SportsFirst

Season pass utilisation and churn-risk dashboard

Data & reportingWorkflow application4-6 week first releaseAnalyse dataPrototype-ready

Problem

Scan-in and attendance data lives inside the ticketing platform's own reporting screens, broken down by game rather than by account. Nobody pulls it together with the member list except around premium and hospitality accounts, where a sales manager checks usage before a renewal call. For the rest of the season ticket base, the first sign that a pass is going unused is the renewal call itself, when the member says they stopped coming months ago. By then the account is already a non-renewal, not a save. The membership manager has no standing view of which accounts are drifting towards zero attendance while there is still a season left to do something about it.

Product idea

A dashboard that pulls scan-in events and the season pass account list into one view and buckets every account into an attendance band for the season to date: high, moderate, low or zero. Bands trend game by game, so an account sliding from moderate to low shows up before the pass lapses rather than after. A separate view groups seats by habitual non-use rather than by account, which is a different problem: unsold capacity dressed up as a sold seat. It does not contact anyone. It produces the list; the renewal chase workflow and the account managers make the call.

Who it is for

Membership managers and sales managers deciding who to call before renewal, and the head of ticketing who wants a season-long utilisation view rather than a snapshot built only for premium accounts.

Possible first version

A dashboard built from two uploaded CSV exports: scan-in events per game and the current account and seat list. It computes an attendance band per account, shows the trend across the season, and lets staff filter and export a flagged list by band or by seat. Threshold values for each band are configurable. Version one has no live connection to the ticketing platform or CRM: both extracts are uploaded on a schedule the ticketing team sets, and there is no automated outreach.

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 and access control platforms, CRM, Spreadsheets
Product framing
Analyse data

Questions we get asked

What data do we actually need to have ready before this is any use to us?

A scan-in export at the level of an individual account or ticket, not just a per-game total, and an account and seat list that shares an identifier with that export so the two can be joined. If your ticketing platform cannot produce scan data below the level of the whole gate, that is worth confirming first, because without an account-level join the tool has nothing to bucket.

We already check usage by hand for our premium and hospitality accounts. What does this add?

It extends the same check to the rest of the base, which is where most of the volume and most of the missed early warning sits. Nobody is doing this manually for a few thousand ordinary season passes today, and that is precisely the gap. It does not replace the judgement an account manager applies to a premium relationship; it just gives everyone else the same visibility.

Who has to keep this running once it exists?

Someone owns the scheduled export and re-upload of scan data, and someone, usually the head of ticketing, owns deciding what counts as low usage for each product tier, since a premium seat and a general admission seat do not share a threshold. If nobody reviews the flagged list, the dashboard still runs but produces nothing anyone acts on.

Does this predict who will renew?

No. It reports how much a pass has been used, not a probability that the holder will renew. Whether low attendance actually predicts non-renewal is something your own data will show over a season or two, and it is deliberately left as a band rather than a score, because a confident-looking renewal probability built on one season of data would be more misleading than useful.

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.

Tell us about it

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