How DAZN Scaled a Small Data Team using Machine Learning

Until recently, the sports entertainment industry was dominated by cable or satellite TV systems and companies; if a customer wanted to watch a particular sporting event, they had little (or no) choice in how to do so. Now that consumers are breaking free from traditional TV, they are increasingly turning to specialized services streaming exactly the content they’re looking for. And while they are willing to pay for these services, it means that entertainment companies are held to increasingly higher standards when it comes to quality and offerings.

In other words, because customers can turn elsewhere, entertainment companies have had to up their game (so to speak). Today, that means bringing innovation by way of predictive analytics and machine learning to optimize every aspect of the business, from marketing to customer service to product offerings. To do this efficiently, they must also bring this innovation at scale, hiring fewer people to do more such that insights grow exponentially along with the amount of data being collected. But how?

The Challenge

DAZN is a subscription service owned by Perform Group dedicated to live and on-demand streaming of worldwide sporting events. It offers access to more than 8,000 sporting events a year across a wide range of devices to customers in Austria, Germany, Japan, Switzerland, and Canada, with more markets coming soon. In an effort to continue to grow their business in existing and new markets, DAZN wanted a fast, low-maintenance way to enable their small data team to run predictive analytics and machine learning projects at scale.

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In an effort to continue to grow their business in existing and new markets, DAZN wanted a fast, low-maintenance way to enable their small data team to run predictive analytics and machine learning projects at scale.

In addition, they wanted to find a way to allow data analysts who were not necessarily technical or experienced in machine learning to be able to contribute in meaningful ways to impactful data projects. Ultimately, they wanted to support an underlying data culture with advanced analytics and machine learning at the heart of the business

The Solution: Dataiku and AWS

DAZN knew that in order to accomplish their goals quickly, they would need technologies that were simple and in the cloud. They turned to Amazon Web Services (AWS) and Dataiku in combination for their simplicity in setup, connection, integration, and usability, and they got up and running in under one hour.

With AWS and Dataiku, the small data team built and now manages more than 30 models in parallel, all without needing to do any coding so that the processes are completely accessible to non-technical team members. They use these models as the basis for a variety of critical processes throughout all areas of the business, namely:

• Content attribution to determine what fixtures are driving sales, enabling contextual information on key fixtures in each market.

Go deeper: Read More About Marketing Attribution

• Advanced customer segmentation to identify user behaviors, particularly regarding content and devices on which customers use the product.

• Propensity modeling to identify customers that are likely to churn, enabling improved customer targeting for retention activities.

Get the Guide: Address Churn with Predictive Analytics

• Survival analysis to understand customer stickiness, enabling calculation of expected revenues to understand customer return on investment.
• Natural language processing on social networks for market research.

Incorporating Twitter (+ other social data) into a larger data strategy

The Impact

AWS and Dataiku have noticeably shifted the data culture at DAZN and have brought innovations in advanced analytics and machine learning into the spotlight throughout the company. Thanks to Dataiku’s ease, simplicity, and huge efficiency gains, DAZN has hired two data analysts who have already gotten up to speed and are doing as much work as five analysts in the pre-Dataiku team.

Overall, the biggest impact has been empowering a non-technical team to create more models than ever before and get them into the production environment quickly to bring real ROI to the business.

DAZN plans to continue to grow the team to three data scientists and 6-10 analysts to exponentially increase the number of machine learning models in production.

Each DAZN data team member is now 2.5x more efficient in putting models into production.”