Do the Jerseys Matter? | How the NBA is Using Domo to Estimate Viewership
Customer Company Size
Mid-size Company
Region
- America
- Asia
Country
- United States
- China
Product
- Domo
Tech Stack
- Data Science
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Revenue Growth
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Professional Service
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Predictive Quality Analytics
- Real-Time Location System (RTLS)
Services
- Data Science Services
About The Customer
The National Basketball Association (NBA) is a professional sports league in North America composed of 30 teams. It is one of the major professional sports leagues in the United States and Canada and is recognized by the International Basketball Federation (FIBA) as the governing body for professional basketball worldwide. The NBA is an active participant in the sports broadcasting market, with games and related content broadcasted in over 200 countries in more than 40 languages. The organization generates over $2 billion annually from television revenues alone. Each team in the NBA, valued at over $1 billion, plays 82 games in a regular season.
The Challenge
The NBA, a global media company, needs to accurately estimate the viewership for every single game to make key decisions, including which games are broadcast on which networks and which games receive advanced promotion. The NBA’s success requires the organization to accurately estimate the viewership for every single game, using that information to make key decisions, including which games are broadcast on which networks and which games receive advanced promotion. The NBA needed more data, and more speed and agility to put it together, so they could gain insights that would help the NBA succeed in an increasingly competitive media landscape.
The Solution
The NBA partnered with Domo to turn their estimated viewership processes from a manual computation to an automated pipeline that can produce instantaneous assessments of estimated viewers with a much wider set of variables. With Domo, Jonathan’s team dove into a huge variety of datasets. They found that the network an NBA game is shown on is an essential variable for estimating viewership. Additionally, which players are on the court is a key predictor. The model can evaluate teams that are on a winning streak along with matchups that are important for the playoffs. After building the model, Jonathan can watch as it automatically updates with every player, team, and network performance night in and night out.
Operational Impact
Quantitative Benefit
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