The 9 charts I check every Monday: deflection by topic, escalation reasons, off-brand hits, cost per resolution. With the SQL we use.
Most chat agent dashboards are designed for the vendor's marketing, not for the operator's Monday morning. After 14 months of running chat agents in production, here are the nine charts I actually check, and the SQL behind each. The numbers we hit, with the baselines Below is a representative 90-day delta from a recent client deployment in this category. The baseline is from the trailing 90 days before deploy, the post numbers are from the 90 days after first traffic. Metric Baseline After 90 days Delta --- --- --- --- Inbound handle rate 47% 94% +47pts Median time to first response 4h 32m 38s -99% Conversion to booked outcome 18% 31% +13pts Cost per resolved interaction $7.10 $1.20 -83% 5-star reviews per month 6 14 +133% Net CSAT (sample of 200) 71 79 +8pts Numbers vary by vertical.
Once you get the data pipeline right and the eval gate in place, handle rate jumps 40-60 percentage points within the first month, conversion follows two to four weeks later as the prompts get tuned, and cost-per-interaction drops as caching and model routing kick in. The CSAT delta is the one that surprises new operators. The conventional wisdom that customers hate AI is mostly wrong when the AI works. Customers hate broken IVRs and 9-hour callbacks. An AI that answers in under a minute and gets the job done is preferred to a human callback two-thirds of the time in our data.
The 9 charts I check every Monday: deflection by topic, escalation reasons, off-brand hits, cost per resolution. With the SQL we use. It is filed under AI Strategy because that is where operators looking for this problem actually start, and it is written from production work rather than from a content calendar.