Two different types of product recommendations were included in the home pages of the different stores dedicated to home shopping. For the “Picked for you” block, the “ Personalized recommendations by sales ” algorithm was selected, which suggests products based on the experiences of users with a recent purchase history similar to that of the browsing user. For the “Best sellers” block, the “ Trending products ” algorithm was selected as the algorithm, which displays the list of the most popular products in a catalog based on the number of views and clicks.
2. RECOMMENDED PRODUCTS ON THE PRODUCT PAGE
In the product sheet, with the aim of increasing the value of the user's cart by egypt cell phone number list stimulating the addition of other products, a product recommendation block has been inserted that suggests, through the " Frequently bought together " algorithm, products that are often purchased together with the product to which the current page refers.
Customizing the browsing experience - Food
Product Recommendation - Food Industry
3. RECOMMENDED PRODUCTS ON THE CATEGORY PAGE
In the Eataly.it store, on the other hand, we chose to include product recommendations in the main pages of categories and subcategories. In this case, the algorithm identified was “ Personalized Trending Recommendation ”, which shows a mix between the most popular products and products based on the user’s recent browsing history.
Thanks to Blendee's contribution, Eataly was able to leverage information about users' purchasing behavior to offer products that match their tastes and needs. In all online stores, personalization marketing strategies were implemented that allowed users to be shown potentially more interesting products based on their segment.
Article taken from the ebook Eataly Case Study. Personalization and omnichannel customer experience: the success story of the food sector.