Appier supports EF Shop to adopt AI in its digital transformation trilogy

EF Shop’s latest products “Anti-UV environmentally friendly antibacterial series”, are eco-friendly garments made from recycled PET bottles, with excellent sun protection and Japanese antibacterial and deodorant functions. Aside from effectively blocking 90% of UV rays, quickly wicking sweat and reducing odors, the products can also reduce energy consumption and carbon emissions by 70%-85%. EF Shop also recommended its Anti-UV products based on the characteristics of other new products, such as recommending this new product to users who have purchased sweat-wicking, quick-drying, breathable, and antibacterial materials. At the same time, EF Shop compared consumers who have recently purchased similar styles and recommends additional items on the shopping cart or checkout page to increase the chance of cross-selling.

The fashion e-commerce market is extremely competitive and fast-paced. To stand out, product recommendation has become one of the essential tools for many e-commerce. Common recommendation engines are mostly popular product recommendations based on sales, or similar product recommendations based on product categories. However, in order to meet the diverse personalization needs of customers, e-commerce brands must provide a more diverse product recommendation mechanism in order to seize the ever-changing sales opportunities.

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According to the report from McKinsey & Co, 35% of what customers purchased from Amazon and 75% of what they watched on Netflix are based on the algorithms of product recommendation. If brands can master the essentials of recommendation, they will have a greater chance to win over customers. The advanced recommendation algorithm may also be able to increase the visibility of all products by displaying more comprehensive products to customers, and e-commerce providers may be able to manage inventories more effectively.

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