A Fortune 50 retail company handles customer service calls at a volume no team could read one at a time. Its quality team could review fewer than 10% of interactions by hand. When a customer problem surfaced, it took 7 days to reach leadership. Leadership was deciding on a sample.
We built an AI-powered review program that scores calls against the company's own service standards for policy, outcome, and how the customer felt. Nine in ten interactions now get a result, up from fewer than one in ten. The patterns across them, the problems that keep coming up, go to leadership as a regular report.
A customer problem now reaches leadership within 24 hours instead of 7 days, because the report is built from the reviews as they happen rather than from a sample read a week later.
The share of interactions reviewed went from under 10% to 90%. The time from a customer problem to a leadership report fell from 7 days to 24 hours. Reviewing that many by hand would have needed thousands of additional employees. The saving in operating costs came to $15M.
If your team reviews a sample of customer interactions because reviewing all of them is out of reach, the same program applies. The review uses your own standards, and the patterns reach leadership within a day.