Natural-language reporting tool for the season ticket base
Problem
Questions about the season ticket base surface at board meetings and sales planning sessions: which price tier renews worst, how many members used fewer than half their seats last season, which access control gate is under strain on a big night. Answering any of them today means someone pulling a sales export from the ticketing platform, a renewal export from the CRM, and a scan log from access control, and stitching them together in a spreadsheet built for that one question. The spreadsheet is rebuilt from scratch next time because nobody kept the formulas, and the exports rarely land on the same day, so the numbers do not quite match. The person who can do this is usually one analyst, and the question waits for their calendar.
Product idea
A reporting tool built around the exports the organisation already produces: a sales file from the ticketing platform, a renewal file from the CRM, a scan log from access control. Staff ask a question in plain English, such as 'how many premium members used fewer than half their seats last season', and get back a table with the underlying rows, not just a number, so the answer can be checked. A question can be saved as a recurring report that runs monthly and lands in an inbox rather than being rebuilt each time. It does not predict which members will churn and does not write anything back to the CRM: it answers the question asked and shows its working.
Who it is for
Head of ticketing and membership managers who currently wait on a single analyst, and the sales manager who needs the same numbers before a renewal push each season.
Possible first version
The smallest useful version accepts manually uploaded CSV exports from the ticketing platform, the CRM and the access control scan log, and joins them into one table by member ID. Staff type a question in plain English or pick from a set of common templates, and see a results table with an optional bar chart, plus a button to save the query as a report that emails monthly. Version one has no live connection to any of the three source systems and no predictive scoring of renewal risk: those exports are uploaded by hand until the query layer proves useful.
- Build classification
- Workflow application
- Rough effort
- 4-6 week first release
- Roles involved
- Head of ticketing, Membership manager, Sales manager
- Relevant to
- Professional club, Women's league, Venue & stadium operator, League office
- Systems in play
- Ticketing platforms, CRM, Access control system, Spreadsheets
- Product framing
- Analyse data
Questions we get asked
What do you need from us on day one for this to do anything useful?
Three exports: a sales file from the ticketing platform, a renewal file from the CRM, and a scan log from access control, each with a member or account identifier that matches across the three. That last part is the real test. If a member has a different ID in the ticketing platform than in the CRM, the join fails and the tool is useless until that is fixed. Sorting that mapping out is usually the first week's work, before a single query gets asked.
We already pay for a CRM with reporting built in. Why add another tool?
Your CRM reports well on CRM data. The questions that take an analyst a day are the ones that cross systems, for example matching a renewal list against actual scan-in attendance from access control. This sits alongside your CRM rather than replacing its reporting, and it only exists to answer the cross-system questions that currently require someone to export three files and stitch them together by hand.
Our analyst already builds these pivot tables when we ask her to. Why change that?
It does not replace her judgement, and it should not try to. What it removes is the rebuilding: the same join, the same lookup, done from scratch every time a version of the same question comes up. That frees her time for interpreting what the numbers mean rather than assembling them. Someone still needs to check that a query answered the right question, because a plausible-looking wrong join is easy to produce and hard to spot.
Does it tell us which members are likely to not renew?
No, and that is deliberate. Version one answers the question asked; it does not score, rank or predict. Renewal risk scoring is a genuinely different problem, one that needs a model validated against real outcomes before anyone acts on it, and a wrong prediction that looks authoritative is more dangerous than no prediction at all. If the reporting layer proves useful, a scoring model is a separate decision to make later, not a default that ships with this.
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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