Self-service

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 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.

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.

Automation rate is the percentage of incoming contacts handled entirely without a human agent. It measures reach rather than quality, so support teams read it alongside containment rate and satisfaction scores to confirm that automated volume is genuinely resolved rather than simply absorbed and later reopened by the customer.