Artificial Intelligence and Sport Sponsorship – a marriage made in heaven?

In a recent article that I co-authored with Nader Chmait, we reflected on Artificial Intelligence and Machine Learning in sport research. We argued that in the last decade, Artificial Intelligence (AI) has transformed the way in which we consume and analyse sport. The role of AI in improving decision-making and forecasting in sport, amongst many other advantages, is rapidly expanding and gaining more attention in both the academic sector and the sport industry. It can be argued that one specific area of business in sport is yet to fully realise the potential of AI - sports sponsorship. In this posting, I will explore how AI will dramatically impact sport sponsorship and what this means for the industry.

We can of course look at sponsorship from two broad perspectives – that of the sponsor and that of the sponsee. For those organisations investing part of their marketing budget in sponsorship, AI can help them identify the right sponsorship opportunities. But for (sport) organisations seeking sponsorship, they also can use AI to target potential benefactors. By analysing data such as fan demographics, interests, and spending habits, AI can help companies find the most suitable sport properties to sponsor or indeed, find the right sponsors that fit the sport best. This can save sponsors and sponsees time and money by ensuring that their efforts and investments are focused on the most relevant audiences. In addition, AI can help identify emerging sports and young and upcoming athletes, providing companies with new sponsorship opportunities that they may have missed otherwise.

Beyond the identification of (new) opportunities, AI can help measure the impact of sponsorship investments. Traditionally, sponsorship ROI has been difficult to measure accurately. In the early days of ROI measurement of sponsorship, real humans… were employed to search for, track, and count the number of brand or logo exposures that brands (or logos) would attract on the back of the sponsee’s sporting results. Without automation and limited ability to identify and track all media outlets that would report on the sport, such ROI measurement was crude at best. However, through the development of a bespoke algorithm, AI can help companies track various metrics such as brand exposure, engagement levels, and conversion rates.

An algorithm is a set of rules or instructions that a computer program follows to solve a problem or perform a task. In the context of AI-informed data tracking, algorithms can be used to analyse and process large amounts of data, identify patterns and trends, and make predictions based on that data. To design an algorithm for tracking brand exposure of a sport sponsorship, you would need to define the variables to be tracked, identify the source data that will deliver the variables (for example the live broadcast of the sport event), and then develop an algorithm that will need to be able to recognise the brand logo or name, analyse the context of the mention (e.g., was it positive or negative?), and calculate the duration of the mention. For example, an algorithm could be designed to analyse user behaviour on a website, tracking which pages they visit, how long they stay on each page, and what actions they take (such as clicking on links or filling out forms). By analysing this data, the algorithm could identify patterns in user behaviour and make predictions about what actions are most likely to lead to conversions (such as making a purchase or signing up for a newsletter). By automating the process of data analysis and prediction, AI algorithms can provide insights that would be difficult or impossible for humans to identify on their own, ultimately leading to better-informed decisions and improved business outcomes.

AI can also help sponsors and sponsees create personalised sponsorship experiences for fans, in the process becoming more familiar with the fan’s consumer preferences. That in turn can help in sponsor sales activations by analysing data such as social media activity and purchase history. When designing activations and gamifications, AI can help companies create targeted sponsorship activations that resonate with individual fans. For example, a sports apparel company could use AI to analyse a fan's social media activity and determine which athlete they follow the most. The company could then create a personalised sponsorship experience for that fan, such as a meet-and-greet with the athlete or a customised jersey with the fan's name on it. By creating personalised experiences, companies can increase fan engagement and loyalty, ultimately leading to increased sales and revenue.

AI can also help both sponsors and sponsees protect their brand reputation. In the era of ubiquitous social media, a single negative incident involving a sponsored athlete or team can quickly spread and damage the reputation of the sport (organisation) and as such, of those (sponsors) that associate with the sport. AI can help the sport organisation (but also the sponsor) monitor social media for potential issues, such as controversial statements or bad behaviour from sponsored athletes. By identifying potential issues early, action can be taken (or prepared for) to mitigate the damage and protect their brand reputation.

In order to stay ahead of the competition AI can help companies analyse competitor activity and identify gaps in the market. With so many companies vying for sponsorship opportunities, it can be difficult to stand out from the crowd and by using AI to keep a close eye on the competition, companies can secure the best sponsorship opportunities and maintain a competitive edge.

Now let’s put this into practice with a fictional example of AI being used in a sport sponsorship deal. It is the partnership between professional football club Champion FC and a gaming company called Virtuous Reality (VR). Recently, VR announced that they would be sponsoring Champion FC’s official esports team, Champion Esports. As part of the partnership, VR will be using their AI-powered platform to analyse fan engagement data from Champion Esports' social media channels, including Twitter, Instagram, and TikTok. The platform will use machine learning algorithms to identify which types of content are resonating with fans, as well as which fans are most engaged with the team. This data is then used to create targeted sponsorship activations that are tailored to the interests and preferences of individual fans. For example, VR has used the data to create personalised social media posts featuring Champion Esports players, which are then targeted to fans who have shown a particular interest in those players. In addition, VR is also using their AI platform to measure the effectiveness of their sponsorship investment, and if the investment leads to direct or indirect increases in revenue. The platform tracks metrics such as social media engagement levels, follower growth, and sentiment analysis, allowing VR to monitor the impact of their sponsorship activations and make adjustments accordingly.

This fictional partnership between Champion FC and VR shows how AI can be used to create personalised sponsorship experiences and measure the impact of sponsorship investments. By leveraging the power of AI, VR will be able to identify and engage with Champion Esports fans in a more meaningful way, ultimately driving VR brand awareness and loyalty.

While AI will certainly have a significant impact on the sport sponsorship industry, it is unlikely that it will completely eliminate any specific jobs (in the short term). Instead, AI will likely automate certain tasks and provide more data-driven insights, allowing individuals in the industry to focus on higher-level strategic planning and relationship-building. As explained earlier, AI can automate the process of analysing data and identifying potential sponsorship opportunities, but it still requires human input to make the final decision on which sponsorships to pursue. Similarly, AI can help track and analyse the effectiveness of sponsorship investments, but it still requires individuals to interpret the data and make strategic decisions based on the results. It will require professionals in sport to learn about the digital transformation of industries, and then adapt and evolve in their own professional development in order to fully leverage the potential of AI.