Słownik

Słownik kontroli jakości obsługi klienta

Pojęcia z zakresu kontroli jakości, automatyzacji QA, coachingu i wskaźników obsługi klienta, każde zdefiniowane w jednym zdaniu. Każdy wpis prowadzi do pełnego wyjaśnienia.

Liczba pojęć: 35 · Słownik według tematów

#

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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