Boosting Revenue with Smart Recommendations
A UK based sports streaming platform offering live and on-demand sports content faced challenges in increasing user engagement. Viewers primarily watched content related to their favorite teams or sports, limiting overall platform interaction and retention rates.
Business Objective
The company aimed to implement an AI-powered recommendation engine that would personalize content suggestions, encourage content discovery, and enhance user engagement to maximize ad revenue and subscription reten
How We Accomplished It
Codino’s AI specialists designed a personalized recommendation system that analyzed user interactions, viewing preferences, and behavioral data. The system leveraged collaborative filtering and content-based recommendation techniques to identify relevant content for each user. Real-time analytics tracked watch history and dynamically adjusted recommendations to reflect changing interests.
A scalable data pipeline was implemented to process millions of interactions daily, ensuring seamless integration with the platform’s existing infrastructure. The recommendation engine was optimized for low-latency predictions, providing users with instant content suggestions based on ongoing interactions.
The Results
25% increase in user engagement, with viewers exploring a broader range of content.
Higher subscription retention, as users spent more time on the platform.
Increased monetization, leading to improved revenue through extended viewing sessions and targeted advertisements.
Ready to boost your business?
Unlock the potential of AI—streamline logistics with optimized supply chains, detect fraud in healthcare insurance, or use advanced social listening to strengthen your portfolio companies.
A UK based sports streaming platform offering live and on-demand sports content faced challenges in increasing user engagement. Viewers primarily watched content related to their favorite teams or sports, limiting overall platform interaction and retention rates.
Business Objective
The company aimed to implement an AI-powered recommendation engine that would personalize content suggestions, encourage content discovery, and enhance user engagement to maximize ad revenue and subscription reten
How We Accomplished It
Codino’s AI specialists designed a personalized recommendation system that analyzed user interactions, viewing preferences, and behavioral data. The system leveraged collaborative filtering and content-based recommendation techniques to identify relevant content for each user. Real-time analytics tracked watch history and dynamically adjusted recommendations to reflect changing interests.
A scalable data pipeline was implemented to process millions of interactions daily, ensuring seamless integration with the platform’s existing infrastructure. The recommendation engine was optimized for low-latency predictions, providing users with instant content suggestions based on ongoing interactions.
The Results
25% increase in user engagement, with viewers exploring a broader range of content.
Higher subscription retention, as users spent more time on the platform.
Increased monetization, leading to improved revenue through extended viewing sessions and targeted advertisements.
Ready to boost your business?
Unlock the potential of AI—streamline logistics with optimized supply chains, detect fraud in healthcare insurance, or use advanced social listening to strengthen your portfolio companies.
Boosting Revenue with Smart Recommendations
A UK based sports streaming platform offering live and on-demand sports content faced challenges in increasing user engagement. Viewers primarily watched content related to their favorite teams or sports, limiting overall platform interaction and retention rates.
Business Objective
The company aimed to implement an AI-powered recommendation engine that would personalize content suggestions, encourage content discovery, and enhance user engagement to maximize ad revenue and subscription reten
How We Accomplished It
Codino’s AI specialists designed a personalized recommendation system that analyzed user interactions, viewing preferences, and behavioral data. The system leveraged collaborative filtering and content-based recommendation techniques to identify relevant content for each user. Real-time analytics tracked watch history and dynamically adjusted recommendations to reflect changing interests.
A scalable data pipeline was implemented to process millions of interactions daily, ensuring seamless integration with the platform’s existing infrastructure. The recommendation engine was optimized for low-latency predictions, providing users with instant content suggestions based on ongoing interactions.
The Results
25% increase in user engagement, with viewers exploring a broader range of content.
Higher subscription retention, as users spent more time on the platform.
Increased monetization, leading to improved revenue through extended viewing sessions and targeted advertisements.
Ready to boost your business?
Unlock the potential of AI—streamline logistics with optimized supply chains, detect fraud in healthcare insurance, or use advanced social listening to strengthen your portfolio companies.
Boosting Revenue with Smart Recommendations