The Clubhouse
How Clubs Turn Data Into Revenue
Most sports clubs are not short of data.
Ticket purchases, attendance records, email engagement and transaction histories all provide a detailed picture of supporter behaviour. Dashboards are built, reports are circulated and trends are discussed internally. On paper, clubs are more informed than ever before.
And yet, for many, revenue does not move in line with that insight.
The issue is not access to data. It is what happens next.
The clubs that see results are not those collecting the most information. They are the ones making small, deliberate decisions based on what that data is actually telling them. When that happens, the connection between insight and revenue becomes much clearer.
When Atlanta Falcons opened the Mercedes-Benz Stadium, they were faced with a familiar problem. Supporters consistently reported that matchday felt expensive, particularly when it came to food and drink. Traditional thinking would suggest that lowering prices would reduce revenue.
The Falcons took a different view.
Using fan feedback and purchasing data, they identified that high prices were suppressing demand. The decision was made to significantly reduce concession prices, introducing items such as $2 hot dogs and $5 beers. Rather than protecting margin, they focused on increasing volume.
The result was immediate. According to reporting by Sports Business Journal, overall fan spend increased, transactions rose and total concession revenue grew despite lower individual prices.
This is a simple example, but an important one.
Data did not lead to a complex model. It led to a clear decision: fans would spend more if the experience felt better value. Revenue followed from that.
Revenue is not only about selling more tickets. It is also about ensuring that tickets sold translate into actual attendance.
At Arsenal FC, one of the challenges identified through ticketing data was the number of unused season tickets. Matches appeared sold out, but seats were often empty on the day. This represented lost atmosphere, lost secondary spend and missed opportunity.
The response was not to increase demand, but to unlock existing supply.
Arsenal expanded their ticket exchange system, making it easier for season ticket holders to resell seats they could not use. By analysing no-show patterns and reducing friction in the resale process, the club was able to increase actual attendance without needing to sell additional inventory.
Coverage from BBC Sport and The Guardian has highlighted how this approach improved utilisation and helped ensure more seats were filled on matchday.
This connects directly to the wider issue explored in how football clubs reduce no-shows on matchday, where the gap between tickets sold and fans in the ground has both operational and commercial consequences.
Again, the principle is straightforward.
Data highlighted a problem. The club removed a barrier. Revenue that would have been lost was recovered.
Understanding behaviour is most valuable when it influences how clubs communicate.
At San Francisco Giants, ticketing data was integrated with CRM systems to build a clearer view of individual supporters. Rather than treating all fans the same, the club was able to segment its audience based on attendance patterns, purchase history and engagement.
This allowed for more targeted communication.
Supporters who had attended previously but not recently could be re-engaged. Frequent attendees could be offered upgrades or additional value. Casual fans could be nudged towards fixtures that matched their behaviour.
According to case studies published by Salesforce, this approach led to increased campaign effectiveness, higher conversion rates and improved repeat attendance.
This reflects a pattern seen across many clubs.
Communication becomes more effective when it reflects behaviour. This is explored further in how football clubs improve ticket conversion rates, where relevance and timing often determine whether intent turns into action.
These are very different clubs, operating in different sports and markets. One is focused on matchday spend, another on attendance, another on communication.
But the pattern is the same in each case.
Data → Insight → Decision → Revenue
None of these changes required complex transformation programmes. They were not dependent on advanced modelling or large-scale restructuring. They were grounded in understanding behaviour and acting on it.
This is where many clubs struggle.
Data is often treated as something to report on rather than something to act on. Insights are noted, but not always translated into changes that affect the supporter experience.
The opportunity for clubs is not to collect more data, but to use the data they already have more effectively.
Understanding when supporters buy, how they behave and where friction exists in the journey allows for incremental improvements that compound over time. These improvements may be small in isolation, but together they can have a meaningful impact on revenue.
This applies across areas such as pricing, communication and experience, all of which are influenced by behaviour. It also reinforces the importance of first-party fan data, which provides the level of visibility required to make these decisions with confidence.
Data does not generate revenue on its own.
It provides the signal, but the outcome depends on what clubs choose to do with it. The most effective organisations are not those with the most sophisticated systems, but those that act consistently on what their data is telling them.
Because in the end, the value of data is not in what it shows.
It is in what it changes.