Red Eye Tickets

Production evidence

How the production numbers are counted

The case study uses conservative display floors from a dated production snapshot. This page defines each existing scale claim and makes its limits explicit.

Snapshot: September 11, 2026 at 3:51 PM PDTRead-only queryQA fixtures excluded
01

The publishable production snapshot

285Sales-generating events
19.1K+Unique buyers
28.5K+Paid orders
42.7K+Tickets sold
$1.44M+Captured customer charges

Counts and currency are compacted into conservative public display floors. Exact totals, supporting figures, cohort notes, and query provenance remain in the dated internal source record.

02

Definitions

Sales-generating events
Distinct events represented by at least one paid order.
Unique buyers
Distinct normalized paid-order emails, falling back to user ID only when email is missing.
Paid orders
Orders with recorded paid timestamps, excluding known check-in pilot fixtures and comp-only orders. This historical portfolio cohort is retained across refreshes.
Tickets sold
Non-canceled, non-comp line-item quantities on recorded paid customer orders, plus nonduplicative legacy tickets. This historical cohort is not a settled-payment or attendance measure.
Captured customer charges
Captured customer charge volume including fees and taxes; this is payment volume, not ticket-sales revenue.
03

Exclusions and privacy boundary

  • The published figures are aggregate counts and sums; customer names, emails, order IDs, and payment details are not published.
  • Unique buyers use normalized paid-order identity rather than the larger registered-account count.
  • Captured customer charges include fees and taxes and are not labeled as ticket-sales revenue.
  • Tickets sold follow the report's lifetime sold-ticket cohort and are not represented as attendance.
  • All lifetime totals include July 15–18; those dates are excluded only from the corrected payment-method cohort.
04

What the metrics do—and do not—prove

These figures establish operating scale for a production system. They do not, by themselves, prove that one feature caused conversion, revenue, or reliability to improve. Any before-and-after claim requires its own dated cohort, denominator, and measurement window.

Verification boundary: the values were checked against a read-only production database snapshot and the application's reporting definitions. They have not been independently audited by a third party.