Customer support glossary

Abandonment rate is the share of customers who leave a queue, chat, or callback line before ever reaching an agent. Contact centres track it alongside wait time because abandonment climbs sharply once queues pass a customer’s patience threshold, making it an early signal that staffing or deflection is not keeping pace with demand.

Average handle time is the mean time an agent spends on a single contact, including talk or chat time and after-contact work. Because the definition varies between teams, comparisons are only meaningful when both sides confirm whether wrap-up time is included inside the calculation being reported.

Backlog is the set of unresolved tickets carried past their target response window. A growing backlog signals that arrival rate has outpaced capacity, and because ageing tickets attract follow-up contacts, backlog tends to generate additional volume rather than simply waiting quietly to be cleared later.

Business rules are the conditions that decide how a contact is prioritised, routed, or escalated. Typical rules combine customer tier, channel, detected intent, and elapsed time to determine where a conversation goes and how quickly it must be answered under the applicable service level agreement.

A contact centre is the team and tooling that handle inbound customer contacts across every channel, including voice, chat, email, and social. The term replaced call centre as support expanded beyond telephony, and it covers workforce management, quality assurance, and routing as well as the agents themselves.

Data residency is the requirement that customer data be stored and processed inside a specific country or region. Regulated industries and public-sector buyers frequently mandate it, which makes deployment location a procurement question rather than a technical detail for any support platform handling personal data.

Deflection is steering a contact toward a self-service or automated resolution before it reaches an agent queue. Effective deflection resolves the underlying question; unsuccessful deflection merely delays the contact, so teams measure it by whether the customer returns rather than by how many contacts were diverted.

A disposition code is the label an agent or AI applies to a closed contact to describe what actually happened. Disposition data drives volume reporting and root-cause analysis, so inconsistent or overly broad codes quietly destroy a support team’s ability to explain why contacts arrive at all.

Escalation is moving a contact to a more senior agent, a specialist team, or a human when automation stalls. Escalation paths are defined by business rules, and the rate at which contacts escalate is a direct measure of whether the first line is equipped to resolve them.

First contact resolution is the share of issues fully resolved during a customer’s first interaction, with no follow-up contact about the same problem. It correlates strongly with satisfaction and cost, but only when measured over a defined reopen window rather than at ticket close.

Full resolution time is the total elapsed time from a customer’s first message to a confirmed resolution, including waiting, escalation, and overnight gaps. Unlike response time, it reflects the experience of waiting for an answer rather than the speed of the first reply alone.

A hybrid support model has AI handling routine, high-volume contacts while human agents own complex, sensitive, or high-value interactions. The design question is not what the technology can handle but where the cost of an error is high enough to justify keeping a person in the conversation.

A knowledge base is the maintained source of truth that AI agents and human agents both answer from. Its accuracy sets a ceiling on the quality of every automated answer, which makes knowledge maintenance an operational requirement rather than a documentation project owned by nobody in particular.

A knowledge gap is a recurring customer question the knowledge base cannot answer, usually visible as repeat escalations or low-confidence fallbacks on the same intent. Systematically closing gaps is the highest-return maintenance activity available to any team running automated support at scale.

Live chat is real-time text support delivered in-product or on-site, fronted by a human agent, an AI agent, or both in sequence. Its defining constraint is synchrony: the customer is waiting, so response latency and handoff quality matter more than in asynchronous channels.

A macro is a saved reply or action set that agents apply to a common case, inserting standard wording and performing routine updates in one step. Macros reduce handle time and variation, but stale macros are a frequent source of incorrect policy reaching customers at scale.

Multilingual support means serving customers in their own language, including handling Arabic and English side by side within a single operation. Beyond translation it requires language-aware routing, quality scoring in each language, and knowledge content that is maintained rather than machine-translated once and forgotten.

Occupancy rate is the portion of an agent’s logged-in time spent actively handling contacts rather than waiting for them. Sustained high occupancy predicts burnout and attrition, so workforce planners treat it as a health measure rather than a productivity target to be maximised.

Omnichannel describes one continuous conversation and history across chat, email, voice, and social, so a customer who switches channel does not have to restart. The distinction from multichannel is continuity: offering many channels is not omnichannel unless context follows the customer between them.

Peak volume is the highest-contact period a staffing plan has to absorb, whether a daily rush hour, a seasonal spike, or an incident-driven surge. Because peaks drive headcount decisions, automation is usually justified on peak absorption rather than on average daily contact volume.

PII redaction is the automatic removal of personally identifiable information from transcripts, logs, and training samples. It lets support conversations be analysed, stored, and used to improve models without carrying customer identifiers into systems that were never scoped to hold them.

Quality assurance is the structured review of conversations against a scorecard, applied to human agents and AI agents alike. Traditional QA samples a handful of conversations per agent each month; automated QA scores every conversation, which changes the exercise from spot-checking into actual measurement.

A queue is the ordered set of contacts waiting to be handled, usually segmented by channel, language, skill, or customer tier. Queue design determines who waits and for how long, making it one of the most consequential and least visible decisions in a support operation.

Resolution rate is the share of contacts closed with the customer’s issue actually solved, as distinct from contacts merely closed. The gap between the two is where reopened tickets and repeat contacts live, so the measure is only trustworthy when reopens are tracked against it.

Response time is how long a customer waits for a reply once they have made contact. Teams report it as a first-response figure and as an average across subsequent replies, because a fast opening reply followed by long silences produces a poor experience despite good headline numbers.

Routing is deciding which agent, team, or AI agent receives a contact, based on intent, language, skill, customer tier, and availability. Accurate routing is the cheapest available reduction in handle time, because a misrouted contact pays the cost of being read and reassigned twice.

Self-service is help a customer can complete without contacting support at all, such as a help centre article, an account action, or a guided flow. Its success is measured by task completion rather than page views, since a read article that fails to resolve still generates a contact.

A service level agreement sets the committed response and resolution targets for a customer, tier, or contract, often with financial penalties attached. Support operations design queues, routing, and escalation around SLA thresholds, which makes the agreement a structural constraint rather than a reporting line.

Shrinkage is the share of paid agent time unavailable for handling contacts, covering breaks, training, meetings, absence, and system downtime. Workforce plans that ignore shrinkage systematically understaff, because scheduled headcount and available headcount differ by a predictable and often substantial margin.

A ticket is the record of a single customer issue and everything done about it, including messages, internal notes, actions taken, and final disposition. The ticket is the unit most support reporting counts, which is why inconsistent ticket creation quietly distorts every downstream volume metric.

Time to first response is how long a customer waits before any human or AI reply arrives. It is the most visible service measure to the customer and the easiest to improve with automation, which is why it should always be read alongside resolution quality.

Triage is sorting incoming contacts by intent, urgency, and value before they are worked, so the most consequential conversations reach the right place first. Automated triage now performs the classification step, leaving prioritisation rules and exception handling as the main human contribution.

A unified inbox is one workspace where conversations from every channel land, so agents work a single prioritised list instead of switching between tools. It reduces context-switching cost and makes cross-channel history visible at the moment an agent is drafting a reply.

Wrap-up time is the after-contact work an agent performs before taking the next conversation, including notes, disposition coding, and system updates. It is frequently excluded from handle time reporting, which is the most common reason two teams’ average handle time figures cannot be compared.