Attendance reconciliation report across ticketing and till systems
Problem
Every Monday the insight team pulls a ticketing export and a till or point-of-sale export for the weekend's fixtures and rebuilds the same join in a spreadsheet, because the two systems were never built to agree on what a fixture is. Box office counts pre-sale, the turnstile counts who actually walked in, and the till system logs spend against whichever terminal was open. When commercial and operations each report attendance for the same fixture, the numbers differ, and both are technically correct because neither is working from a shared definition. The analyst who does the join knows why the high-variance fixtures look odd, but that knowledge lives in their head and a spreadsheet tab nobody else opens, so the same investigation gets repeated whenever someone asks.
Product idea
A reconciliation report that takes a ticketing export and a till export for a set of fixtures and aligns them by fixture and date rather than forcing one system to be the master. It shows a reconciled attendance and spend figure per fixture alongside the variance between sources, and where the variance crosses a set threshold it surfaces the specific line items driving the gap: a block of walk-up sales missing from the pre-sale file, a terminal that logged spend against the wrong fixture. It does not decide which source is correct; that judgement stays with the analyst. The output is a one-page report per fixture, exportable, so the same investigation is not repeated by hand every week.
Who it is for
Insight and BI analysts who currently rebuild this comparison by hand, and the head of data who wants one defensible attendance figure per fixture rather than two competing ones.
Possible first version
Upload of two CSV exports, ticketing and till, matched on fixture date and venue. A reconciliation screen showing the reconciled total, the variance, and a drill-down into the line items causing it. Export to CSV or PDF for a board pack. Version one has no live connection to either source system: files are uploaded manually, and there is no automated rule for which figure is treated as correct when they disagree.
- Build classification
- Micro-tool
- Rough effort
- 10-day prototype
- Roles involved
- Insight and BI analyst, Head of data, Analytics engineer
- Relevant to
- Professional club, Venue & stadium operator, League office, Collegiate athletics
- Systems in play
- Ticketing platforms, Spreadsheets, Business intelligence and visualisation tools
- Product framing
- Analyse data
Questions we get asked
What do we need to hand over on day one to try this?
Two exports covering the same set of fixtures: whatever the ticketing platform produces as a sales or attendance file, and whatever the till or point-of-sale system exports as transaction data, both with a recognisable fixture date. No live connection or credentials are needed for the first version. If the two files use different fixture naming, that mapping has to be sorted before the demo means anything, which is usually the first thing worth checking.
Does this replace the BI tool we already report out of?
No. It produces a reconciled figure and the reasoning behind it, not a dashboard. The output is meant to feed into whatever reporting tool already exists, or sit next to it as the source a disputed number gets checked against. It does not attempt to become the warehouse's single definition of attendance either; that is a separate and larger decision involving whoever owns the metric.
We already have a spreadsheet that does this. Why change it?
Because the spreadsheet works until the person who built it is away, or a new fixture format breaks a formula nobody remembers writing. The tool does not do anything the spreadsheet conceptually does not already do; it makes the join repeatable, keeps the variance logic visible instead of buried in a cell reference, and produces the same output whoever runs it. If the current spreadsheet is genuinely stable and understood by more than one person, this may not be worth building yet.
What happens when the two source files disagree and there's no obvious explanation?
It gets flagged for manual review rather than guessed at. The tool surfaces the line items behind a variance so a person can judge it faster, but it will not silently pick a number when the cause is not clear. That is a deliberate limit: an automatic best guess on a figure that ends up in a board pack is a worse outcome than a flagged fixture someone has to look at.
Is this your workflow?
Tell us one sports workflow that still runs on paper, spreadsheets, WhatsApp or an outdated system. We will map it and show you what a simpler product looks like.
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