For me, the most useful CX measurement comes from combining operational metrics with customer feedback and a little healthy skepticism about what the numbers appear to say.
We regularly look at metrics like first response time, resolution time, backlog, reopen rate, SLA attainment, and CSAT. Those measures help us understand whether customers are receiving timely, effective support and whether the overall operation is healthy.
Benchmarking is also valuable. Comparing an individual, team, channel, or issue type against the average can help identify outliers and point us toward areas worth investigating. But an outlier should start a conversation, not end one.
An especially efficient employee and a sloppy employee can look surprisingly similar on a metrics board. Both might have short handle times or high ticket volumes, but one may be resolving issues clearly and confidently while the other is rushing, missing details, or creating repeat contacts. The same is true in the other direction: an agent with longer resolution times may be struggling, or they may simply be handling the team’s most complex cases.
That is why context and segmentation matter. We try to look beyond overall averages and break the data down by contact reason, complexity, channel, customer tier, product, brand, and escalation type. Metrics like reopen rate, repeat contact, transfers, and customer effort can also help reveal whether apparent efficiency is actually creating more work later.
When setting targets, I prefer starting with the team’s current baseline rather than adopting an arbitrary industry benchmark. Customer expectations, contractual commitments, case complexity, and the type of support being provided should all influence what a good target looks like. From there, the goal should be measurable and realistic improvement over time.
Quantitative data is excellent at showing us where something may be changing. Qualitative feedback, ticket reviews, and conversations with agents and customers help explain why. The strongest CX decisions usually come from using both, while resisting the temptation to jump to conclusions based on a single dashboard.