Contact centre glossary

Adherence in contact centres – Adherence in a contact centre refers to how closely agents follow their scheduled activities and operational routines. It measures whether agents are available when planned, whether they move between tasks on time, and whether their working patterns match forecasted demand. Strong adherence keeps queues stable, protects service levels, and prevents burnout by keeping workload evenly distributed.

After Call Work (ACW) – After-call work refers to the brief period after an interaction when agents finish the behind-the-scenes tasks that keep the workflow moving. It may involve noting key details, updating tools, or triggering follow-up actions. Even though this stage happens after the customer disconnects, it directly affects service accuracy, queue flow, and the overall quality of the contact centre’s operations.

Agent assist – In modern contact centres, agent assist transforms everyday conversations. By blending AI intelligence with human empathy, it helps agents respond faster, personalise interactions, and create more satisfying customer experiences.

Agent Desktop – An agent desktop is the unified software application that agents use to manage customer interactions across multiple communication channels.

Agentic AI – Agentic AI refers to artificial intelligence that can take action, make decisions, and work towards a goal with a level of independence. It goes beyond simple automation by understanding context, choosing a next step, and acting across connected systems. In simple terms, agentic AI behaves more like a digital agent than a passive tool. It can identify what a customer needs, check information, trigger workflows, update records, or escalate an issue when human support is required. Instead of waiting for every instruction, it helps move the customer journey forward. For contact centres, agentic AI is especially useful because many interactions involve repeated decisions and actions. A customer may need to change details, book an appointment, check a case, or request an update. Agentic AI can help manage those steps while keeping the agent informed and in control. The real value of agentic AI is not just automation. It is intelligent action. Used well, it reduces manual effort, improves response times, supports agents, and helps customers reach the right outcome with less friction.

Agentic automation – Agentic automation transforms how organisations manage digital work by enabling systems to take initiative, respond to change, and coordinate tasks with minimal supervision. It supports complex business processes, adapts in real time, and helps teams work faster, smarter, and with greater accuracy.

AHT (Average Handle Time) – Average Handle Time (AHT) is a contact centre metric that measures the total duration of a customer interaction. It includes average talk time, average hold times, and wrap-up time, together forming the complete customer interaction.

AI call centre – An AI call centre uses artificial intelligence to automate, assist, and improve the handling of customer conversations across digital and voice channels.

AI customer assist – AI customer assist refers to the use of Artificial intelligence to support organisations in delivering structured customer service, customer support, and customer care across channels. It helps support teams reduce Response Time, manage customer queries more accurately, and create consistent customer experiences. As customers expect instant help across social media, messaging, and voice, AI customer assist provides a balance between automation and human agents to manage increasing workloads.

AI Customer Interaction Automation – AI customer interaction automation is the use of artificial intelligence to manage parts of the customer conversation across channels such as voice, chat, email, messaging, and social media. It helps organisations respond faster, route enquiries more accurately, and handle routine requests more consistently. In simple terms, it allows AI to support or complete everyday customer service tasks. This could include answering common questions, checking order status, updating customer records, sending confirmation messages, or passing a complex issue to a human agent with the right context. For contact centres, AI customer interaction automation is useful because customer demand often arrives across multiple channels at the same time. Customers expect quick answers, while agents need time to focus on more complex or sensitive issues. Automation helps reduce avoidable contact and keeps service moving. The real value is not replacing every human interaction. It is knowing which parts of the journey can be handled automatically and which need human support. When done well, it improves speed, consistency, and customer experience without making customers feel blocked by automation.

AI for Customer Service Workflow Automation – AI for customer service workflow automation refers to the use of artificial intelligence to manage the steps behind a customer request. It helps move work through the right process by identifying what needs to happen, triggering actions, and reducing manual admin. In simple terms, it connects the conversation to the process. When a customer contacts an organisation, there may be forms to complete, records to update, cases to create, teams to notify, or follow-up messages to send. AI workflow automation helps manage these steps more quickly and consistently. For contact centres, this is especially valuable because many delays happen after the customer explains the issue. Agents may need to switch between systems, copy information, check policies, and remember the next step. AI can reduce this pressure by guiding or completing parts of the workflow. The real value of AI workflow automation is smoother service. Customers get fewer delays and clearer updates, while agents spend less time chasing process and more time solving problems.

AI Routing – AI routing refers to the process of using artificial intelligence and machine learning to decide the best path for requests, data, or customer interactions. It analyses multiple factors, predicts outcomes, and automatically selects the most efficient route or agent. This intelligent automation improves speed, reduces delays, and enhances customer satisfaction across different channels.

AI Self-Service – AI Self-Service refers to digital support systems powered by Artificial Intelligence that allow customers to resolve issues, ask questions, or complete tasks without depending on human agents. These systems use technologies such as natural language processing, machine learning, conversational AI, predictive analytics, and generative AI to create self-service experiences that feel intuitive, responsive, and personalised. Across industries, AI-driven customer self-service reduces pressure on support teams, improves response time, and supports smart call deflection without harming customer satisfaction.

Asynchronous messaging – Asynchronous messaging allows conversations and tasks to continue even when both parties are not present at the same time. It creates a flexible communication method that supports modern customer service needs by reducing wait times, improving customer experience, and enabling smooth interactions across digital channels without forcing users or agents to stay online continuously.

Auto QA – Auto QA brings automation into quality assurance by evaluating customer interactions at scale. It reviews conversations, checks compliance, and highlights behavioural patterns so organisations can maintain consistent service quality across every channel without depending solely on manual analysis.

Auto summary in contact centres – Auto summarisation refers to AI turning long customer conversations into short, accurate notes. Instead of agents typing up call wrap-ups or scrolling through long transcripts, the system captures the key points, issues raised, and actions agreed. It reduces the admin workload that often slows agents down and increases the pressure between calls.

Auto summary in contact centres – Auto summarisation refers to AI turning long customer conversations into short, accurate notes. Instead of agents typing up call wrap-ups or scrolling through long transcripts, the system captures the key points, issues raised, and actions agreed. It reduces the admin workload that often slows agents down and increases the pressure between calls.

Automatic Call Distribution (ACD) – Automatic Call Distribution, or ACD, is a telephony function that receives incoming calls and routes them to the most suitable person or team. Instead of relying on front-desk staff to manually pass calls around, it uses rules and data to support intelligent call routing. This reduces constant misdirects, improves customer satisfaction, and helps organisations use their people more effectively.

Average Handle Time (AHT) – Average Handle Time is one of the most widely used metrics in contact centres to understand how long agents take to resolve customer interactions. It helps organisations balance efficiency, quality, and customer satisfaction by tracking the total time spent on each conversation from start to finish.

Average Speed of Answer (ASA) – Average Speed of Answer measures how quickly a customer reaches an agent after entering a call queue. It helps organisations understand wait times, staffing efficiency, and the overall responsiveness of their contact centre operations.

Backlog – A backlog is a list of work that is accepted but not yet completed. It sits between planning and execution. Teams use it to keep track of pending items without mixing them into current tasks. A backlog can exist in an office, a service operation, or a project workflow. It helps create visibility and order, especially when demand arrives faster than a team can finish it. Backlog is not automatically a problem. In many workplaces, it is a normal way to queue tasks, review priorities, and decide what gets handled next.

Behavioural Routing – Behavioural Routing is an adaptive routing approach used in modern communication and network systems. It focuses on selecting routes based on live conditions and contextual signals, rather than relying on fixed paths. This makes it suitable for environments where traffic patterns and interaction volumes change frequently.

Blended Agent – A blended agent is a contact centre professional trained to handle both inbound calls and outbound calls across multiple communication channels.

BOT or IVA – BOT or IVA refers to software systems designed to interact with people using text or voice. These systems are used to answer questions, guide users through processes, and complete routine tasks without direct human involvement. Bots and intelligent virtual assistants are now a common part of digital services, especially where speed, availability, and consistency are important. Although the terms are sometimes used interchangeably, bots and IVAs differ in capability and complexity, which affects how and where they are used.

Capacity Planning – Capacity planning is about making sure your team has the right number of people, tools, and systems to handle the work coming their way.

Case – A case in a call centre is a structured record created to track a customer issue from the moment it is reported until it is fully resolved. It represents the problem itself, not just a single conversation. A case can include multiple interactions across voice calls, emails, chats, or digital messages, all tied to the same issue. In operational terms, a case provides continuity. Even if the customer speaks to different agents over time, the case ensures that the full context is available, reducing confusion and repeated explanations.

CCaaS – CCaaS, or Contact Centre as a Service, is a cloud-based platform that provides all the tools a modern contact centre needs via a subscription model.

Computer Telephony Integration (CTI) – Computer Telephony Integration (CTI) connects telephone systems with computer applications, allowing contact centres to handle calls more intelligently and efficiently. Instead of agents working with separate phone systems and desktop applications, CTI brings them together so information flows automatically between the two.

Concurrency – Concurrency is the ability of a system to deal with more than one task in the same time window. It does not always mean tasks run at the exact same instant. Instead, a concurrent system manages multiple activities so each one makes progress without blocking the others for long. You see concurrency in everyday computing. Your phone can download an update while you message someone and listen to music. The system keeps switching attention across tasks so everything stays responsive. Concurrency becomes even more important when thousands of users interact with the same platform at once, such as in an AI-enabled contact centre.

Containment – Containment is a core concept in customer service and contact centre operations. It refers to how effectively customer queries are resolved without being passed to a live agent. When containment is implemented well, customers receive faster responses, agents focus on complex issues, and service teams operate more efficiently. Containment does not remove human support. Instead, it ensures that simple and repetitive requests are handled at the earliest stage, while human agents are reserved for cases that require judgment, empathy, or deeper investigation.

CSAT – CSAT stands for Customer Satisfaction Score. It is a customer experience metric used to understand how satisfied someone feels after a specific interaction. It is usually collected right after an event, such as a support call, a chat, a delivery, or a service request. Because CSAT captures feedback quickly, it helps teams spot problems early and improve service without waiting for long-term trends. In simple terms, CSAT tells you how customers feel about one moment. That is what makes it valuable for day-to-day service quality.

Customer Care – Customer care refers to the support and services provided to customers before, during, and after a purchase.

Customer Communications – Customer communication refers to the exchange of information between a business and its customers. This includes conversations through email, phone calls, live chat, social media, and messaging apps.

Customer engagement – Customer engagement refers to the ongoing interaction between a business and its customers across every channel, at every stage of the customer journey.

Customer Experience Metrics – Customer experience metrics are measurable indicators that evaluate how people perceive and interact with a business.

Customer Intent Recognition – Customer intent recognition is the process of understanding what a customer is trying to achieve when they contact an organisation. It uses AI and language analysis to identify the meaning behind a customer’s words, whether they are asking a question, reporting an issue, making a change, or requesting support. In simple terms, intent recognition helps organisations understand why the customer has made contact. A customer might say “my bill looks wrong”, “I’ve been charged twice”, or “why has this payment gone out again?” The wording is different, but the intent may be the same: they need help with a billing issue. For contact centres, customer intent recognition is especially useful because customers do not always use the same language as the business. They may be frustrated, vague, or unsure which team they need. Recognising intent helps route them correctly, support self-service, and give agents better context before the conversation begins. The real value of intent recognition is reducing customer effort. It helps customers reach the right support faster, improves first contact resolution, and gives organisations better insight into why people are getting in touch.

CX Analytics – CX analytics, short for customer experience analytics, is the process of collecting, analysing, and interpreting customer data to understand how people interact with a business across touchpoints.

CX Platform – A CX platform is a centralised system used by businesses to manage and improve the entire customer experience. It integrates various tools for communication, support, automation, and analytics into a single platform.

Deflection in contact centres – Deflection refers to moving customer demand from high-cost channels (like phone calls) to lower-cost alternatives or self-service. The goal is reducing contact volume and operational costs whilst maintaining or improving customer experience. When done well, deflection benefits both the organisation and the customer. When done poorly, it creates frustration and damages trust.

Digital Customer Experience – Digital customer experience (DCX) refers to how customers interact with a business through digital channels and how those interactions shape their overall perception of the brand.

Digital Customer Service – Digital customer service is how organisations handle customer inquiries and support needs through online channels instead of traditional methods like phone calls or in-person visits.

Disposition – Disposition is the label applied to an interaction recording what happened and how it ended. When an agent finishes a call, chat, or email, they select a disposition code describing the outcome - whether the issue was resolved, what type of query it was, and what needs to happen next. This simple labelling system drives reporting, forecasting, routing decisions, and follow-up actions across the contact centre. When done well, disposition provides accurate insight into customer needs and operational performance. When done poorly, it creates meaningless data and wastes agent time.

Erlang – Erlang is a set of mathematical formulas used to calculate how many agents you need to handle predicted contact volume whilst meeting service level targets. Named after Danish mathematician Agner Krarup Erlang who developed them in 1909 for telephone networks, these formulas remain the foundation of contact centre workforce planning today. The maths sounds intimidating, but the concept is straightforward: if you know how many contacts are coming, how long they'll take, and what service level you're aiming for, Erlang tells you how many agents to schedule.

Escalation – Escalation in a contact centre refers to the process of moving a customer issue to a more experienced or authorised agent when the first point of contact cannot resolve the query.

FCR (First Contact Resolution) – First Contact Resolution (FCR) measures whether a customer's issue gets resolved on the first interaction without them needing to contact you again. It's one of the most important metrics in contact centre performance because it directly impacts both customer satisfaction and operational costs. When FCR is high, customers are happy because their problems get sorted quickly. Costs are lower because you're not handling the same issue multiple times. When FCR is low, customers are frustrated, agents are demoralised, and your contact volume is artificially inflated by people calling back about things that should have been fixed already. Simple concept. Surprisingly difficult to measure accurately and even harder to improve consistently.

Feedback management – Feedback management is the structured process of capturing, analysing, and acting on customer, user, or employee feedback.

Feedback management – Feedback management is the organised practice of collecting, analysing, and acting on insights from employees, customers, and stakeholders. It converts raw opinions into practical improvements for services, processes, and products. A good approach builds a clear loop between what people say, what teams learn, and what changes next.

Forecast Accuracy – Forecast accuracy measures how close your predictions were to what actually happened. You forecast that 500 calls would arrive on Tuesday morning with an average handle time of 4 minutes. Tuesday arrives and you get 650 calls averaging 5 minutes. Your forecast was wrong, your staffing was short, and your service level collapsed. This happens constantly in contact centres. The gap between what you predicted and what actually occurred determines whether you've staffed appropriately or whether you're either drowning in unexpected volume or paying agents to sit idle.

Guest experience – Guest experience refers to the overall impression a person forms when interacting with a service or environment, especially within hospitality, retail, or public-facing organisations.

Hand-off – Hand-off is the moment when a chatbot or virtual assistant transfers a customer to a human agent, ideally with full conversation context and transcript. When done well, the customer moves seamlessly from AI to human without repeating themselves. When done badly, it's the digital equivalent of being transferred three times and starting from scratch each time. This single moment often determines whether customers see AI as helpful or infuriating. The chatbot might have handled the first part brilliantly, but if the hand-off breaks and the agent has no idea what's already been discussed, the entire experience collapses.

Hosted services – Hosted services are contact centre solutions where everything runs on the internet. There’s no need for physical servers in your office.

ID&V (Identification and Verification) – Identification and verification (ID&V) is the process of confirming who the customer is and that they have the right to access account information or make changes. It's the "can you confirm your date of birth and postcode" ritual that starts almost every contact centre interaction. ID&V sits at the uncomfortable intersection of security and customer experience. Too little verification and you're handing account access to fraudsters. Too much and you're putting legitimate customers through interrogation just to check their balance. Every contact centre struggles with this balance. Make it too easy and you'll be explaining to regulators why you gave account details to someone who shouldn't have them. Make it too hard and customers will be fuming before the actual conversation even starts.

Inbound call – An inbound call is a phone call made by a customer, client, or other external party to a business.

Intelligent email sorting – Intelligent email sorting is the use of artificial intelligence and machine learning to organise, prioritise, and route emails automatically. Instead of relying only on manual sorting or static folders, intelligent systems analyse email content, sender importance, behaviour patterns, and context to decide where each message should go. This approach helps people and teams organise their inbox more efficiently, reduce email overload, and build sustainable email management habits. For many organisations, intelligent email sorting is the backbone of modern email management services and supports the goal of reaching inbox zero in a realistic way.

Intent – Intent is what the customer wants to achieve when they contact you. Not what they say, but what they mean. "My bill is wrong" might be the words, but the intent is "dispute charge." "I need to speak to someone" might be the request, but the intent is "get refund" or "cancel service" or "technical support." Understanding intent matters because it determines where the contact goes and how it gets handled. Get it right and customers reach the right place quickly. Get it wrong and they're bounced around explaining themselves repeatedly whilst frustration builds. Every contact centre tries to identify intent, whether they call it that or not. IVR menus asking customers to "press 1 for billing, 2 for support" are intent detection in its most basic form. Modern AI does the same thing but by understanding natural language instead of forcing customers through numbered menus.

Interaction analytics – Interaction analytics is the process of examining customer interactions across multiple communication channels to uncover trends, patterns, and opportunities for service improvement. It combines speech, text, and digital data to help organisations understand customer sentiment, detect root causes of issues, and improve overall customer experience. By leveraging artificial intelligence and conversational intelligence, organisations can analyse large volumes of customer conversations to make data-driven decisions and improve operational efficiency.

Interaction Analytics – Interaction analytics is the process of analysing customer conversations across channels to uncover patterns, trends, and insights that improve service. Instead of relying on gut feel or small samples, you examine what customers are saying, how agents are responding, and what outcomes those interactions produce. Every call, chat, email, and message contains information. Interaction analytics extracts that information systematically, turning thousands of conversations into actionable intelligence about what's working, what's broken, and where to focus improvement efforts. This matters because sampling a handful of interactions gives you a partial view at best. Interaction analytics gives you the full picture: what customers complain about most, which agents handle escalations well, where processes create unnecessary effort, and what language drives better outcomes.

Journey mapping – Journey mapping is a visual tool that shows the steps a customer takes when dealing with your business.

KCS (Knowledge-Centered Service) – Knowledge-Centered Service (KCS) is a methodology for building and maintaining knowledge through use rather than through separate documentation projects. Instead of writers creating articles in isolation, the people solving customer problems create and improve knowledge as part of their daily work. The core idea: when an agent solves something, they capture it immediately. When they use an article that's unclear or outdated, they fix it on the spot. Knowledge stays current because the people using it are the people maintaining it. This is fundamentally different from traditional knowledge management where documentation teams write articles, publish them, and update them on schedules whilst agents work separately using (or ignoring) whatever content exists. KCS collapses that separation. The work of solving problems and the work of documenting solutions become the same thing. For contact centres, this matters because traditional knowledge bases rot quickly. Articles get published, become outdated, contradict each other, and fill up with content nobody uses whilst the things people need aren't documented. KCS prevents this by making knowledge creation inseparable from customer service.

Knowledge Base – A knowledge base is a centralized repository of information that helps people find answers without asking someone else. For customers, it's the FAQs, help articles, and guides they search before contacting you. For agents, it's the internal documentation they reference whilst handling contacts. The promise is simple: capture information once, reuse it endlessly. Write an article about password resets. Customers use it to solve the problem themselves. Agents reference it during calls. Chatbots pull from it to answer questions automatically. One piece of content serves everyone. The reality is messier. Most knowledge bases start with good intentions and deteriorate into sprawling collections of outdated, contradictory, or impossible-to-find content that nobody trusts or uses. The technology is straightforward. Making it work is the challenge.

Knowledge management – Knowledge management refers to the process of systematically capturing, organising, distributing, and using information within an organisation.

Knowledge surfacing – Knowledge surfacing is the process of identifying, extracting, and displaying the right information at the right time across digital systems. It ensures that useful knowledge stored in databases, documents, or conversations becomes immediately available to employees and customers who need it. By connecting information sources with intelligent systems, organisations can improve decision-making, response accuracy, and service quality.

LLM (Large Language Model) – A Large Language Model (LLM) is an AI system trained on vast amounts of text to understand and generate human-like language. Instead of following rigid rules or scripts, LLMs respond to natural language input by predicting what text should come next based on patterns learned from their training data. For contact centres, this means technology that can understand customer questions phrased a thousand different ways, generate helpful responses, summarise conversations, draft replies, and assist agents in real-time. The difference from older chatbots is flexibility - LLMs handle variation and nuance rather than breaking when someone asks something slightly differently than expected. ChatGPT, Claude, and similar systems are LLMs. The technology underpinning many modern customer service automation tools, virtual assistants, and agent copilot products relies on LLMs working behind the scenes.

Logs – Logs are time-stamped digital records generated by systems, applications, or communication tools.

Menu – A menu in contact handling is a digital feature that allows callers to choose how they wish to proceed when they reach out to a business or service centre.

Model Drift – Model drift is what happens when an AI or machine learning model becomes less accurate over time because the world it's working in has changed from the world it was trained on. The model still works the same way technically, but the data it's seeing now looks different from the data it learned from, so its predictions get worse. For contact centres, this shows up when systems that worked brilliantly six months ago start making mistakes. Intent detection misclassifies queries. Routing sends customers to wrong teams. Chatbots struggle with questions they used to handle perfectly. Sentiment analysis misses frustrated customers. The technology hasn't broken. The data has changed. Customer language evolves, products launch, policies update, competitors shift behaviour, and suddenly your AI model is operating in a different environment than it was trained for. Without retraining or adjustment, performance degrades gradually until someone notices things aren't working like they used to.

Natural language processing – Natural language processing refers to the ability of a system to analyse and respond to spoken or written language in a way that is meaningful.

NBA (Next Best Action) – Next Best Action (NBA) is decision logic that recommends what an agent should do next during a customer interaction. Instead of following rigid scripts or making decisions from scratch every time, the system analyses the situation and suggests the optimal next step. This might be which question to ask, which product to offer, which solution to try first, or which retention offer to make. The recommendation comes from patterns in data - what worked in similar situations previously, what this specific customer responds to, and what typically drives the best outcomes. For contact centres, NBA sits between completely scripted interactions (where agents follow predetermined paths regardless of context) and completely freeform handling (where agents decide everything based on experience and judgment). It provides intelligent guidance whilst leaving room for human judgment when the situation demands it.

NPS (Net Promoter Score) – Net Promoter Score (NPS) is a customer loyalty metric based on one question: "How likely are you to recommend us to a friend or colleague?" Customers answer on a 0-10 scale. The score categorises responses into Promoters (9-10), Passives (7-8), and Detractors (0-6), then calculates NPS as the percentage of Promoters minus the percentage of Detractors. So if 50% of customers are Promoters, 30% are Passives, and 20% are Detractors, your NPS is +30. Scores range from -100 (everyone's a Detractor) to +100 (everyone's a Promoter). Anything above zero means more people would recommend you than wouldn't. Above +50 is considered excellent. Below zero suggests serious problems. For contact centres, NPS appears everywhere. Post-interaction surveys ask the magic question after calls, chats, or emails. Leadership dashboards track NPS trends. Performance reviews include NPS targets. The metric has become shorthand for customer satisfaction and service quality. Whether it deserves this prominence is debatable. Some organisations swear by NPS. Others think it's overrated theatre that measures the wrong things. The truth probably sits somewhere between worship and dismissal.

Omni-channel contact centre – An omni-channel contact centre is a cloud-based contact centre solution that allows people to engage with organisations through a variety of channels, such as voice, email, live chat, social media platforms, and mobile apps, while maintaining a seamless customer experience.

Omnichannel Routing – Omnichannel routing is the process of sending customer interactions from different channels to the most suitable agent, team or automated service. These channels can include phone, email, live chat, SMS, messaging apps, social media, web forms and other digital contact routes. In simple terms, omnichannel routing helps customers reach the right place, no matter how they choose to get in touch. Instead of treating calls, emails, chats and social messages as separate queues, it connects them so work can be prioritised and assigned more intelligently. For contact centres, omnichannel routing is useful because customers often move between channels during the same journey. Someone might start on chat, follow up by email, and then call. Omnichannel routing helps the business recognise that journey and avoid making the customer repeat themselves. The real value of omnichannel routing is not just moving contacts faster. It is routing smarter. When it works well, customers get more relevant support, agents receive work they are better equipped to handle, and managers gain better control across the whole operation.

Patient engagement – Patient engagement is the active collaboration between individuals and their healthcare providers to manage health decisions, behaviours, and outcomes.

Performance management in contact centres – Performance management in a contact centre is the structured way supervisors monitor, coach, and support agents so they can deliver consistent customer experiences. It aligns daily behaviour with operational goals and gives agents a clear sense of what “good” looks like. When done well, it becomes less about policing numbers and more about setting people up to succeed.

Post-call summarise – Post-call summarisation refers to the process of documenting the essential points of a customer interaction after a conversation ends. It helps agents capture key details such as the customer issue description, actions taken, escalation requirements, and resolution steps. Modern contact centres use artificial intelligence, natural language processing, and speech analytics to generate call summaries automatically, reducing manual note-taking and improving customer experience. Accurate post-call summaries also support customer service consistency and help teams maintain a clear activity timeline for future interactions.

Predictive analytics – Predictive analytics refers to the use of statistical modelling, machine learning, and artificial intelligence to forecast future outcomes based on historical data. Organisations rely on predictive modelling to understand customer behaviour, measure potential risks, and plan more accurately across different functions. The insights generated from these models help teams anticipate patterns such as seasonal demand changes, shifts in user activity, or early signs that a customer may become disengaged. Predictive analytics supports everything from forecasting cash flow to identifying changes in endpoint behaviour that may suggest performance or security issues. By moving beyond descriptive analytics it provides forward-looking guidance so teams can make decisions with more confidence.

Predictive Dialler – A predictive dialler is a type of automatic dialler that places outbound calls using algorithms to connect agents only when a real person answers.

Queue – A queue in customer service refers to the virtual line where incoming calls or other customer enquiries are placed until a centre agent becomes available.

Real-Time Adherence – Real-time adherence, often shortened to RTA, shows whether agents are following their planned schedule at that exact moment. It compares what an agent should be doing with what they are actually doing, such as being available for calls, taking a break, attending training or handling after-call work. In simple terms, RTA helps contact centres see whether the live operation matches the plan. A schedule may look right at the start of the day, but calls run over, breaks move, meetings overrun and demand changes. Real-time adherence shows those gaps while there is still time to respond. For contact centres, RTA is useful because small schedule changes can quickly affect service levels. If too many agents are away from the queue at the wrong time, waiting times rise, backlogs grow and more pressure falls on the agents who are available. The real value of real-time adherence is not catching people out. It is understanding what is happening now, managing workload fairly and making better decisions before customers and agents feel the impact.

Real-time knowledge surfacing – Real-time knowledge surfacing is the process of presenting the most relevant information, guidance, or insights to employees or customers during an interaction. Instead of relying on manual searching or switching between multiple systems, real-time surfacing uses artificial intelligence, knowledge management techniques, and machine learning to detect what the user needs and display helpful content instantly. It improves customer experience and supports agents by reducing response times, simplifying decision-making, and improving accuracy during customer interactions.

Response rate – Response rate is a measurement used to understand how many people answered a survey, completed a form, or responded to a communication out of the total number invited.

Robotic Process Automation – Robotic process automation, often shortened to RPA, is technology that uses software bots to complete repetitive, rule-based tasks. These bots can copy data, update systems, complete forms, move files, send messages, and trigger routine actions. In simple terms, RPA handles the admin work that follows clear rules. If a person has to repeat the same task across systems every day, there is a good chance RPA could support it. The bot does not need to understand emotion or make complex decisions. It follows a defined process quickly and consistently. For contact centres, RPA is useful because agents often spend time moving information between systems instead of focusing on customers. RPA can help update records, create cases, send confirmation emails, process simple requests, or complete after-call tasks. The real value of RPA is practical efficiency. It reduces manual effort, improves accuracy, and helps agents spend more time on customer conversations rather than repetitive admin. When combined with AI, it can also help turn customer understanding into action.

Sentiment Analysis – Sentiment analysis is the process of using AI to understand the emotional tone behind customer language. It looks at what customers say in calls, chats, emails, reviews, surveys and social media, then helps identify whether they sound satisfied, neutral, confused, frustrated, angry or at risk of escalation. In simple terms, sentiment analysis helps organisations understand how customers feel, not just what happened. A contact may be resolved quickly but still feel cold, stressful or unhelpful. Sentiment analysis helps uncover those emotional signals so teams can see where the experience is working and where it is causing frustration. For contact centres, sentiment analysis is useful because customer emotion appears in everyday conversations. Repeated phrases, rising frustration, short responses, complaints and changes in tone can all show where customers need more support. These insights can help with coaching, routing, live intervention and wider service improvement. The real value of sentiment analysis is not the score itself. It is how the insight is used. When combined with context, such as contact reason, outcome, customer history and quality review, sentiment analysis helps teams understand the emotional side of service and act before frustration becomes failure.

Shrinkage – Call centre shrinkage is the gap between the time agents are paid to work and the time they are actually available to handle customer interactions. It includes anything that takes agents away from live contacts, such as breaks, training, meetings, coaching, sickness, holidays, admin or system issues. In simple terms, shrinkage explains why a contact centre can look fully staffed on paper but still be short of people in reality. Agents may be scheduled for a shift, but not every minute of that shift is available for calls, chats, emails or messages. For contact centres, shrinkage is a key workforce planning metric. If it is not included in forecasts and schedules, staffing plans become too optimistic. Queues build, service levels drop and teams end up firefighting because fewer agents are available than expected. The real value of tracking shrinkage is not to remove every non-contact activity. Breaks, coaching and training are important for wellbeing and performance. The aim is to plan honestly, reduce avoidable lost time and make sure there is enough live capacity to meet customer demand.

Supervisor Copilot – Supervisor Copilot refers to an AI-driven tool designed to assist team leaders and managers in real time across contact centre operations. It provides instant insights, summaries, and recommendations to improve agent performance, customer satisfaction, and service standards. Built on Generative AI and data intelligence, it supports supervisors with quick decision-making and reduces manual review time.

Support ticket categorisation – Support ticket categorisation is the process of assigning structured labels to customer issues so that support teams can manage, route, and resolve requests efficiently. Ticket categorisation allows organisations to organise support requests, reduce resolution time, improve customer satisfaction, and maintain consistent service across departments. By grouping tickets under meaningful categories, support agents and help desk teams can prioritise work, identify patterns in customer experiences, and improve overall support workflow performance.

Text-to-speech – Text-to-speech, often shortened to TTS, is a type of speech synthesis technology that converts written text into audio output.

Upsell – Upselling is the practice of encouraging a customer to purchase a more advanced or premium version of a product or service than they originally intended.

Voice of the Customer (VOC) – Voice of the Customer (VOC) is the process of collecting, analysing, and acting on customer feedback. It brings together what customers say across surveys, calls, chats, emails, complaints, reviews, social media, and other interactions, then turns that feedback into insight the business can use. In simple terms, VOC helps organisations understand what customers are actually experiencing. It shows where customers are happy, where they are frustrated, and where the journey is creating avoidable problems. Instead of relying on assumptions, teams can use real customer feedback to see what needs improving. For contact centres, VOC is especially useful because so much customer insight already exists inside everyday conversations. A repeated complaint, a confusing chat transcript, a poor survey comment, or a pattern of repeat calls can all reveal where service is breaking down. VOC helps connect those signals and turn them into action. The real value of VOC is not just listening to customers. It is what happens next. A good VOC programme helps teams fix root causes, improve processes, support agents, reduce friction, and prove to customers that their feedback is being taken seriously.

Virtual agent – A virtual agent is a digital tool that interacts with users through voice or chat to provide information, solve problems, or complete tasks.

Workflow Management – Workflow management refers to the process of defining, organising, and optimising a sequence of tasks within a business to improve consistency, efficiency, and visibility.

Workforce augmentation – Workforce augmentation is a staffing approach that allows organisations to enhance their internal teams by bringing in external professionals with specialised skills. Instead of hiring full-time employees, businesses add temporary or project-based experts to support workload peaks, fill skill gaps, or meet specific technical requirements. This approach strengthens business objectives while helping teams scale quickly without restructuring their existing workforce.

Workforce Optimisation – Workforce optimisation (WFO) plays a vital role in improving operational efficiency, employee satisfaction, and service quality across the contact centre.

Workforce planning – Workforce planning is all about making sure you have the right people, with the right skills, in the right roles, both now and in the future.

XML integration – XML integration links contact centre systems like CRMs, IVRs, and dashboards to share structured data in real time.

Yield rate – Yield rate is a measure of how many customer interactions result in successful outcomes compared to the total number of contact attempts.

Zero-call resolution – In today’s service-driven climate, many customers prefer not to call at all. Zero-call resolution is a strategy that aims to resolve customer issues before they reach the contact centre.