Media & EntertainmentMachine LearningRecommendation & Personalization
How Schibsted's AI model helped boost subscription sales
Schibsted
Schibsted developed a machine learning model to serve personalized subscription-sales recommendations to anonymous, non-logged-in front-page readers, using first-party demographic data (age/gender predictions from advertising) and sales insights to generate real-time, on-demand recommendations rather than batch processing. Of 158 candidate data points tested, around a dozen were retained as model features. A/B tests showed a 75 percent increase in subscription sales from front-page articles compared to previous models. The system is integrated with Schibsted's Curate content recommendation platform, using Flyte for orchestration and AWS DJL for model inferencing, and the team is transitioning to Tecton as a managed feature store.
