A comprehensive search experience in online retail requires not only speed and relevance but also the ability to offer meaningful recommendations that complement a consumer's buying intent. To achieve this, retailers can leverage machine-learning recommender systems that don't require extensive technical expertise. By incorporating recommendations into their search bar, businesses like Spotify, Airbnb, and Amazon have transformed it into a comprehensive browsing, content management, and merchandising experience called searchandising. The key to successful recommendations lies in user preferences, which are used to segment customers into profiles and personalize product suggestions. This can be achieved through various methods, including user-based collaborative filtering, content-based filtering, and combining both approaches. By providing users with relevant and meaningful recommendations, businesses can significantly shape a shopper's buying choices and increase revenue in unexpected ways.