IntermediateSITUATIONAL
Imagine your latest monthly dashboard shows that average handle time has increased, while customer satisfaction scores for claims‑related calls have dropped, but staffing levels and call volumes are unchanged. Based on analysis work you’ve actually done before, what steps would you take to diagnose the root cause and validate your hypothesis?
Other
General

Sample Answer

When I’ve seen that pattern before—AHT up, CSAT down, volume and staffing flat—I treat it as a signal that the work itself has changed. I’d start by segmenting the data: claims type, complexity, call reason codes, and agent groups. In one case, that quickly showed a 25% AHT spike and a 9‑point CSAT drop specifically in bodily injury claims. From there, I pulled 30–40 call recordings and used text analytics on notes to see themes. We discovered a new documentation checklist had been rolled out, adding 3–4 extra verification steps without revising call flows or scripts. My hypothesis was that process friction, not agent behavior, was driving both metrics. To validate, I ran a two-week test: a streamlined version of the checklist with pre-call system prompts for a pilot group of 20 agents. Their AHT dropped by 14% and CSAT improved by 6 points, confirming the root cause and supporting a broader rollout.

Keywords

Use segmentation (claim type, reason codes, agent cohorts) to localize the issueCombine quantitative dashboards with qualitative review of calls and notesForm a clear, testable hypothesis around process changes vs. agent performanceRun a controlled pilot to validate the hypothesis and measure impact
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