Customer experience glossary

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.

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.

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 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.

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.

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. Clarity’s platform, which processes over 50 million customer interactions monthly, is built on this unified-record model.

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.

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.

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.