I was stuck trying to measure support team performance beyond just ticket volume. Had two agents with similar resolution times but completely different workload compositions, and I couldn’t explain the gap. Standard metrics—tickets handled, resolution time—made them look equivalent when they clearly weren’t.
Worked with an AI assistant to build an effort-scoring system. The approach: export raw ticket data, map each tag to a complexity tier (Low/Medium/High/Critical), then apply a rule that combines the highest tier plus a bonus for volume. Built a Google Apps Script that processes 7,000+ tickets in seconds, flags unmapped tags, and outputs clean effort scores per ticket.
Turned out one agent consistently handles 14% harder work on one channel than another. Another agent I thought was underperforming was actually handling the most complex tickets in the team. The data completely reframed how I present performance to leadership.
The script runs on a button click, takes 2 minutes to add new data, and now I have a framework to set realistic SLAs instead of one-size-fits-all targets. Saved hours of manual spreadsheet work and weeks of guessing on staffing decisions.
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