Before

The team’s ticket tracking process ran across two disconnected sources with no automation between them:

  • Two separate exports (a general request tracker and an account-creation form log) that had to be manually merged for any reporting
  • “Responded within 4 hours” was a self-reported field, filled in by hand, with no verification against actual reply timestamps
  • SLA measured in raw calendar time — a ticket submitted Friday evening looked identical to one that sat unanswered all weekend
  • Request IDs weren’t actually unique; the field was quietly reusing the submitter’s ID across every ticket they’d ever filed
  • Ticket type and account classification depended on someone reading free-text requests and typing the category in by hand
  • Assignee tracking was 100% manual, with no reliable link back to who actually claimed a given ticket

After

Rebuilt into a live tracker: every request classifies, logs, and times itself the moment it comes in — no manual entry left in the loop.

BeforeAfter
Manual merge of two exportsSingle live-updating tracker
Self-reported SLA fieldVerified SLA, measured against real reply timestamps
Non-unique Request IDsAuto-generated, collision-safe sequential IDs
Manual type/category taggingAuto-detected from the request’s own structure
Manual assignee loggingCaptured automatically the moment ownership changes
Static monthly reportingLive monthly summary, feeding a team-wide metrics dashboard

Every ticket now flows through with:

  • A unique Request ID, generated the instant it’s submitted
  • Type and account-category classification, detected directly from the request itself
  • A first-response timestamp captured the moment a real reply lands
  • SLA elapsed time calculated only across actual business hours (9:30am–6pm, Monday–Friday) — no more weekends or after-hours gaps counted against response time
  • A visible flag on anything the automatic classification couldn’t confidently resolve, with a one-click way to correct it
  • Assignee, filled in automatically the moment ownership changes on the underlying ticket list

A short reference guide sits at the top of the tracker covering what’s automatic, what still needs a human, and what each color flag means — so the system stays legible to anyone else on the team, not just whoever built it.

Monthly ticket volume chart with a sample data insight and four callouts on the benefits of a self-updating, in-house tracker Sample data shown for illustration — the live tracker reports on real ticket volume and SLA performance.