Ticket transfer failure diagnosis assistant
Problem
When a supporter transfers a season ticket to a friend through the app and the transfer fails at the gate, the incident gets a note in a spreadsheet or a message in the gate staff group chat, whichever is closer to hand at the time. Supporter services deals with the complaint that follows, resolves it, and moves on. Nobody looks across incidents to see that failures cluster around transfers completed in the final hour before kickoff, or around a particular app version, or spike on high-demand fixtures when the ground's network is under strain. The same failure returns at the next comparable fixture, and each time it is treated as new.
Product idea
An assistant that takes the log of transfer failure incidents, entered directly or uploaded from the existing spreadsheet, and checks a new spike against the conditions recorded for past ones: how long before kickoff the transfer completed, app version, device type, and the fixture's demand tier. It proposes the shared condition that best explains the pattern and shows the specific past incidents it is drawing on, not just a score. It does not resolve individual transfer disputes and does not touch the ticketing platform directly. The first question it answers well is whether last Saturday's spike looks like the one from three fixtures ago, or something new.
Who it is for
Digital product managers who own the ticketing flow inside the app, and supporter services leads who log and resolve transfer complaints. The head of fan engagement is the usual sponsor once a pattern costs a matchday's worth of complaints.
Possible first version
A web tool where supporter services logs each transfer failure against a fixed set of fields: fixture, time before kickoff, app version, device type and demand tier. Past incidents can be bulk loaded from the existing spreadsheet. When a new incident is logged, the assistant checks it against the recorded history and surfaces the closest matching past cluster with the shared condition and the incident list behind it. Version one has no live connection to the ticketing platform or gate scanners; every field is entered by hand or imported as CSV.
- Build classification
- Workflow application
- Rough effort
- 3-5 week first release
- Roles involved
- Digital product manager, Supporter services lead, Head of fan engagement
- Relevant to
- Professional club, League office, Women's league, Venue & stadium operator
- Systems in play
- Ticketing systems, Mobile apps, Spreadsheets, Messaging apps
- Product framing
- Diagnose
Questions we get asked
What do we need before this produces anything useful?
A run of past transfer failure incidents with at least the basics recorded: which fixture, roughly when the transfer completed relative to kickoff, and ideally app version and device type. A handful of incidents will not show a pattern. Somewhere around a season's worth, or several comparable high-demand fixtures, is closer to where correlation starts to mean anything. If your current log is just a note in a messaging group, the first job is getting supporter services into the habit of capturing those fields consistently, which is worth doing regardless of whether this tool follows.
Does this replace the spreadsheet supporter services already uses?
No, not in version one. It reads from it. The spreadsheet or incident log stays the system supporter services types into during a shift, and this tool ingests that log, either through a bulk CSV import or by logging alongside it, to look for patterns across incidents that nobody has time to spot by re-reading old rows. Whether it earns a place as the primary log later is a separate decision, best made once the correlation is proven worth the extra step.
Our transfer failures all seem different from each other. Is there really a pattern to find?
Sometimes not, and the honest answer is that the assistant should say so rather than force a match. Some failures are genuinely one-off: a supporter mistyped an email, a phone was offline. What it is built to catch is the failure that looks unrelated at the gate but shares a condition with three others from the season, transfers all completed inside the last twenty minutes before kickoff, say. If the incidents really are unconnected, it will show no strong match, which is itself useful information.
Who has to maintain this once it exists?
Whoever owns the incident log day to day, usually a supporter services lead, needs to keep entries consistent enough that the fields it correlates on are actually filled in. The digital product manager is the one who acts on what it finds, since the fix usually lives in the app or the transfer flow, not in supporter services. Left unmaintained, the log degrades and the correlations get less reliable, the same risk as any tool that depends on people recording things properly at the time.
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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