Amazon Amazon's recommendation engine showing two indian whatsapp numbers sections: "Customers who bought this item also bought" and "Customers who read this book also read" The key to Amazon's success and dominance in the e-commerce sector is its ability to recommend different products to customers based on several factors. Of course, shopping and browsing are important, but so are the types of products consumers buy, as well as the styles, colors, and features. So, what impact does the recommendation system have on the overall business? You already know that 35% of all sales are made through the recommendation system. The data collected during shopping is analyzed by an open AI platform known as DSSTNE (pronounced “destiny”), which not only recommends products, but also decides in what order to present them.
Amazon continues to optimize the DSSTNE system to more often promote less popular but more profitable products in its recommendations. Netflix A new feature on Netflix that lets people hit “Play Anything” and watch a movie or TV show based on their preferences. Source: Marker.Medium Consumer behavior data powers Netflix’s recommendation engine, with its sophisticated algorithms suggesting content based on the behavior of millions of viewers. In order to expand its service to new regions, Netflix needed massive amounts of data to power its recommendation system. But the effort has paid off. According to Neil Hunt, Netflix’s former chief product officer, their recommendation system saves them $1 billion a year.
