Fan history query tool across ticketing, CRM and app data
Problem
Ticketing data lives in the ticketing platform, loyalty points in a separate rewards tool, app usage in analytics nobody reads, and email opens in the messaging platform. Each system has its own identifier for the same supporter, so answering an ordinary question, such as how many first-time attendees from last month came to a second game, means the CRM manager exporting four CSVs and joining them by hand in a spreadsheet, matching on name and email address and hoping the spellings line up. The result is usually stale by the time it's finished, and it is rebuilt from scratch every time the marketing manager asks a slightly different question. Nobody outside that one person can get an answer without waiting days, so most questions about supporter behaviour simply go unasked.
Product idea
A reporting layer that joins ticketing, CRM, app and email records into one fan record using email and account identifiers as the match key, with a confidence flag on records it cannot match automatically. On top of that sits a query box where the head of fan engagement or marketing manager can type an ordinary question, such as 'how many first-time attendees in the last three months bought a second ticket' or 'which supporters opened the last five emails but haven't attended in a year', and get a table and a chart back, not a written narrative. Saved queries can be scheduled to land in an inbox weekly. It does not build or send campaigns itself: it answers the question that decides whether a campaign is worth building.
Who it is for
The CRM manager and marketing manager who build these reports by hand today, and the digital product manager who owns the underlying data links. Sponsored by the head of fan engagement.
Possible first version
A data model populated from manually uploaded exports (CSV from the ticketing platform, the CRM and the email platform) rather than live connections, a matching step that joins records on email address and flags anything below a confidence threshold for manual review, and a query box that translates a fixed set of common question patterns (attendance frequency, repeat rate, email engagement crossover) into a table and chart. No live sync, no write-back to any source system, and no support for open-ended questions outside the patterns built into version one.
- Build classification
- Platform module
- Rough effort
- 3 month first phase
- Roles involved
- Head of fan engagement, CRM manager, Marketing manager
- Relevant to
- Professional club, League office, Women's league, Venue & stadium operator
- Systems in play
- CRM platforms, Ticketing systems, Email and messaging platforms, Spreadsheets
- Product framing
- Analyse data
Questions we get asked
What data do we need to hand over before this could work?
Version one runs on exports you already know how to produce: a ticketing export, a CRM export and an email platform export, each with an email address or account ID present. No live connection is set up first. The accuracy of the matching step depends entirely on how consistently those emails are captured at point of sale and sign-up, which is worth checking before committing to a build, because it is usually messier than teams expect.
We already pay for a CRM with reporting built in. Does this replace it?
No. The CRM's reporting only sees what happens inside the CRM. This sits above it and the ticketing platform and the email tool, answering questions that need all three at once, such as whether an email opener actually attended. Campaign building, segmentation for sending and record management stay in the CRM. This is a question-answering layer, not a replacement system.
Our CRM manager already builds these reports by hand. Why would they trust this instead?
They probably won't, initially, and shouldn't until the matching logic has been checked against a report they already trust. The honest approach is to run the tool alongside the manual process for a few cycles and compare answers before anyone relies on it alone. If the match rate is poor because of inconsistent email capture, that is worth knowing regardless of whether the tool is adopted.
Can it tell us why a supporter stopped coming, not just that they did?
No. It reports patterns in the data that already exists: attendance frequency, spend and engagement crossover. It has no access to anything a supporter hasn't already told the organisation through a system, so it cannot explain motivation or answer anything that needs actual outreach. Questions like that stay a job for supporter services, not the query tool.
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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