Customer experience glossary

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 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.

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.