Glossaire
Glossaire du contrôle qualité du service client
Les termes du contrôle qualité, de l’automatisation de la QA, du coaching et des indicateurs du support, définis chacun en une ligne. Chaque entrée renvoie à l’explication complète.
35 termes · Parcourir le glossaire par thème
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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
- Containment rate is the share of conversations an AI agent or bot ends without a human. The formula, a worked example, and why contained is not resolved.
- 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 Meaning in Customer Service and Call Centers
- DSAT meaning in customer service: the dissatisfaction score formula with a worked example, what counts as negative, and why DSAT is not the inverse of CSAT.
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 and compare results so scores stay consistent. Why it matters for support teams and how to run it.
- QA Rubric (Quality Scorecard)
- A QA rubric is the set of criteria, rating levels and weights used to score support conversations. See a chat and email example and how the score is worked out.
- QA Sampling in Customer Service
- QA sampling means reviewing a subset of support conversations. Learn what a small sample can and cannot tell you, and why full coverage is replacing it.
- 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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