Renewal rate report by seating section and price tier
Problem
Ahead of the annual pricing decision, someone in the box office pulls three separate exports: season pass sales from the ticketing platform, the renewal outcome list from the CRM, and a scan-in report showing who actually turned up. They paste all three into a workbook, match rows by section and seat block by hand, and build a pivot table nobody else can update. It takes the better part of a week, gets done once a year around the board pack deadline, and by the time it is finished, next season's price tiers have usually already been set from memory and the sales manager's sense of which stands felt empty. The section that has been quietly losing renewals for three seasons running gets the same asking price as the one that sells out.
Product idea
A report that joins the same three exports (sales, renewal outcome, scan-in) into a single view by seating section and price tier, and calculates renewal rate, average tenure and the revenue at risk if the current non-renewers do not come back. It ranks sections from most to least at risk and shows the trend across the last several renewal cycles the data covers, not just this year's snapshot. It does not recommend a price. It shows where the pattern is and leaves the pricing call to the people who own it. Filters exist for section, price tier and pass type, and nothing about the report writes back to the ticketing platform or the CRM.
Who it is for
Sales managers and box office supervisors preparing the pricing case, and the head of ticketing who sponsors it and presents the numbers at the board pack meeting.
Possible first version
A single web report that takes three manually uploaded CSV exports, season pass sales, renewal outcomes and scan-in records, and computes renewal rate, average tenure and revenue at risk by section and price tier, with a trend view across whichever cycles the uploaded data covers. Filtering by section, tier and pass type is included. Version one has no live connection to the ticketing platform, CRM or scan-in system; each refresh is a fresh upload, and automated ingestion is explicitly out of scope until the report has proven useful.
- Build classification
- Micro-tool
- Rough effort
- 2 week prototype
- Roles involved
- Head of ticketing, Sales manager, Box office supervisor
- Relevant to
- Professional club, Women's league, League office, Venue & stadium operator
- Systems in play
- Ticketing and access control platforms, CRM, Spreadsheets
- Product framing
- Analyse data
Questions we get asked
What do we need to upload to see anything useful?
Three exports: season pass sales with section and price tier, the renewal outcome list from the CRM showing who renewed and who did not, and a scan-in report showing attendance against each pass. The report only needs common fields across the three, an account or pass identifier and a section or seat block code, to join them. If the section code is not recorded consistently in all three exports, that gets fixed before the report will mean anything, not after.
We already build this pivot table once a year. Why replace it?
The spreadsheet is not the problem, the once-a-year cadence is. Because it takes days to rebuild by hand, it only gets done under board deadline pressure, and by then the pricing decision is usually already made from memory. This does not claim to build a better pivot table. It claims to make the same view cheap enough to pull monthly, so the pattern is visible while there is still time to act on it.
Who has to keep this running once it exists?
Whoever currently builds the annual pivot, usually a box office supervisor, becomes the person who uploads the three exports on whatever cadence the head of ticketing wants. That is minutes of work per refresh rather than the days the manual version costs, but it is not zero: someone still has to pull the exports and notice if a section code has drifted between systems.
Does it tell us what to charge next season?
No, and it is built not to. It shows which sections and tiers have falling renewal rates, shrinking tenure or the most revenue sitting on non-renewers, and it ranks them so the worst pattern is not buried in a spreadsheet tab nobody opens. The pricing decision, and the judgement about why a section is underperforming, stays with the sales manager and head of ticketing.
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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