Ticketing analytics worth watching

Most ticketing dashboards are a vanity number (gross) with a graph. The analytics that change decisions are about pace, source and repetition — and if your platform won't show them, it should at least export cleanly enough for a spreadsheet that will.

The four decision-grade metrics

1. Sales pace vs capacity-days: tickets/day against the days remaining — the number that says 'push now' or 'relax'. A flat line three weeks out is an action signal; gross revenue isn't.
2. Channel attribution: which link sold which ticket (UTM-tagged links per channel at minimum). This is how you learn your Instagram sells and your posters don't — or vice versa.
3. Arrival curve: scan timestamps by minute — next event's staffing, bar stocking and support-act billing live in this chart (see door ops).
4. Repeat rate: % of buyers who've bought before — the single best health metric for a recurring event. Needs cross-event attendee identity, which not every platform models.

Dashboard minimums to demand

• Real-time sales with per-tier breakdown and refunds netted honestly.
• Fee and VAT lines separated (your accountant's afternoon, again).
• Full CSV export of orders and scans — clean exports forgive most dashboard sins.
• Discount-code performance, if you use codes — redemption × margin, not just count.

Privacy note

Analytics on attendee behaviour is personal data processing — aggregate freely, but per-person profiling (beyond purchase history you legitimately hold) needs the same UK GDPR care as marketing. The useful metrics above are all aggregates; treat that as a design constraint, not a limitation.