We are reaching an inflection point of no return and we will have to adapt very quickly to this new technological environment, especially in sports, which move much more slowly than other industries when it comes to innovation. Is.
History has been very clear, we have gone through three revolutions in the past: steam, electricity, computing and now comes artificial intelligence. AI can help companies make informed, data-driven decisions about where and how to invest in sports sponsorship, which can improve efficiency and return on investment. And that is, in the 21st century we continue to see sponsorship contracts based primarily on branding and with very few assets that measure performance indicators such as sales growth, user acquisition and very little reach to users through data. going to transfer.
There remains widespread confusion of CRM (customer relationship management), which in the pursuit of commercialization has led marketers to misunderstand that the relationship with the client is business intelligence or worse, to believe that it could be fan/capture behavior. Of course, there are very specific cases where a CRM can capture certain data, such as frequency of access/consumption of products and services, and it is also true that they can help with personalization, but this is far from being the basis of a revolution. away that we are living.
More than sporting institutions, it is the brands that sponsor the sport that have to emphasize: they must remember that a sponsorship needs to be activated, and that the total cost of the sponsorship comes from the sum of the payments to the sporting property. rights, as well as the cost of activation (ATL, BTL, digital, etc.). A sponsorship that is not active, only for publicity, is doomed to return on investment (return of media value). Therefore, sports sponsorships of the future will focus on offering comprehensive business solutions that help drive KPIs (key indicators) and OKRs (key objectives) in both branding and (mainly) performance.
Due to the above, it becomes important that both the sponsor and the sponsored have the bases covered to be able to reach effective exploitation of the sponsorship relationship and that is aligned with the business objectives. With so much data, the return on investment (ROI) will increasingly be questioned or the data used to analyze the convenience of celebrating sponsorship with X or Y entity.
In lectures that I have had the opportunity to attend on this topic, I have repeatedly mentioned that the “dataification” of all information doubles every 1.2 years and continues to accelerate. An infinite number of devices are connected to the network and more and more data is being produced and with such magnitude it is necessary to know what to do with it.
With the announcement of Google and its Bard platform, not to be outdone by ChatGPT (which is partnered with Microsoft), what is coming is a new world of simply and simply information, derived content, delegated tasks and Where very quickly many actors will look and feel overwhelmed. Very few companies are prepared for this. For example, read about universities in the United States banning the use of AI tools because they don’t know how to handle the situation.
So we need to start thinking about what we need in the future, or what sponsor should start looking at: One of the main ways AI is being used in sponsorship is through hearsay segmentation. AI can analyze vast amounts of data about sports fans, including demographic information, interests and purchasing behavior, allowing companies to segment their target audiences and personalize their strategies.
The new fan data DNA will include social attributes, social influence, daily events, data usage, volatility, call network, checkout behavior, demographics, text usage, website clicks, app usage, complaint history, offer history, for starters. And where do you get this data from? From stadiums, to websites, mobile apps, social networks, email, gaming, streaming and e-commerce, of course. (We can discuss the ethical and data privacy implications later.)
Furthermore, AI can be used to analyze sponsored athletes’ performance data, which will also lead the sponsor to evaluate their investment decision. AI can use data on sports performance, popularity on social media, and other relevant indicators to determine which athletes are most effective at promoting brands and generating interest among the public.
Also, AI can be used to improve the effectiveness of activation strategies. An example of this would be AI being able to analyze audience, attendance, ratings and performance data to determine which sporting events and platforms are most effective at reaching target audiences and maximizing sponsorship impact.
Let’s not stop there, AI can be used to optimize sponsorship budgets. AI can analyze cost-benefit data from different sports sponsorships, compare them, and determine the best way to spend the budget. Or, analyze results from previous activations and suggest a better time to activate.
A new world is coming that we are still only imagining. Who will be the first to raise his hand?
