How to measure the customer experience as AI scales

Summary

CSAT captures fewer than 10% of conversations, and its responses skew toward delighted or furious customers. Most customers say nothing, leaving teams to assess customer experience from an unrepresentative sample that compresses product, policy, and service issues into one score. As AI handles more conversations end-to-end, the post argues for scoring every AI and human interaction on service quality, resolution, customer effort, and related attributes rather than relying on survey responses alone. Fin’s CX Score assigns each interaction a 1–5 score and surfaces reasons behind it, providing roughly five times more coverage than CSAT for most teams. Fin sets targets from CX Score attributes, operational correlations, historical performance, modelling, and team ambition, then uses recurring patterns to route fixes to support, product, or workflow owners.

Takeaways

1. 1

   Fin’s CX Score evaluates every AI and human interaction and provides roughly five times more conversation coverage than CSAT for most teams.

2. 2

   Fin’s initial targets were 80% for Fin support, 70% for human support, and 78% overall, based on score modelling, historical performance, and team ambition.

3. 3

   Conversation-level patterns can reveal poorly performing topics, channel differences, handoff friction, and whether root causes belong to support, product, or a workflow.