Support Queue and Response Time Audit
Analyse queue data and find where customers wait.
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Audit our support queue. Ticket data for [PERIOD] with created time, first response time, resolution time, channel, priority and status: [TICKET_DATA]. Staffing by hour: [STAFFING]. Our targets: [TARGETS]. Calculate median and 90th percentile first response and resolution times, the share meeting target, and the volume by hour and day, with the arithmetic for at least two metrics. Identify peak times where staffing is short, tickets waiting longest, reopens, and channel differences. Create a table of findings and options (schedule changes, triage rules, canned answers for the top three topics). Provide a simple forecast of staffing needed per hour from ticket volume and average handling time, showing the formula (volume times handling time divided by 60 gives hours of work), and caveats. Use my data only, and label every estimate as an estimate. Add a worked percentile example with invented times: 20 first-response times with median 2 h 10 m and a 90th percentile of 9 h 30 m, and what the gap suggests about weekends or one product line. Include a daily workload formula: 180 tickets at 7 minutes each is 21 hours of work.
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