Case 3 · Revenue ↑ · Merchant side
Stand-by-stand insight for a top European football club
Payment terminal data mapped to stadium sections — sales seen where they happen, crowds managed with it, returning fans recognized through it.
The situation. A leading European football club. Dozens of points of sale across the stadium, every one generating terminal transactions — but the club saw totals, not geography and not behaviour.
The leak. Sales per stand, crowd flow, and returning visitors were invisible. Staffing, stocking and crowd-management decisions ran on habit — while the data to steer them was already being produced by every terminal in the building.
What I did. Mapped terminal transactions to stadium sections using terminal location data, and built a management dashboard showing sales per section in near real time — giving the club a tool to steer event-day staffing and crowd management. Identified returning customers partially, through tokenized card and TWINT identifiers in the payment data — a first picture of visit frequency without collecting anything new.
If you run venues, shops, or any network of points of sale: you already own the behavioural data. No new tracking, no new tooling — mapping and dashboarding what your terminals already produce. That distinction is the whole point.
Working on something similar?
Twenty-five minutes on a call — or breakfast in Porto or Minho. You describe the stack; I'll tell you what I'd measure first.