Sports and gaming companies are forging ahead with the use of data science as a competitive differentiator. According to an industry report, the global AI in media and entertainment market size was valued at $10.87 billion in 2021 and is estimated to grow 26.9% annually until 2030. 

Companies are continually innovating on ways to use data science for creative purposes. For example, Electronic Arts (EA) uses AI to create more realistic and challenging non-player characters in games such as FIFA and Battlefield, while Activision Blizzard uses AI to personalize the gaming experience for individual players in games such as Call of Duty and World of Warcraft. EA CEO Andrew Wilson recently said he believes the gaming industry is “probably going to be one of the greatest beneficiaries of AI broadly.” 

Behind the scenes, sports and gaming companies are also employing data science to develop in-depth fan insights so they can identify the most effective marketing strategies and make better business decisions. A broader, deeper understanding of fans through a fan 360 view can help them develop better ways to reduce customer churn and maximize customer lifetime value.

To create a fan 360 view, companies need to securely collect and analyze a wide array of data that comes from various sources. But there are a number of challenges to achieving fan 360. 

Legacy technology: Many companies still function with a fragmented IT landscape that includes on-premises and cloud-based legacy systems. Outdated technology makes it difficult to collect and share data on fans to achieve insights. Data often has to be manually fed into different systems, and it’s difficult to gain a single view of customers. 

Siloed data: Achieving a 360-degree view of fans relies on unifying data from various sources including websites, CRM systems, online streaming, ads, social media, and more. But data is often stuck in silos in different systems and departments. Copying that data often causes duplication and data quality issues, making it difficult to use up-to-date, accurate, and comprehensive data to make strategic decisions. 

Privacy and regulatory concerns: Consumers are demanding greater privacy features and control over their own data, and rewarding companies that adhere to strict data governance standards. Meanwhile, lawmakers are imposing tougher regulations on companies that handle consumer data. Regulations such as the EU’s GDPR and California’s Consumer Privacy Act must now be factored into sports and gaming organizations’ data strategy. 

Three data science best practices for fan 360

Here are three best practices that companies should follow to achieve an effective fan 360 view, deliver insights into ways to improve the fan experience, and optimize customer revenue streams. (A list of all eleven best practices can be found in our Fan 360 Best Practices for Data Science ebook.) 

1. Leverage the cloud: To implement and scale data science projects quickly and cost-effectively, it is vital to leverage the cloud. When looking for a cloud provider, choose one that is cloud agnostic, allowing your organization to break down data silos and seamlessly connect data projects across public clouds in a flexible, easily scalable way. This not only democratizes access to data but allows data science projects that are forward-looking.

2. Use a data clean room for secure, governed, real-time data analysis: A data clean room (DCR) provides a private, secure, and governed environment to bring data together in a privacy-preserving way, enabling two different organizations, such as a publisher and an advertiser, to conduct joint analyses without revealing the underlying data itself. It also allows organizations to define what data can be queried, what types of questions can be asked of the data, and who can access and query it. 

DCRs enable you to:

• Share first-party data in a secure infrastructure with audit and controls in place to match data between separate organizations, all live and up-to-date.

• Join first-party data without middlemen taking a cut of the campaign and reducing transparency.

• Perform measurement and attribution directly in the environment with technology providers to enable compliance with privacy requirements, while understanding the outcome of marketing activations (for example, return on ad spend).

3. Use predictive analytics: Today, the most sophisticated data platforms are designed to help companies shift from descriptive to predictive analytics. Instead of viewing a dashboard that tells a CEO what happened in the past, these tools predict and analyze future outcomes and conditions. This may dramatically change how companies operate and anticipate disruptions. As a result, it is incumbent upon technology decision-makers to drill into a potential partner’s predictive capabilities. Ultimately, media and entertainment organizations should aspire to ascend the analytics maturity curve to where decisions can be optimized over time. 

Speaking of maturity curves, machine learning is still in its earliest stages of development. Because of its nascency, companies and industries are adopting this tech at varying levels and speeds, leading to growing fragmentation. Even though the market promises to sort itself out over time, it’s important to ensure that various tools and products feature interoperability at their core.

To learn more, check out our new ebook, Fan 360 Best Practices for Data Science