review calls
Rollout checks
- Separate critical compliance items from softer coaching items.
- Calibrate scoring weekly during the first rollout.
- Track reviewer overrides so the scorecard improves over time.
Search intent
What teams want when they search for automated call scoring
Reduce subjective scoring drift across reviewers.
Route low-scoring and high-risk calls for QA review.
Connect scores to agent, team, and manager views.
Keep scoring logic aligned with active business rules.
Capabilities
A QA workflow that produces evidence, not just analytics
Scorecard-based review
Evaluate calls against the QA criteria your team already uses.
Reviewer prioritization
Bring weak calls, unusual patterns, and flagged conversations to the top of the queue.
Performance visibility
Track scores by agent and call volume so coaching is grounded in recent evidence.
Workflow
From call recording to QA action
Define scoring criteria
Map scorecards to required behaviors, quality signals, and compliance expectations.
Evaluate each call
KnownSense produces consistent score outputs and call context for reviewers.
Close the loop
Use scored calls for feedback, training, and follow-up with agents.
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
Two reviewers would normally disagree on empathy, but the scorecard separates observable behaviors such as greeting, discovery, ownership, and closure.
Decision it supports
The QA lead can pilot automated scoring against manager-reviewed calls and decide which criteria are ready to scale.
Operating fit
Built around real QA jobs
Built for QA scorecards, not only transcript search.
Combines score, flags, summary, and call ownership.
Helps teams audit more calls without losing reviewer control.
FAQ
Questions buyers ask before a demo
How does automated call scoring help QA teams?
It applies consistent criteria to more calls, which helps managers find coaching opportunities and review risky conversations faster.
Can automated scoring be calibrated?
Yes. QA leaders should calibrate scorecards and review samples regularly so scoring stays aligned with business standards.
Keep exploring
Related pages
Features
AI call quality monitoring
KnownSense helps QA teams monitor call quality with AI scoring, transcripts, summaries, flags, and agent performance visibility.
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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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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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