Customer experience glossary

Anomaly detection identifies data points or patterns that deviate from an established baseline of normal behavior. Methods range from static statistical thresholds, which flag values beyond a fixed range, to seasonal or time-series decomposition, which accounts for expected cyclical variation, to unsupervised machine learning models that learn normal patterns without labeled examples. In CX, it flags unexpected spikes in ticket volume, negative sentiment, or a specific contact reason before they become widespread. Thresholds set too tightly generate false positives and alert fatigue, and real problems get buried in noise. OnClarity’s Voice of Customer includes real-time alerting that can flag unusual shifts in feedback volume or sentiment as they surface.

Automated reporting is the scheduled or event-triggered generation and distribution of analytics outputs. The goal is that no one rebuilds a deck by hand each cycle. It differs from dashboards, which sit always-on and wait to be pulled up. It also differs from alerts, which fire the moment a threshold is crossed. A sound setup specifies refresh cadence, audience-specific views, delivery destinations, and auditable underlying figures, all core pieces of contact center AI terminology. Example: OnClarity’s Voice of Customer pushes a weekly briefing on top complaint drivers into Slack, Jira, and Linear, with real-time alerting for sudden shifts. Teams can also follow specific topics so a spike triggers an alert outside the normal reporting cycle.

Churn rate is the percentage of customers who stop doing business with a company during a set period. It’s calculated as customers lost during the period divided by customers at the start of the period. Logo churn counts customer accounts. Revenue churn measures dollars lost. The two figures can diverge if the customers who left were smaller than average. Churn also splits by cause: voluntary churn, where customers choose to leave, and involuntary churn, caused by failed payments or expired cards. A monthly subscription and an annual contract produce structurally different churn math, so comparisons only hold within the same industry and billing cycle.

A churn signal is a pattern in support contacts that predicts a customer is about to leave, such as repeated escalations, cancellation questions, or a sharp drop in usage alongside unresolved complaints. Identifying these patterns early lets retention teams intervene while the relationship is still recoverable.

Closed loop feedback is the practice of acting on individual customer feedback and reporting back to the customer about what changed as a result. It operates on two levels. The inner loop is where a team responds directly to the customer who gave the feedback, typically within a defined service window. The outer loop is where the underlying theme is routed to whichever team can address the root cause. Closing the loop requires three things: a named owner, a deadline, and a reply to the customer. OnClarity’s Voice of Customer pushes daily reports, feedback topics and bug insights into Slack so themes reach the right owner without manual handoff.

CSAT, or customer satisfaction score, is a post-contact rating averaged across responses and usually collected with a single question immediately after an interaction closes. Because only a small fraction of customers respond, CSAT reflects the experience of people motivated to answer rather than the whole contact population.

Customer effort score measures how hard customers say it was to get their issue resolved, usually on a seven-point scale asked immediately after contact. Effort predicts loyalty more reliably than satisfaction, because a customer can be satisfied with an outcome yet exhausted by the process of reaching it.

Customer friction is any step, delay, or requirement that makes a task or issue harder to complete than necessary. Repeat contacts, channel switching, re-authentication, IVR misroutes, and unclear policies are common sources. Friction isn’t captured by a direct satisfaction question, so it gets inferred from effort and behavior. Customer Effort Score, repeat contact rate, and handoff counts all serve as proxies. High effort scores or frequent transfers signal friction even when a customer satisfaction index stays flat.

A customer health score is a composite, weighted metric that combines multiple account signals into a single indicator of renewal, expansion, or churn risk. Inputs typically include product usage depth and frequency, support ticket volume and severity, survey responses such as satisfaction or effort ratings, invoice and renewal signals, and stakeholder engagement across the account. Providers assign weights to each input based on its correlation with retention outcomes. The resulting score gets bucketed into bands, commonly red, yellow, and green, to flag risk level at a glance. No standard formula or weighting scheme exists; the mix reflects each business’s product and customer base. Customer success teams use score drops to trigger outreach before renewal is at risk.

Customer insights are conclusions about customer behavior and needs that are specific enough to act on. They differ from raw feedback, which is simply what a customer said, and from analytics, which shows what happened in aggregate. A customer insight explains why something happened and what to change next. Customer insights come from combining feedback, conversation data, usage data, and survey responses across channels. The step that matters is classifying patterns by root cause, not just counting volume. Example: a spike in refund-related tickets across chat, email, and reviews first looks like a support volume problem. Root-cause classification shows most of those tickets trace back to a single checkout error. OnClarity’s Voice of Customer surfaces that root cause automatically and pushes the finding into Slack, Jira, or Linear. The product team that owns the checkout flow gets the ticket directly, instead of support agents closing the same complaint one at a time for weeks.

A customer journey map is a visual document that shows the stages a customer moves through when interacting with a company. It’s built from the customer’s outside-in perspective, not from internal process design. A usable map includes defined stages, such as awareness, consideration, purchase, and support. It also includes the touchpoints within each stage, the customer’s goals and actions, their emotional state, and points of friction. This differs from a service blueprint, which maps the internal systems, staff, and processes operating behind each touchpoint. Customer journey mapping fails most often when teams build maps from internal assumptions about customer behavior, instead of actual contact records, support transcripts, and feedback data.

Customer lifetime value is the total gross margin a business expects to earn from a customer across the full relationship. It is not the revenue that customer generates. A workable formula is average purchase value multiplied by purchase frequency, multiplied by average customer lifespan, multiplied by gross margin. Historic CLV sums the margin a customer or cohort has already delivered. Predictive CLV forecasts future margin and discounts it to present value. The most common error is substituting revenue for margin. That substitution inflates CLV and leads teams to justify acquisition spending that a true margin-based figure would not support.

A customer satisfaction index is a composite score that combines multiple satisfaction measures into a single number, usually scaled from 0 to 100. The term covers two distinct uses. The American Customer Satisfaction Index, founded at the University of Michigan and now run by ACSI LLC, applies a standardized econometric model across roughly 400 companies to benchmark satisfaction by industry and over time. A company-internal customer satisfaction index instead blends its own CSAT, effort, and loyalty responses into one weighted figure. Unlike a single-question CSAT percentage, which reflects one interaction, a CSI aggregates several attributes. Its weighting method has to stay documented and unchanged for the score to be comparable period over period. See OnClarity’s CSAT glossary entry for how the single-question version works.

CX analytics is the practice of combining three data families to explain why customer experience is trending a certain way, beyond simply reporting that it is. Those families are operational metrics (handle time, resolution rate, channel mix), interaction data (calls, chats, emails), and solicited feedback (surveys, reviews). Where standard reporting shows a CSAT drop, voice of customer analytics attributes it to a cause. For example, a policy change surfaced across three channels in the same week, assigned to a team for action. OnClarity’s Voice of Customer supports this by aggregating 100+ feedback sources and classifying them by topic and sentiment to surface root causes. This closes the gap between closed loop feedback and first contact resolution. A mature CX analytics glossary should separate these three data families clearly, since vendors often blend them into a single “experience score” that hides which one moved.

Data cleansing is the process of correcting or removing records that are duplicated, incomplete, malformed, or inconsistent before they enter analysis. Standard operations include deduplication, standardizing formats (dates, channels, agent names), validating entries against defined rules, handling missing values, and reconciling customer or ticket identifiers across source systems. Skipping this step lets duplicate tickets inflate complaint volumes and distort topic rankings. For example, a customer’s chat and email thread about the same issue should be merged into one case before counting contacts. Cleansing differs from data enrichment, which adds new attributes rather than fixing existing ones. It often happens as a first pass over unstructured data, before any topic or sentiment model runs.

Data enrichment is the process of attaching additional attributes to an existing customer record. These attributes can come from internal systems or external sources, so analysis can be segmented rather than read as a flat total. In CX, this usually means joining account tier, plan type, tenure, region, channel, or order history onto a feedback or support ticket. A plain complaint count only shows volume. An enriched record shows which segment it belongs to. For example, a spike in billing complaints might look like a general product problem until enrichment reveals it sits almost entirely within one plan tier. Enriched data carries the same privacy obligations as the source record it is attached to. OnClarity is SOC 2 Type II certified, is GDPR and HIPAA Ready, and aligns with Saudi PDPL. Enrichment pipelines should meet the same compliance standard as data cleansing.

A detractor is a Net Promoter Score respondent scoring between zero and six, treated as an active churn and word-of-mouth risk. Detractor comments are usually the most useful part of an NPS programme, because they name specific failures that aggregate scores cannot surface on their own.

Experience management is the discipline of collecting experience data across customer, employee, product, and brand touchpoints, then running a structured process to act on it. It differs from customer experience (CX), which describes the interactions themselves. It also differs from voice of customer (VoC), which covers only the feedback-collection layer. Experience management is the broader system that pairs collection with follow-through. It combines two data types: operational data (what happened, such as transaction records or support logs) and experience data (how it felt, captured through surveys, reviews, and conversations). Platforms like OnClarity’s Voice of Customer sit inside this discipline, feeding the collection stage. Organizationally, experience management typically spans CX, product, and insights teams rather than living in one department.

Interaction analytics is the analysis of full conversation transcripts to surface demand drivers, friction, and risk across every contact rather than a sampled few. Because it reads what customers actually said, it identifies emerging issues that structured ticket fields and survey scores are too coarse to reveal.

Journey mapping lays out every touchpoint a customer passes through, from first awareness to renewal, to find where support demand is created. The value for support teams is diagnostic: most contact volume originates in an upstream product or billing step rather than in the support experience itself.

Net Promoter Score is a likelihood-to-recommend score used as a loyalty benchmark, calculated by subtracting the percentage of detractors from the percentage of promoters. It produces a single comparable number, but the free-text comment attached to each score carries most of the diagnostic value.

Net revenue retention (NRR) is the percentage of recurring revenue a company keeps from its existing customer base over a set period. It includes expansion, contraction, and churn, but excludes revenue from new customers. The formula: starting recurring revenue plus expansion minus contraction minus churn, divided by starting recurring revenue, times 100. NRR above 100% means upsells and expansions outpaced downgrades and cancellations, so the existing base grew without adding a new logo. NRR below 100% signals that churn and contraction outweighed expansion. Companies typically measure NRR over a trailing 12-month window, tracked release over release to see whether retention is improving or slipping.

Net sentiment is a single directional score for a topic or time period. It is calculated as positive mentions minus negative mentions, divided by total mentions. Unlike a per-item sentiment score, which rates one piece of feedback, net sentiment aggregates many. Buyers should know two limits. Neutral mentions are handled inconsistently across vendors. And the score is volume-sensitive, so a stable reading can mask rising positive and negative shifts that cancel each other out. Example: a CX team compares net sentiment on checkout-flow feedback before and after a release. OnClarity’s Voice of Customer supports voice of customer analytics by classifying feedback by topic and sentiment, so a net sentiment score can be traced back to the comments driving it.

Omnichannel customer experience is an approach where every channel draws on one shared conversation history and one customer record. A customer who opens a chat today and calls tomorrow doesn’t have to repeat themselves. This differs from multichannel service, which offers the same range of channels but runs each as a separate silo with no shared context. The practical test is simple: does context carry across a channel switch? If an agent can’t see what happened elsewhere, it’s multichannel, regardless of how many channels exist. Omnichannel customer experience is a data architecture question, not a channel-count question. OnClarity’s platform, which processes over 50 million customer interactions monthly, is built on this unified-record model.

Root cause analysis is the practice of tracing a symptom, such as a spike in complaints, back to the underlying process, policy, or product defect that produced it. The alternative, resolving each ticket in isolation, never stops the symptom from recurring. Established methods include 5 Whys (repeated questioning to reach the originating cause), fishbone (Ishikawa) diagrams (mapping possible causes by category), and Pareto analysis (isolating the small number of causes driving most of the volume). In CX, this starts with clustering tickets by theme, then tracing the largest theme to its source. OnClarity’s Voice of Customer automates this by surfacing root causes directly from feedback. Grubhub uses it to turn feedback into insights that shape its roadmap. The real test of success is recurrence: a fix that addresses the root cause should stop the issue from reappearing.

Sentiment analysis scores the emotional tone of a conversation to flag risk and measure experience without waiting for a survey response. Applied across every contact rather than a sampled few, it covers the large majority of customers who never complete a satisfaction survey at all.

A sentiment score is a numeric value representing the emotional polarity of a piece of text or speech. It’s typically scaled from -1 (negative) to +1 (positive), with 0 as neutral, or normalized to a 0-100 index for reporting. Aggregate sentiment gets calculated by classifying a batch of conversations, then averaging or netting the results (percent positive minus percent negative). Sentiment differs from CSAT: sentiment is inferred from language, while CSAT is a rating the customer directly reports. Accuracy drops with sarcasm, mixed-polarity messages, and multilingual or code-switched text. OnClarity’s Voice of Customer pairs sentiment scoring with topic classification to ground each score in context, surfacing the patterns and root causes behind a customer satisfaction index.

Service recovery refers to the actions an organization takes after a service failure to resolve the problem and restore the customer relationship. A complete recovery process has four components: acknowledging the failure, fixing the underlying issue, offering compensation where warranted, and following up to confirm the customer is satisfied with the outcome. The “service recovery paradox” is the claim that a well-handled failure leaves customers more satisfied than if no failure had occurred. Some studies support this claim; others contradict it. Evidence suggests it holds only for minor failures resolved quickly and fairly, not as a general rule. Speed is the variable most consistently linked to recovery outcomes. Recovery failures show up in reviews and sentiment data before they move a brand health score. Tools like OnClarity’s AI Agent QA, which evaluates 100% of voice and text engagements against your own evaluation matrix, flag breakdowns early enough to act.

Speech analytics automates analysis of recorded or live customer calls. It uses speech-to-text transcription combined with natural language processing to extract keywords, topics, and sentiment. Many systems also measure acoustic signals such as silence, talk-over, and speaking pace, to flag stress or disengagement that word choice alone would miss. Real-time speech analytics prompts agents mid-call and raises live compliance flags. Post-call analysis surfaces trends and root causes across a larger call sample. Standard uses include detecting required compliance phrases, categorizing contact reasons, and feeding evidence into quality assurance scoring.

Text analytics automates the process of extracting structure and meaning from unstructured written feedback: support tickets, chat transcripts, survey verbatims, reviews, and social posts. Core techniques include classifying text into predefined topic taxonomies, extracting named entities such as products, features, and locations, modeling topics to surface unlabeled themes, and tagging a sentiment score to gauge tone. The practical output is a count of how often each issue appears and how that frequency trends over time. Results are only as useful as the underlying taxonomy: a vague or inconsistent category structure produces noisy, hard-to-action data. OnClarity’s Voice of Customer applies these techniques across feedback aggregated from 100+ sources.

Topic clustering groups conversations by theme to size the drivers behind contact volume, without requiring an analyst to define categories in advance. Because clusters emerge from what customers actually said, the method surfaces new issues that a fixed intent taxonomy would file under a generic other.

Unstructured data is information with no predefined schema: call recordings, chat transcripts, email bodies, open-ended survey comments, app reviews, and social posts. It contrasts with structured fields like ticket category, CSAT rating, or handle time, which record that something happened but not why. In CX, the diagnostic detail lives in the free text. A ticket disposition coded “billing” only becomes useful once the transcript shows customers describing a confusing invoice layout. Voice of customer analytics depends on reading this text at scale. OnClarity’s Voice of Customer does this for Grubhub, unifying diner feedback into one view. Before analysis, unstructured text typically needs data cleansing to remove duplicates and standardize formatting.

A verbatim is a customer’s exact words, kept unedited as evidence in voice-of-customer reporting. Verbatims carry the specificity that aggregate scores strip out, which is why they persuade product and executive audiences when a percentage change on a dashboard cannot do so on its own.

Voice of customer is the discipline of turning reviews, support tickets, surveys, and conversations into operational decisions. The distinguishing feature of a working programme is the closed loop: findings reach the team that can fix the cause, and the resulting change is measured against contact volume.

Voice of employee (VoE) is the structured collection and analysis of feedback from staff about their work, tools, and observations of customer problems. Programs combine pulse surveys, eNPS, post-shift feedback, and agent-flagged contact reasons to capture both sentiment and operational detail. Frontline agents often notice recurring complaints, product bugs, or policy friction before they surface in a customer satisfaction index, which makes VoE a leading indicator. Paired with voice-of-customer data, VoE helps explain why an index score moved and points toward root causes. Response honesty depends on anonymity and survey cadence: infrequent or attributable surveys tend to understate real problems.