This page explains every metric and term in AI QA so you can interpret your call analysis results. When a metric fails, see Address Metric Issues for step-by-step guidance on how to fix it.
Latency
| Metric | Description |
|---|
| Average Latency | End-to-end delay between user speaking and agent beginning spoken response. Measured in seconds — lower is better. |
| Latency P50 | 50th percentile (median) latency. Half of all responses are faster, half are slower. |
Sentiment Analysis
| Metric | Description |
|---|
| User Sentiment | Emotional state of the caller inferred from speech content, tone, and pitch — positive, negative, or neutral |
| User Positive Sentiment Rate | Percentage of user interactions with positive sentiment |
| User Negative Sentiment Rate | Percentage of user interactions with negative sentiment |
| Negative Sentiment Rate | Overall rate of negative sentiment in the conversation |
| Agent Sentiment | Emotional tone expressed by the AI during speech |
| Agent Positive Sentiment Rate | Percentage of agent responses with positive sentiment |
| Agent Natural Tonality Rate | How natural and human-like the agent’s tone sounds |
Transcription Metrics
| Metric | Description |
|---|
| WER (Word Error Rate) | Percentage of words incorrectly transcribed. Calculated as: (Substitutions + Insertions + Deletions) / Total Words × 100%. Lower = better. |
| Mistranscribed Entities | Count of specific entities (names, dates, numbers) incorrectly transcribed. Only critical factual errors that change meaning are counted. |
Call Quality Metrics
| Metric | Description |
|---|
| Interruptions | Count of times the user interrupted the agent. High counts may indicate the agent speaks too long or doesn’t respond appropriately. |
| Avg. Interruptions | Average interruptions per call across the cohort |
| Agent Naturalness | How human-like the agent sounded — pronunciation, intonation, pacing, turn-taking, absence of robotic patterns. Higher = more natural. |
| Natural Tonality Rate | Percentage of agent speech that sounds natural in tone and delivery |
AI Accuracy Metrics
| Metric | Description |
|---|
| LLM Hallucination Rate | Rate at which the LLM generated incorrect or fabricated information not supported by context or knowledge base |
| Agent Hallucination | How often the agent hallucinated. High values mean the agent may be providing incorrect information to users. |
| KB Recall | How effectively the agent retrieved and used relevant knowledge base information. Higher = better. |
| Metric | Description |
|---|
| Tool Call Accuracy | Rate at which the agent correctly invoked tools. Higher = agent uses right tools at right times. |
| Tool Call Inaccuracy | Rate at which the agent invoked incorrect tools (inverse of Tool Call Accuracy) |
| Custom Tool Success Rate | Percentage of custom tool calls that completed successfully |
| Avg Custom Tool Latency | Average time for custom tools to execute and return results |
Conversation Flow Metrics
| Metric | Description |
|---|
| Transition Accuracy | Accuracy of transitions between conversation nodes. Higher = agent follows intended flow. |
| Node Transition Inaccuracy | Rate of incorrect node transitions in conversation flows |
Call Resolution Metrics
| Metric | Description |
|---|
| Call Resolution Rate | Percentage of calls successfully resolved per your defined criteria |
| Average Score | Overall quality score calculated from resolution criteria and weighted scoring |
| Calls Analyzed | Total calls analyzed in the cohort |
Transfer Metrics
| Metric | Description |
|---|
| Transfer Success Rate | Percentage of calls successfully transferred |
| Transfer Wait Time | Average time users wait before transfer completes |
Statistical Terms
| Term | Description |
|---|
| P50 | 50th percentile (median) — half above, half below |
| Cohort | A filtered set of calls sharing common characteristics, analyzed with the same resolution criteria |
| Sampling Percentage | Percentage of matching calls included for analysis |
| Weekly Max | Maximum calls analyzed per week regardless of sampling percentage |
Resolution Criteria Terms
| Term | Description |
|---|
| AI Evaluated Condition | Qualitative criteria evaluated by AI from transcripts (e.g., “Call resolved”) |
| Performance Metric | Quantitative threshold a call must meet (e.g., latency < 2s) |
| Weighted Scoring | Assigns different weights to criteria to prioritize certain conditions over others |
| Calibration | Manual override of automatic metric evaluations for a specific call |
Some metrics show “N/A” when there isn’t sufficient data or when the metric doesn’t apply to a call type (e.g., transfer metrics on non-transfer calls).
