Contact Center & Quality Management Terms

This glossary offers over 140 definitions of both general and AI-related terms essential for understanding contact center operations and quality management practices.

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Contact Center AI Terms Quick Reference

AI Assist

A feature  utilizing artificial intelligence to support agents in contact centers. This includes automating post-call work with call summaries, AI coaching, key fact extraction, call categorization, automated responses etc.  

AI Call Categorization:

The classification of calls into predefined categories based on content, purpose, or outcome, aiding in the management, analysis, and follow-up of customer interactions.

AI Call Scoring (also Auto Score Card):

An automated tool that evaluates customer interactions and measures agent performance using AI to analyze voice recordings and call transcripts, generating comprehensive scorecards.

AI Call Summary:

A brief overview of the key points and resolutions from a customer call, created through AI-driven Agent Assist tools, for quick reference and understanding.

AI Coaching:

Utilizes AI technologies that analyze agent performance and provide personalized feedback based on the context of the conversations with training steps for agents.

AI-Powered Quality Assurance (QA):

An automated quality assurance system that uses artificial intelligence (AI) to evaluate customer conversations for compliance with service standards, enhancing customer service analysis and improvement.

Artificial General Intelligence (AGI):

Theoretical research aiming to develop AI with human-level cognitive functions, capable of learning autonomously across different knowledge domains without specific training.

Artificial Intelligence (AI):

Defined by Columbia’s School of Engineering as the development of computers and robots capable of mimicking and surpassing human capabilities, including the use of smart devices and voice assistants. AI is a broad field that encompasses Machine Learning, Deep Learning, Natural Language Processing, and more.

Auto-Redaction (AI-Powered):

An automated process using Named Entity Recognition (NER) and Large Language Models (LLM) that identifies and removes sensitive information, like credit card or social security numbers, from recordings and/or transcripts.

Automated Quality Management (AQM):

A technology-driven approach to streamline and enhance the quality management processes in a contact center, ensuring consistent service quality.

Conversational AI:

A subset of NLP focusing on creating chatbots or virtual agents for dialogue with humans, incorporating technologies like speech recognition, NLP, Natural Language Understanding (NLU), Natural Language Generation (NLG), and Text-to-Speech.

Conversation Intelligence:

Advanced analytics and AI applied to analyze customer interactions based on transcribed call recordings, providing insights into behavior, preferences, and sentiment to improve customer experience and agent performance.

Data Extraction:

The process of obtaining relevant information from unstructured data sources, like call transcripts, using AI and NLP techniques, to inform analysis, reporting, and decision-making.

Deep Learning (DL):

A subset of ML that uses artificial neural networks to mimic human brain learning processes, enabling more complex outcomes. It requires large amounts of labeled data and computing power.

Generative AI:

A subset of ML that generates new, original content by learning from data and models. It employs technologies like Generative Adversarial Networks (GANs) and transformer models.

Key Fact Extraction:

The process of extracting key facts from unstructured data sources, such as call recording transcripts, using NLP and other AI techniques.

[Large] Language Model (LLM):

A probabilistic model that can generate text based on the data it was trained on, notable for achieving general-purpose language understanding and generation.

Machine Learning (ML):

A subset of AI that uses algorithms to enable machines to learn from data without being explicitly programmed. It involves recognizing patterns and insights from data to perform tasks like speech recognition or content generation.

Named Entity:

In data extraction and NLP, it refers to the identification and classification of important information into predefined categories, such as names and locations, facilitating quick extraction of critical data from customer interactions.

Natural Language Processing (NLP):

Focuses on enabling computers to understand and manipulate human language, making it useful for applications like sentiment analysis and chatbots.


Abandon Rate:

The percentage of calls where the caller hangs up before reaching an agent. It indicates customer patience and the efficiency of the call center's response times.

After Call Work (ACW):

After Call Work refers to a range of tasks an agent completes immediately after a customer interaction, such as reviewing notes or summarizing the call. This work is traditionally done manually and can take between two to five minutes, but now it is becoming more and more AI-supported.

Agent Analytics:

This involves analyzing contact center agent performance across multiple channels, utilizing diverse tools to assess effectiveness and areas for improvement.

Agent Evaluation:

The process of assessing and measuring the performance of agents to ensure they are providing customers with high-quality service. Traditionally done manually by supervisors, it now leverages call scoring, voice analytics, sentiment analysis, spoken keyword expression syntax, and auto-redaction, with technologies like MiaRec offering specialized evaluation forms.

Agent Performance:

A comprehensive evaluation of an agent's effectiveness, gauged through various call center metrics and Key Performance Indicators (KPIs).

Agent Turnover Rate:

The yearly percentage of agents leaving the contact center, a critical metric given the industry's high turnover challenges.

Arrival Rates:

The frequency of incoming calls within a set period, indicating the volume of customer interactions and the demand on resources.

Average Handle Time (AHT):

The total average duration taken by an agent to handle a call or interaction, including talk time, hold time, and after-call work. It's a critical metric for assessing efficiency and customer service quality.

Average Hold Time:

The average duration callers are placed on hold during a call. This metric helps in understanding the effectiveness of call handling and resource allocation.

Average Speed of Answer (ASA):

The average time it takes to answer incoming calls, a key indicator of responsiveness and customer service efficiency.

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The practice of comparing business processes and performance metrics to industry bests and best practices from other companies, aiming to improve contact center operations.

Blended Agents:

Agents capable of handling both inbound and outbound calls, enhancing the contact center's flexibility and responsiveness.

Business Continuity Planning (BCP):

The creation of systems for prevention and recovery to deal with potential threats to a company, including operational strategies for contact centers during unforeseen events.

Business Process Outsourcing (BPO):

The delegation of specific business operations, such as customer service, IT, or HR, to external vendors, enabling focus on core business activities.

Bounce Rate (Email/Chat):

The rate at which emails or chats are not delivered or connected with an agent, indicating potential communication channel or customer engagement strategy issues.

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Call Blending:

The practice of enabling agents to handle both inbound and outbound calls as demand fluctuates, maximizing efficiency and resource utilization.

Call Calibration:

The process where contact center managers work with external evaluators to ensure consistency and accuracy in assessing customer service calls.

Call Center Audit:

A systematic review of the processes, workflows, and guidelines in a contact center, aimed at ensuring compliance and identifying areas for improvement.

Call Center Coaching:

A developmental strategy for agents to acquire new skills and improve their performance through feedback and targeted training sessions.

Call Center Compliance:

Adherence to both internal policies and external regulations governing contact center operations.

Call Center Efficiency:

The effectiveness with which a contact center achieves its annual objectives, focusing on reducing inefficiencies and enhancing performance.

Call Center Evaluation Form:

A standardized document used by Quality Assurance analysts to rate and provide feedback on the performance of agents during calls.

Call Center Monitoring Form:

A tool used to assess agent interactions with customers against predefined criteria to ensure quality and effectiveness.

Call Center Staffing Model:

A strategy for adjusting staffing levels based on call volume and customer wait times to optimize service levels.

Call Escalation:

The process of transferring a customer issue that exceeds an agent’s authority or expertise to a supervisor or manager for resolution.

Call Handling Time:

The total time an agent spends with a caller, including conversation, hold time, and any related after-call work.

Call Queue:

A virtual line that incoming calls are placed in, waiting to be answered by the next available agent.

Call Routing:

The process of directing incoming calls to the most appropriate agent or department based on specific criteria, such as skill set or inquiry type.

Call Scoring:

Call Scoring is a process used to assess the quality of customer service in contact centers. It involves analyzing the performance of agents during customer interactions, such as phone calls and chats, and assigning a numerical score that indicates how well they handled the conversation. This score can be based on factors such as call time, customer satisfaction ratings, response times, and such. See also Auto Score Card.

Call Scripting:

The use of predefined scripts by agents to ensure consistency and accuracy in customer interactions, often used in training or to maintain compliance.

Call Sequencing:

The strategy of prioritizing incoming calls based on predefined rules, ensuring that calls are answered in an order that aligns with business objectives.

Call Tagging:

Assigning labels or tags to calls in order to categorize them for easier retrieval, analysis, or reporting purposes.

Call Tracking:

Monitoring and recording data about incoming and outgoing calls in the contact center to analyze performance, customer trends, and campaign effectiveness.

Call Wrap-Up Time:

The time spent by an agent completing post-call tasks necessary to conclude an interaction, not including the conversation time.

Campaign Management:

The planning, execution, and analysis of outbound contact strategies to achieve specific business goals, such as sales targets or customer engagement.

Capacity Planning:

The process of forecasting contact center workload and determining the necessary resources, such as staffing and technology, to meet customer demands efficiently.

CCaaS (Contact Center as a Service):

A cloud-based solution that provides contact center functionalities as a service from a third-party vendor, offering scalability, flexibility, and access to advanced technology without significant infrastructure investment.


An AI-powered tool that simulates human conversation, allowing customers to receive immediate, automated responses to inquiries via chat interfaces.

Cloud Contact Center:

A virtual contact center hosted on cloud infrastructure, offering scalability, flexibility, and access to advanced technologies without the need for physical hardware.

CRM Integration:

The process of connecting the contact center's technology platform with customer relationship management (CRM) software, enabling agents to access and update customer information in real time.

CSAT (Customer Satisfaction):

A measure of how pleased customers are with a product, service, or experience, focusing on immediate satisfaction rather than long-term loyalty.

Customer Engagement:

The various ways a company interacts with its customers across multiple channels to foster a strong, lasting relationship and encourage brand loyalty.

Customer Experience:

The overall perception a customer has of a company, shaped by all interactions with the contact center and influencing the decision to remain with or move to a competitor.

Customer Journey Mapping:

The practice of visually representing the customer's experience through different touchpoints within the organization, helping to identify opportunities for improvement in service delivery.

Customer Sentiment:

Insights into a customer’s feelings, attitudes, and opinions about a brand or product, essential for understanding and enhancing customer satisfaction.

Customer Sentiment Analysis:

An AI-driven technique that evaluates customer emotions and opinions regarding a product, service, or brand to inform business strategies.

Customer Verification:

The procedure contact centers use to confirm a customer’s identity and validate their information for security and service customization purposes.

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Data Monitoring:

The proactive examination of key business data to ensure its quality and compliance with set standards and protocols.

Dead Air:

The silent periods during a contact center interaction when there is no verbal communication between the customer and the agent.

Deep Learning (DL):

A subset of ML that uses artificial neural networks to mimic human brain learning processes, enabling more complex outcomes. It requires large amounts of labeled data and computing power.

Digital Transformation:

The integration of digital technology into all areas of a business, changing how operations are conducted and value is delivered, particularly within contact centers through cloud-based platforms, AI, and machine learning.


The process of segmenting call recordings to differentiate between speakers and periods of silence, analyzing the duration of speech and silence during the call.

Disposition Codes:

Short codes used by agents after a call to categorize the outcome or nature of the interaction, aiding in call outcome analysis and performance adjustments.

Drop Call Rate:

The percentage of calls disconnected before completion, indicating potential network or efficiency issues.

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Experience Map (Customer Journey Map, CJM):

A visual representation of the customer's journey with a business, showing all touchpoints from initial engagement to conversion or action.

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FAQ (Frequently Asked Questions):

A compilation of common questions and answers on a particular topic, used to address customer queries efficiently.

First Call Resolution (FCR):

The rate at which customer issues are resolved on the first call, a key indicator of customer service efficiency.

First Response Time (FRT):

The time elapsed from customer inquiry submission to the initial response by a contact center agent, measured in business hours to account for non-working times.


The prediction of future call volumes and workloads based on historical data, essential for staffing and resource planning in contact centers.

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Implementing game-design elements in non-game contexts to motivate and engage contact center employees, enhancing training and performance management efforts.


A categorization within contact centers based on criteria such as location or department, facilitating organized management and task allocation, with roles including group members and managers.

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Handle Time:

The total duration spent by an agent on a call or interaction, encompassing talk time, hold time, and after-call work.

Hosted Contact Center:

A cloud-based service managing all forms of customer interactions off-site, providing scalability and reducing the need for physical infrastructure.

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Interactive Voice Response (IVR):

An automated system that interacts with callers to collect information and direct calls to the appropriate recipient, improving efficiency and customer service.

IVR Deflection:

The strategy of using IVR systems to guide callers towards self-service options, aiming to reduce direct agent interactions and optimize resource allocation.

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Key Performance Indicator (KPI):

Quantitative measures used to gauge the performance of various aspects of a contact center's operations, such as customer service effectiveness.

Knowledge Base:

A centralized digital repository containing helpful information, including FAQs and guides, that customers and agents can access to find quick answers.

Knowledge Management:

The systematic management of an organization's knowledge and information, aimed at bolstering learning and understanding across the contact center.

Knowledge Process Outsourcing (KPO):

Outsourcing of tasks that require significant knowledge or expertise, such as analysis or research, to specialized external firms.

Keyword Extraction:

The application of Natural Language Processing (NLP) to identify important words and phrases from customer conversations, aiding in trend analysis and insights gathering.

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Details about other data, such as the caller's IP address or phone number, crucial for analyzing customer interactions.

Multichannel Support:

Providing customer support through various channels like phone, email, live chat, and social media without integrated management.

Multimedia Queuing:

Managing customer interactions across different media types, including voice, email, chat, and social media, in a unified queue.

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NPS (Net Promoter Score) Follow-Up:

Actions taken in response to customer feedback from NPS surveys to address concerns and improve service quality.

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Omnichannel Customer Service:

A seamless customer service experience across various channels, integrating communication paths for efficiency and consistency.

On-hold Time:

The duration customers wait on hold during a call, indicating the efficiency of agent availability and resource management.

Outbound Call:

Calls made from the contact center to customers for purposes like sales, surveys, or updates.


Routing excess calls to manage high call volumes and maintain service levels by reducing customer wait times.

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PCI Compliance:

Adhering to security standards to protect credit card information during transactions, ensuring data safety.

Predictive Dialer:

An automated system that makes simultaneous calls, connecting answered calls with available agents to enhance outbound call productivity.

Presence Management:

A system allowing agents to indicate their availability status, improving internal communication and resource planning.

Proactive Customer Service:

Initiating contact with customers to inform them about issues or solutions before they reach out with inquiries or complaints.

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Quality Assurance (QA):

Evaluating customer interactions against quality standards to ensure high levels of service and performance improvement.

Quality Monitoring:

Monitoring and assessing agent-customer interactions to evaluate conversation quality and identify training or performance enhancement opportunities.

Queue Management:

Employing strategies and tools to efficiently manage customer interactions waiting in a queue, aiming to improve service and reduce wait times.

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Real-time Analytics:

Using data analysis tools to monitor contact center operations live, enabling immediate decision-making and insights.

Recorded Line Message (RLM):

Pre-recorded messages used during calls to enhance efficiency and ensure consistent communication.

Remote Agent:

An agent working from outside the traditional office setting, supported by cloud technology for flexibility and broader talent access.

Robotic Process Automation (RPA):

Automating routine, rule-based tasks with software robots to allow agents to focus on more complex interactions.

Role-Based Access Control (RBAC):

Restricting access based on an individual's role within the organization, simplifying and enhancing security.


Directing customer interactions to the most suitable agent or department based on criteria like skill level or inquiry type.

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Service Level Agreement (SLA):

A formal agreement defining the expected service standards between a service provider and the customer, including metrics for response and resolution times.

Skill-Based Routing:

The practice of directing customer calls to the most appropriate agent based on specific skills or knowledge, enhancing efficiency and satisfaction.

SLA Compliance:

The degree to which service delivery adheres to the terms outlined in the Service Level Agreement, essential for maintaining customer trust.

Social Media Customer Service:

Support and engagement with customers through social media platforms, integral to omnichannel service strategies.

Speech Analytics:

The process of analyzing recorded speech to extract meaningful information. This technique focuses on the content of conversations, such as words, phrases, and linguistic patterns, to understand customer needs, behaviors, and sentiments. 

Speech Recognition:

Technology that translates spoken language into text, facilitating voice control and analysis of customer calls.

Speech-to-Text Keyword Expression Syntax:

Encoding method for speech-to-text conversion, using algorithms to accurately transcribe spoken words and phrases.

Supervisor Dashboard:

A real-time tool providing insights into operational metrics and agent performance, enabling effective management decisions.

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Descriptive labels used to organize and categorize call recordings for analysis and retrieval.


In a multi-tenant architecture, a configuration that allows multiple customers or tenants to use the same application or service efficiently.

Ticketing System:

A software tool that manages and tracks customer service requests and issues from start to resolution.

Topic Trends:

Analysis tool that aggregates and tracks call scores or volumes based on predefined topics, offering insights into customer interactions over time.

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UCaaS (Unified Communications as a Service):

Cloud-based delivery of integrated communication and collaboration tools, offering scalability and cost efficiency for businesses.

Unified Communications (UC):

The integration of various communication technologies within an organization into a cohesive system, enhancing collaboration and productivity.


An individual with access to contact center software, defined by specific roles and responsibilities, and often grouped for organizational purposes.

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Virtual Agent:

AI-driven chatbots or voice assistants designed to handle customer inquiries autonomously, supplementing human customer service efforts.

Voice Analytics:

The analysis of voice recordings or live calls with speech recognition software, aimed at extracting useful information and ensuring quality service. Voice Analytics goes beyond the spoken words to analyze characteristics of the speaker's voice, such as tone, pitch, emotion, and stress levels.

Voice of the Customer (VoC):

Feedback and insights from customers about their experiences and expectations, vital for improving service and customer satisfaction.

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The technique of embedding a marker in audio files to verify their authenticity and integrity, crucial for compliance and legal purposes.

Wrap-Up Time:

The period post-interaction where agents complete necessary administrative tasks before commencing another customer engagement.

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