Did you know that 40% of sports streaming service subscribers cancel their subscription after an event or a season? This means that providers of this type of content face the challenge of maintaining the interest and loyalty of their customers, who are increasingly demanding and volatile. How can they do it? The answer is in data analysis.
What is data analysis and how does it apply to sports streaming services?
Data analysis is the process of collecting, processing, and extracting useful information from large amounts of data. This information can help make better decisions, optimize processes, improve products and services, and generate value for the business and customers.
In the case of sports streaming services, data analysis can be applied to different aspects, such as:
User behavior and preferences
You can find out what content they consume, when, how, and from which devices. This allows segmenting customers, personalizing the offer, recommending relevant content, and creating more satisfying experiences.
Performance and quality of service
You can measure speed, resolution, loading time, bandwidth consumption, and other technical indicators that affect the user experience. This allows detecting and solving problems, improving infrastructure, reducing costs, and increasing efficiency.
Impact and return on marketing actions
You can evaluate the effectiveness of advertising campaigns, promotions, discounts, loyalty programs, and other strategies to attract and retain customers. This allows optimizing the budget, increasing reach, generating more conversions, and retaining subscribers.
What benefits does data analysis have for sports streaming services?
Data analysis can offer multiple benefits for sports streaming services:
Increase retention
By better understanding customers and offering them content and experiences tailored to their tastes and needs, they can be encouraged to stay subscribed to the service longer and recommend it to others.
Increase revenue
By personalizing the offer and recommending relevant content, consumption is encouraged, generating more cross-selling and upselling opportunities. Additionally, by optimizing performance and service quality, the dropout rate due to technical issues can be reduced.
Differentiate from competitors
By using data analysis as a competitive advantage, added value can be offered to customers, standing out in an increasingly saturated and competitive market.
Strategies used by sports streaming platforms to leverage user data and analysis
Sports streaming platforms have to compete in an increasingly saturated and demanding market, where users have many options to choose from and can easily switch providers. Therefore, it is essential for these OTT platforms to use user data and analysis to better understand them, offer them quality service, and retain them.
Some strategies used by sports streaming platforms to leverage user data and analysis include:
Create exclusive and personalized content
OTT platforms can use data to know which sports, teams, players, and competitions users prefer, and create content tailored to their tastes and interests. For example, Netflix produces original documentaries about sports figures like Michael Jordan or Pelé, while DAZN offers local and international content on football, basketball, tennis, boxing, and other sports.
Offer a smooth, uninterrupted experience
They can use analysis to measure performance and service quality, and optimize speed, resolution, loading time, and bandwidth consumption. This ensures a smooth, uninterrupted experience for users, who can enjoy their favorite content anytime and from any device. For example, DAZN uses a technology called Per-Title Encoding that optimizes the quality and size of each video based on its visual complexity.
Implement gamification and recommendation programs
Platforms can use data to encourage user participation and loyalty through gamification and recommendation programs. Gamification involves applying playful elements to the service, such as points, badges, levels, or rewards, to motivate users to consume more content or interact with the platform. Recommendation involves suggesting content related or complementary to the user, based on their preferences or those of similar users. For instance, Twitch has a point system called Channel Points that allows users to redeem them for personalized rewards on the channels they follow, while Amazon Prime Video has an algorithm that recommends content based on the user’s viewing history.
How can JUMP DATA DRIVEN help you improve retention with data analysis?
JUMP DATA DRIVEN is a standout player in the subscriber retention industry. With our churn churn prevention model, we use advanced data analysis to identify behavior patterns that may indicate an increase in subscriber’s churn risk.
JUMP Retention is a solution that helps you improve retention and reduce the dropout of your video service customers through predictive data analysis. It allows you to identify users who are about to cancel their subscription and take measures to retain them, segment users based on their churn risk % and churn influencers, offer them personalized content, measure the impact of your actions on retention and abandonment, and optimize your strategies.
If you want to learn more about how JUMP DATA DRIVEN, a business data management platform designed specifically for video service players, can help you improve retention with data analysis, contact us now.
