Closed loop feedback

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.

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.

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.

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.

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.