Glossary
Customer service QA glossary
The terms of quality assurance, QA automation, coaching and support metrics, defined in a line each. Every entry links to the full explanation.
35 terms · Read the glossary by theme
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- 100% QA Coverage
- 100% QA coverage means scoring every customer conversation, not a 2% to 5% manual sample. Here is what full-coverage QA changes for teams.
A
- Agent Coaching
- Agent coaching is the ongoing process of helping support agents improve using specific, evidence-based feedback. Here is how QA data makes it work.
- Agentic AI
- Agentic AI is AI that acts autonomously toward a goal, taking multi-step actions without step-by-step prompting. Here is what that means for customer service.
- Agentic QA
- Agentic QA uses autonomous AI agents to score 100% of customer conversations against your scorecard, automatically and continuously. Here is how it works.
- AHT (Average Handle Time)
- Average Handle Time (AHT) is the average time to handle one contact. Here is the formula, benchmarks, and why lower is not always better.
- AI Agent in Customer Service
- An AI agent is autonomous software that handles customer interactions end to end. Here is how it works and why its quality still needs to be measured.
- Auto QA (Automated Quality Assurance)
- Auto QA is the automated scoring of customer conversations against a quality scorecard, replacing manual sampling with full, continuous coverage.
- Auto-Fail in QA
- What an auto-fail means, examples of failures that earn one, and how it differs from simply giving a criterion a heavy weight on your scorecard.
C
- Call Center Attrition
- How call center attrition is calculated, why it runs so high in support, what each departure really costs and how attrition differs from turnover.
- CES (Customer Effort Score)
- CES measures how much effort a customer spent to get an issue resolved. Here is how it is asked, how it is scored, and why low effort predicts loyalty.
- Chat Assurance
- Chat assurance is QA for chat conversations. What the term means, why chat is not voice, the criteria worth scoring, and where it fits in a QA program.
- Coaching Framework
- A coaching framework is a repeatable structure for coaching conversations, like GROW, that keeps feedback consistent and action-focused.
- Containment Rate
- What containment rate measures, what it quietly leaves out, and why a contained conversation is not the same thing as a resolved one.
- Conversation Analytics
- Conversation analytics analyzes customer conversations at scale to surface topics, trends, quality and outcomes. Here is how it works and where it fits.
- Conversation Intelligence
- Conversation intelligence is software that automatically analyzes customer chat, email and voice conversations to surface quality and insight at scale.
- CSAT (Customer Satisfaction Score)
- The standard CSAT survey question, the formula with a worked example, typical benchmarks and the limits to keep in mind when you report it.
- CSAT vs NPS vs CES: The Difference
- CSAT measures satisfaction, NPS measures loyalty, CES measures effort. Here is how the three customer experience metrics differ and when to use each.
D
- DSAT
- The DSAT formula with a worked example, what counts as a negative rating, and why dissatisfaction is not simply the inverse of your CSAT score.
F
- FCR (First Contact Resolution)
- First Contact Resolution (FCR) is the share of issues solved in one interaction. Here is the formula, benchmarks, and why it drives CSAT and cost.
- First Response Time (FRT)
- First Response Time (FRT) is how long a customer waits for the first reply. Here is how it is measured across channels, benchmarks, and why it shapes CSAT.
I
- Interaction Analytics
- Interaction analytics analyzes customer interactions across voice, chat and email to surface quality, sentiment and trends at scale. Here is how it works.
L
- LLM-as-a-Judge
- LLM-as-a-judge uses a large language model to grade text or conversations against defined criteria. Here is how it works and why evidence matters.
N
- NPS (Net Promoter Score)
- NPS measures customer loyalty on a 0 to 10 scale. Here is the formula, what promoters and detractors mean, benchmarks, and how it links to QA.
Q
- QA Analyst in Customer Service
- A QA analyst reviews customer conversations against a scorecard to measure and improve quality. Here are the responsibilities and how the role evolves.
- QA Calibration
- QA calibration is when reviewers score the same conversation to align on standards, so scores stay consistent. Here is why it matters and how to run it.
- QA Rubric (Quality Scorecard)
- A QA rubric is the set of weighted criteria used to score customer service conversations for quality. Here are common criteria and how weighting works.
- QA Sampling in Customer Service
- Why support teams sampled conversations for QA, what a small sample can and cannot tell you, and why full coverage is replacing it for agent decisions.
- Quality Assurance in Customer Service
- What customer service QA involves in practice, how it differs from CSAT, and how review findings become coaching that makes service more consistent.
- Quality Monitoring Form
- A quality monitoring form is the scorecard reviewers use to evaluate a customer service conversation against agreed criteria. Here is how it works.
- Quality Monitoring in Customer Service
- Quality monitoring is the ongoing review of customer conversations against a scorecard to measure quality. Manual vs automated coverage explained.
R
- Real-Time Agent Assist
- Real-time agent assist is software that guides agents live during a conversation with suggested responses, next steps and knowledge. Here is how it works.
S
- Sentiment Analysis in Customer Service
- Sentiment analysis automatically detects the emotional tone of a conversation, positive, negative or neutral, from text or speech. Here is how it works.
- Speech Analytics
- Speech analytics is the automated transcription and analysis of voice calls, examining words, sentiment and silence to surface insight at scale.
T
- Text Analytics
- Text analytics is the automated analysis of written text such as tickets, chats and emails to extract topics, sentiment and intent for support QA and CX.
V
- Voice of the Customer (VoC)
- Voice of the customer (VoC) is capturing and analyzing customer feedback and needs across channels. Here is how conversation data strengthens it.
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