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The Super Bowl is one of the largest sports maintenance events in the world that generates more than a hundred million viewers and sales of billions.
For NFL teams and sports entertainment in general, however, there is a long way to the championship, since Franchise companies aim to build a brand, increase the fandom and maximize the income.
One of the ways to achieve this is AI.
The technology is not a stranger in the world of sports entertainment. Before the modern era of the generative AI – as early as 2017 – large providers such as IBM were already discussing how Ki would do it Disturb sports entertainment networks. The NFL itself uses AI to improve the safety of players with A Digital athlete System that was developed in cooperation with AWS. The NFL also uses AWS to create Gengen AI-powered apps using the Amazon Meamedb database.
For individual teams, both in the NFL and in the entire sports entertainment landscape, there are other options for the implementation of Gen AI. Such an option that is started today comes from RaiseA technology provider under the direction of Al Guido, who also president of the San Francisco 49ers NFL football team.
The new data and the KI platform of the company Cloud (Elevate Performance and Insights Cloud) combine consumer knowledge, ticket management and real estate analyzes to support sports and entertainment organizations that are better committed to fans. The platform helps companies with targeted commitment to better understand potential customer personas. This information helps to determine stadium seat options, ticket prices and fan bond. The platform has already been used by more than 25 organizations, including Tennessee Titans.
Elevate has been in operation since 2018, but now with the Entewaling of gene aiThe company is able to do much more with data.
“The establishment of EPIC has reinforced a fundamental truth that we have seen and validated in the company since – data is only as powerful as the decisions it enables it,” Guido, chairman and CEO of Elevat, told Venturebeat. “In sport, the challenge not only captures this data, but also uses real, implementable intelligence that improve fans’ commitment, sales strategies and operational efficiency.”
The data challenges when building an AI-first engagement system
Elevate already has data for around 220 million people in his system. The company collects provider data on its customer work and relationships. This includes data on fans behavior, ticket sales, sponsorship and other information on real estate. Elevate also licenses and buy third -party data records to further enrich the user profiles.
Guido found that many organizations collect so much infinite data, but they have difficulty combining and using them. Epic was developed to close this gap.
Fully benefit from Modern gen aiData should be available in a vector database format. Cio Jim Caruso explained Venturebeat that his company had undergone an intensive process in order not only to vectorize data, but also to ensure that it is the right data to inform business decisions.
There is no lack of database providers and technologies that claim to make the vectorizing data easy. In reality, Caruso emphasized that the vectorization process is not as easy as switching on a switch. As part of the construction of EPOs, they rated all data and how they could work together to ensure the best findings. The actual vectorization process included testing various approaches and processing pipelines to find the right balance between accuracy and performance.
Elevate Amazon Sagemaker is currently using his vectorization work.
How to help anthopical Claude, XGBOOST and Amazon basic rock to drive AI knowledge for Epic
Caruso explained that the epic system offers a wide range of AI-powered applications, from price cards to the development of consumers. Elevate uses a combination of different technologies to create these tools.
At its core is the anthropic Claude Haiku 3.5 Large language model (LLM), which was finely coordinated with the data from Elevate. Claude provides the interface to ask questions and obtain knowledge based on various personas.
For example, a persona could be an event location operator who wants to determine how the best way to configure Premium seat seats in an event location. This operator must understand who is interested in these seats and how they should be marketed to different groups.
Elevate went beyond the identification of broad demographic segments such as suburban millennials. Instead, they have created a number of different people with a number of attributes, including finances, buying preferences, entertainment decisions and commitment to social networks. The main goal is to provide very concrete, detailed personas with which companies can make certain business decisions.
The system also uses the XGBOOST library (Extreme Gradient Boosting) Open Source Machine Learning (ML) via the Amazon Sagemaker to help especially with numerical data for ticket prices. XGBOOST is an supervised ML algorithm that uses decision -making trees to make predictions. Caruso explained that his team converted historical data and real -time data into 55 different functions. This includes event details, existing details and current sales information. All were then fed into the XGBOOST algorithm.
The competitive landscape for AI about sports entertainment
Guido said that the initial reaction to the epic in the NFL and beyond was positive.
Many properties face similar challenges: fragmented data sources, the development of fan expectations and the need for more intelligent, more efficient use of revenue. Guido also clearly recognizes that the competitive landscape is increasing for this type of technology. There is traditional customer relationship management (CRM) and analysis providers such as Salesforce, but in his opinion, they often lack the industry-specific intelligence that brings EPIC for sport and live entertainment.
“What distinguishes epic is his deep integration into the realities of sport,” said Guido.
How do AI-driven knowledge drive the real effects on the Tennessee Titans
One of the early users of Epic is the Tennessee Titans of the NFL. The team works with Elevate because it develops a new stadium of 2.1 billion US dollars, which is to be opened in 2027.
As part of the commitment, Elevate contributed to leading the sponsoring sale for the new stadium. The company developed a strategic roadmap, a category -specific market launch strategy and determined the annual sales goals through the start of the stadium.
With Epic, the titans were able to create detailed personas for fans to inform targeted marketing strategies, from messaging to premium seat and hospitality. Although the new stadium is still a few years away from the opening, the titans have already exceeded the sales goals for premium seats, with data and AI-driven findings as a foundation.
It’s not just for the NFL; College light athletics also benefit from AI-operated knowledge
There is a lot of money in the NFL, but at other levels of sports entertainment, including universities, there are also a lot of opportunities (as well as many challenges).
“The athletes sports departments are deeply subjected to digital transformations, and data are the focus,” Tom Moreland, Chief Commercial Officer of the University of Illinois Athletics, told Venturebeat. “One of the largest lessons we have learned is that technology alone is not the solution – the strategy comes first.”
Moreland explained that his school had prioritized the prioritization of recording, interpreting and using data to improve the experience of his trainers, athletes and fans.
So far, the epic platform has provided the Athletics of the University of Illinois the crucial data-controlled insights that are required to improve football and men basketball ticketing and an annual donation model. Moreland said that the epic analysis provided intelligence that made it possible for the school to go beyond the assumptions and to make strategic, well -founded decisions. Ultimately, Epic authorized his department to create a more committed and sustainable model for loyal fans and donors.
“Sporty departments that take the time to invest in data quality, structure and application will really benefit from every new technology,” said Moreland.
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