How Affiliates Are Using Collaborative Filtering to Boost Conversions

In this episode, Lucas and Luna explore how affiliates are leveraging collaborative filtering algorithms to recommend products based on user behavior patterns. They break down the difference between user-based and item-based filtering, using real-world examples like Amazon's recommendation engine and Spotify's discover weekly. Lucas explains how affiliates can implement collaborative filtering using open-source libraries or platform APIs, citing a case study where a travel affiliate increased conversion rates by 34% after switching from manual recommendations to a collaborative filtering model. The hosts discuss data requirements, cold start challenges, and privacy considerations, offering practical steps for affiliates to test collaborative filtering without a large engineering budget. #CollaborativeFiltering #AffiliateMarketing #ConversionRateOptimization #RecommendationEngine #DataDrivenMarketing #Personalization #AmazonRecommendations #SpotifyDiscoverWeekly #MachineLearning #MarketingTechnology #ETLFunnel #ColdStartProblem #PrivacyCompliance #AffiliateStrategy #PerformanceMarketing #FexingoBusiness #BusinessPodcast #Marketing Keep every episode free: buymeacoffee.com/fexingo