Learn the essential concepts behind modern customer support. From AI and automation to KPIs and team workflows, everything you need to know in one place.
Agent utilization is a workforce management metric that measures the percentage of an agent's available working time that is spent actively handling customer interactions, as opposed to idle time, training, meetings, or administrative tasks.
An AI chatbot is a software application that uses artificial intelligence — particularly natural language processing and large language models — to simulate human-like conversation with users, answer questions, and perform tasks through text-based or voice-based interfaces.
AI customer support is the use of artificial intelligence technologies — including natural language processing, machine learning, and large language models — to automatically handle customer inquiries, resolve issues, and provide assistance without requiring a human agent.
AI hallucination is a phenomenon where a large language model generates information that appears plausible and is presented with confidence but is factually incorrect, fabricated, or not grounded in the source material provided, posing significant risks in customer support contexts where accuracy is critical.
Auto-resolution rate is the percentage of customer support inquiries that are fully resolved by automated systems — primarily AI chatbots — without any human agent involvement, measuring the effectiveness of AI automation in handling customer issues end-to-end.
Average Handle Time (AHT) is a customer support metric that measures the average total duration of a customer interaction, including the time spent actively communicating with the customer, any hold time, and post-interaction work such as note-taking and ticket documentation.
Bot containment rate is the percentage of customer interactions that are fully resolved by an AI chatbot or virtual assistant without requiring escalation to a human agent, measuring the bot's ability to independently handle customer needs.
Canned responses are pre-written, reusable reply templates that customer support agents can quickly insert into conversations to answer common questions consistently and efficiently.
Chatbot vs live chat refers to the comparison between automated AI-powered chat systems that handle customer conversations without human intervention and live chat staffed by human agents in real time, with most modern support strategies combining both in a hybrid approach.
Customer churn rate is the percentage of customers who stop using a product or cancel their subscription within a given time period, serving as a critical indicator of customer retention, product-market fit, and the overall health of a subscription-based business.
Contact ratio is the average number of support contacts per customer within a given time period, calculated by dividing total support interactions by total active customers, used to measure support demand relative to customer base size and product usability.
Conversation intelligence is the use of AI and natural language processing to analyze customer support conversations at scale, extracting insights about customer sentiment, common issues, agent performance, and emerging trends.
Conversational AI refers to artificial intelligence technologies that enable machines to understand, process, and generate human language in natural dialogue, combining natural language processing, machine learning, and large language models to power chatbots, virtual assistants, and automated support systems.
CSAT (Customer Satisfaction) score is a metric that measures how satisfied customers are with a specific interaction, product, or service, typically collected through a post-interaction survey asking customers to rate their experience on a scale of 1-5 or 1-10.
Customer advocacy is both a business strategy and a customer behavior where the company champions the customer's interests internally, and in return, satisfied customers actively promote and recommend the company to others.
Customer Effort Score (CES) is a customer experience metric that measures how much effort a customer had to exert to resolve their issue, complete a transaction, or get their question answered, typically measured on a 1-7 scale from "very low effort" to "very high effort."
A customer health score is a composite metric that combines multiple data signals — such as product usage, support interactions, satisfaction scores, and engagement patterns — into a single score that predicts the likelihood of a customer renewing, expanding, or churning.
Customer journey mapping is the process of creating a visual representation of every interaction and touchpoint a customer has with a company, from initial awareness through purchase, onboarding, ongoing usage, and renewal or expansion.
Customer Lifetime Value (CLV or LTV) is the total revenue a business can expect to earn from a single customer account over the entire duration of their relationship, factoring in average revenue per customer, gross margin, and expected customer lifespan.
Customer onboarding is the structured process of guiding new customers from initial signup through product setup, first value realization, and ongoing adoption, designed to ensure customers successfully integrate the product into their workflow and achieve their desired outcomes.
Customer retention is the ability of a company to keep its existing customers over a given period, measured as the percentage of customers who continue using the product or service rather than churning.
Customer segmentation is the practice of dividing a customer base into distinct groups based on shared characteristics such as behavior, demographics, plan tier, industry, or support needs to deliver more targeted and effective service.
Customer self-service is a support strategy that empowers customers to find answers and resolve issues independently through resources like knowledge bases, AI chatbots, help centers, community forums, and in-app guidance, without needing to contact a human agent.
Customer support KPIs (Key Performance Indicators) are quantifiable metrics that measure the effectiveness, efficiency, and quality of a company's customer support operations, including first response time, resolution time, CSAT score, ticket volume, and agent productivity.
A customer touchpoint is any interaction or point of contact between a customer and a company throughout the customer lifecycle, including marketing, sales, onboarding, support, billing, and product usage interactions.
An embedding vector is a dense numerical representation of text (or other data) in a high-dimensional space, where semantically similar content is positioned closer together, enabling AI systems to understand meaning, find related information, and power semantic search in applications like customer support.
Escalation rate is the percentage of customer support interactions that are transferred from an initial support tier (such as an AI chatbot or Level 1 agent) to a higher tier (such as a senior agent, specialist, or manager) because the initial tier could not resolve the issue.
First Contact Resolution (FCR) is the percentage of customer support inquiries that are fully resolved during the initial interaction without requiring any follow-up contacts, transfers, or escalations, serving as a key indicator of support efficiency and customer satisfaction.
First response time (FRT) is the amount of time between when a customer submits a support request and when they receive the first meaningful reply from a support agent or AI system, excluding automated acknowledgment messages.
A help center is a customer-facing website or portal that organizes knowledge base articles, FAQs, tutorials, and product documentation into a searchable, browsable interface where customers can find answers to their questions independently.
A help desk is a centralized system or team responsible for receiving, tracking, and resolving customer support requests, serving as the primary point of contact between a company and its customers for issue resolution.
Human handoff is the process of transferring a customer conversation from an AI chatbot or automated system to a live human agent when the AI cannot adequately resolve the inquiry, the issue requires human judgment, or the customer explicitly requests to speak with a person.
A knowledge base is a centralized, searchable repository of information — including articles, FAQs, guides, and documentation — that enables customers to find answers to their questions independently and powers AI systems to generate accurate responses.
Knowledge management is the systematic process of creating, organizing, maintaining, and distributing information within an organization to ensure that the right knowledge is available to the right people — including customers, support agents, and AI systems — at the right time.
Knowledge-Centered Service (KCS) is a methodology that integrates knowledge creation and maintenance into the support workflow, where agents capture, structure, and reuse knowledge as a natural byproduct of solving customer issues.
Live chat is a real-time messaging channel embedded in a website or application that allows customers to communicate directly with support agents or AI assistants for immediate assistance.
Mean Time to Resolution (MTTR) is the average amount of time it takes to fully resolve a customer support issue, measured from when the customer first submits the request to when the issue is confirmed as resolved, including all wait times, agent interactions, and escalations.
Net Promoter Score (NPS) is a customer loyalty metric that measures how likely customers are to recommend a company, product, or service to others, calculated by subtracting the percentage of detractors (scores 0-6) from the percentage of promoters (scores 9-10) on a 0-10 scale.
Omnichannel support is a customer service approach that provides a seamless, unified experience across all communication channels — including email, live chat, social media, phone, and in-app messaging — so customers can switch between channels without losing context or repeating information.
Proactive support is a customer service strategy that anticipates customer needs and addresses potential issues before customers encounter them or reach out for help, using data, behavioral triggers, and predictive analysis to deliver assistance at the right moment.
RAG (Retrieval-Augmented Generation) is an AI architecture that combines information retrieval from a knowledge source with text generation from a large language model, enabling the AI to produce accurate, contextually grounded responses based on specific, up-to-date information rather than relying solely on its training data.
A response template is a pre-crafted message framework that support agents use as a starting point for replies, containing standard language, placeholders for personalization, and structured information to ensure consistent, high-quality responses.
A response time SLA is a specific component of a service level agreement that defines the maximum acceptable time between when a customer submits a support request and when they receive a first meaningful response, typically tiered by issue priority level.
Semantic search is an information retrieval technique that understands the meaning and context of a search query rather than just matching keywords, using embedding vectors and natural language processing to find the most conceptually relevant results even when the exact words differ between the query and the content.
Sentiment analysis is a natural language processing technique that automatically identifies and categorizes the emotional tone expressed in text — such as positive, negative, or neutral — enabling support teams to understand customer mood, prioritize urgent issues, and track satisfaction trends at scale.
A Service Level Agreement (SLA) is a formal commitment between a service provider and a customer that defines the expected level of service, including specific metrics like response times, resolution times, and uptime guarantees, along with consequences if those commitments are not met.
A Service Level Objective (SLO) is an internal performance target that defines the desired level of service quality, such as response time, uptime, or resolution time, used to guide operational decisions and measure team performance.
A shared inbox is a collaborative email and messaging interface where multiple support agents can view, assign, and respond to customer conversations from a single unified queue, ensuring no message is missed or answered twice.
Support automation is the use of technology — including AI, workflows, rules, and integrations — to handle repetitive customer support tasks automatically, such as ticket routing, response generation, status updates, and common inquiry resolution, without requiring manual agent intervention.
A support macro is an automated action or sequence of actions that agents can trigger with a single click to perform repetitive tasks such as categorizing tickets, sending templated responses, updating fields, or executing multi-step workflows.
Support Operations (Support Ops) is the discipline of designing, implementing, and optimizing the systems, processes, workflows, and tools that enable a customer support team to operate efficiently and effectively at scale.
A support queue is an organized list of pending customer support requests waiting to be addressed by agents, typically ordered by priority, arrival time, or other business rules.
Support ticket volume is the total number of customer support requests — including emails, chat messages, phone calls, and form submissions — received by a support team within a specific time period, used to measure demand and plan staffing.
A support tier is a level within a structured support organization that defines the complexity of issues handled, the expertise required, and the escalation path from basic troubleshooting to specialized technical resolution.
Support triage is the process of evaluating, categorizing, and prioritizing incoming customer support requests based on factors like urgency, impact, complexity, and customer tier, ensuring that the most critical issues receive attention first and each request is routed to the appropriate team or agent.
Ticket backlog refers to the accumulated number of unresolved customer support tickets at any given time, representing the outstanding workload that the support team must address, and serving as a key indicator of team capacity, staffing adequacy, and operational health.
Ticket deflection is the practice of resolving customer inquiries through self-service channels — such as AI chatbots, knowledge bases, or help centers — before they become support tickets that require human agent involvement.
Ticket routing is the process of automatically or manually directing incoming customer support requests to the most appropriate agent, team, or department based on predefined rules, ticket attributes, or AI-driven analysis.
Ticket tagging is the practice of applying descriptive labels or categories to support tickets to classify issues by topic, product area, severity, or type, enabling better organization, routing, reporting, and trend analysis.
Voice of Customer (VoC) is the systematic process of capturing, analyzing, and acting on customer feedback, preferences, expectations, and pain points across all interaction channels to drive product, service, and experience improvements.
Get started today and see how Corebee brings AI-powered support to life. $99/mo flat — 30-day money-back guarantee.
Start Free Trial