AI evaluates customer conversations by transcribing the call, segmenting it into phases, and detecting events such as required disclosures, discovery, and resolution. It then scores the interaction against a defined rubric, attaches quotes and timestamps as evidence, and flags compliance or risk issues. Beyond scores, it extracts reasons for contact and friction points so teams can coach consistently and spot trends across all calls, not just a sample.
Quality varies by agent, caller, and context. Important moments are quick and often subtle. Supervisors know what good looks like, but sampling a handful of calls rarely shows the patterns that shape outcomes.
AI changes this by turning conversations into structured, explainable data. When it works, it functions like an instrument: consistent, evidence-backed, and usable across every call instead of a small sample.
Behavior and process. Did the call follow the expected flow—greeting, verification, discovery, resolution, and close—and were handoffs and holds handled appropriately.
Communication quality. Clarity, empathy, tone, confidence, active listening, and professionalism show up in how questions are asked, how options are framed, and whether the customer feels understood.
Compliance and risk. Required disclosures, restricted phrases, data handling, and policy adherence are checked as events with positive and negative evidence.
Customer signals. Intent, objections, friction, sentiment shifts, and escalation risk are identified as a customer signal the organization can act on.
For voice calls, evaluation begins with transcription and diarization. The system assigns words to the right speaker and normalizes punctuation so downstream analysis is consistent.
The call is split into meaningful phases such as greeting, verification, troubleshooting, offer, and close. Within these phases, the system detects events like a clear introduction, identity verification or consent, effective discovery, confirmation of resolution, proper hold and transfer handling, and a closing summary with next steps.
The conversation is scored against your scorecard category by category. Each score includes supporting evidence and a short rationale. This is an explainable evaluation approach: findings are anchored to transcript quotes and timestamps so reviewers can see exactly why a point was awarded or missed.
Beyond the score, the same analysis surfaces operational context: reasons for contact, where customers get stuck, knowledge gaps, process breaks, and the triggers that tend to create escalations or cancellations. These insights guide coaching and upstream fixes.
Keyword spotting reports whether certain words appeared. AI call scoring interprets context: what the customer asked, what the agent did next, whether policy was followed, and whether the exchange moved the issue toward resolution. The same phrase can mean different things depending on turn-taking and timing; for example, “I understand” can be empathy or filler. Context and evidence are what make the difference.
Define the rubric clearly. Specify what counts as Satisfactory versus Needs Improvement in practical language tied to observable moments.
Use evidence by default. Attach transcript quotes and timestamps for both positives and misses. Evidence closes debate and shortens coaching.
Calibrate against human review. Compare AI and supervisor scores on a standing sample. Close gaps and watch for drift as policies and products change.
Review edge cases on purpose. High-emotion calls, escalations, and outliers reveal where instructions or models need tightening.
Transcription errors and overlap. Noisy audio and speaker bleed reduce accuracy. Better capture settings and diarization improve downstream evaluation.
Domain terminology. Industry-specific terms and abbreviations can be misread. Add specialized vocabulary and examples to reduce misses.
Policy nuance. Partial compliance often looks close to correct. Tighten rubric language and include negative evidence (what did not happen) to make misses explicit.
These fixes are routine. The goal is not perfection on day one but a system that is explainable, auditable, and steadily improving with real call feedback.
When scoring and insights cover every call, patterns appear earlier and coaching becomes consistent. Compliance misses surface with evidence, not anecdotes. Trends in reasons for contact and friction move from quarterly narratives to daily signals. The result is a shorter path from what customers say to what the operation does next.
AI Call Quality Monitoring Explained (And Why It Works Better Than Manual Review)
