Beyond The Game: How the AFL is Scoring Big with AI

TL;DR

he article explores how the Australian Football League (AFL) uses AI and analytics to boost fan engagement and make data-driven decisions. These advanced tools have transformed the AFL into a sophisticated business model. The strategies aren’t just for large organizations; accessible data tools enable small and medium-sized businesses to achieve similar results for growth and customer retention.

Introduction

For the uninitiated, the AFL might seem like just a game, where success is measured by goals, tackles, and soaring marks. But look a bit closer, and you’ll see a complex business ecosystem that demands strategic planning, fan engagement, and relentless marketing to thrive. With a whopping 1.2 million members and growing, the AFL is embracing AI and data analytics to revolutionize how clubs understand and interact with their fans, optimize marketing strategies, and even decide the ideal location for new community sports grounds.

In this article, we delve into the fascinating journey of the AFL’s digital transformation—a narrative that encompasses not just machine learning models and Tableau dashboards, but also serves as a powerful case study for any business looking to harness the full potential of AI and data analytics.

The Need for Data-Driven Decisions

In today’s competitive landscape, intuition and gut feeling are no longer sufficient for making business decisions. This rings especially true for sports leagues like the AFL, where the stakes extend far beyond the field. For these complex organizations, fan engagement, ticket sales, and community involvement are crucial metrics that define success. Here, data becomes the linchpin, enabling clubs to make informed decisions that resonate with their diverse audience.

Three years—that’s the time it took for the AFL’s data and analytics team, led by Elisa Koch and Penny Privett, to build their comprehensive “Fan Data Service.” This suite of tools is a game-changer for the 18 clubs that form the league. The service provides a rich set of data products, ranging from self-serve reporting through Tableau to a custom-built machine learning model designed for fan engagement.

The Role of Reports

Before the advent of the Fan Data Service, clubs had to rely on SAP Crystal Reports as their “single source of truth.” These reports, often cluttered with tables and numbers, offered limited insights. The introduction of Tableau dashboards revolutionized the data landscape, delivering visual and more advanced analytics. Now, stakeholders from club CEOs to frontline membership teams receive actionable insights right in their inbox, every morning at 9:00.

The Machine Learning Model

Then there’s AI, AFL’s machine learning model serves a specific yet vital purpose: predicting the likelihood of members renewing their memberships. Before implementing this model, the clubs had a rich database of customer interactions but lacked insights into one crucial aspect—whether individual members would renew their subscriptions or not. The machine learning model rectified this by generating a “propensity score” for each of the 1.2 million members, allowing clubs to tailor their marketing strategies accordingly.

Utilizing machine learning, the AFL can segment its fan base into hundreds of different categories. Clubs can now launch highly targeted campaigns, focusing resources on demographics and locations most likely to deliver high returns in terms of membership growth and retention. The model uses various factors, from the types of games fans attend to their purchasing behaviors, to generate actionable insights.

Mapping for Precision

While traditional methods mapped fans to postcodes, the AFL took it a step further. Leveraging data from the Australian Bureau of Statistics (ABS), the Fan Data Service now maps fans to precise statistical areas. This granularity allows clubs to understand better the socio-economic and cultural fabric of their fanbase, enabling hyper-targeted campaigns and initiatives.

Incorporating ABS’s statistical areas has made the data incredibly rich and nuanced. Clubs can now evaluate not just the number of members in a given area, but also additional metrics like median income, age, and cultural diversity. Such insights are vital for tailoring marketing strategies and community engagement initiatives.

Community Engagement and Grassroots Development

It’s not all about the big leagues. The AFL’s data strategy also focuses on grassroots development, evident in their commitment to invest 10 percent of their revenue in growing community football. The data analytics tools help the game development team identify gaps in local areas, helping them decide where to build new sports facilities or initiate community programs.

By combining member metrics and census metrics, the AFL has a comprehensive dashboard that helps answer crucial questions like, “Where should the next community sports ground be built?” or “How can we align our resources with local governments to benefit communities?” This focus ensures that the AFL’s data strategy aligns well with broader societal goals.

Conclusion

The AFL’s foray into data analytics and AI is a playbook for any business looking to excel in today’s data-driven world. It offers not just a story of how machine learning and Tableau dashboards can revamp fan engagement but a broader narrative of organizational transformation.

By investing in a comprehensive data strategy, the AFL has transformed from a sports league into a sophisticated data-driven enterprise, capable of making informed decisions that benefit both the league and its broader community. For businesses looking to level up, the AFL’s journey serves as an inspiring case study in the transformative power of AI and data analytics.

Scaling Down the Insights: Not Just for the Big Players

You might think that the sophisticated data operations of the Australian Football League (AFL) are only feasible for large organizations with massive fan bases and enormous turnovers. However, the fundamental strategies they use can be scaled down and applied even to small and medium-sized enterprises (SMEs).

Analytics for Small Businesses

Today’s data analysis and visualization tools are increasingly accessible and budget-friendly. This means that SMEs can leverage these resources to gain insights into customer interactions, just like the AFL does on a larger scale. These insights can inform how to tailor your products or services to better meet the specific needs of your current customer base.

Predictive Power for Growth

But it doesn’t stop at understanding your current customers. Predictive analytics can offer foresight into where your next wave of customers will come from. You don’t need a team of data scientists to implement a useful predictive model. Sometimes, a focused strategy and the right set of tools are all you need to create a more meaningful and profitable relationship with your audience.

By embracing even a scaled-down version of what the AFL has implemented, businesses of all sizes can create more targeted and effective strategies, leading to improved customer retention and growth.