An AI system can answer quickly and still leave the customer with a problem. To judge whether it helps, look beyond the number of automated replies.
1. Answer accuracy
Sample real conversations and check whether the reply matched an approved source. Track errors by cause: missing information, outdated information, misunderstanding or a failed integration. Fixing the cause matters more than changing a dashboard score.
2. Resolution
Did the customer’s issue reach a useful end? Define what “resolved” means for each enquiry type. A shipping-policy answer and a completed delivery investigation are different outcomes.
3. Handoff quality
When a person takes over, do they have the context and authority to act? Measure transfers that were necessary, transfers that could have been avoided and cases that were escalated too late.
4. Repeat contact
If customers return with the same issue, the first interaction may not have worked. Review the cause before calling that conversation a success.
5. Customer feedback
Ask a short, relevant question after the issue is handled. Read comments alongside scores, especially when the volume is small.
Choose a few common enquiry types, record a baseline and review samples regularly. The useful question is not “How much did the AI answer?” but “Did customers get the right help?” ReplyCleverly’s AI customer service page provides the product context for this measurement plan.