Online stores are popular with many people, as it is convenient to choose products in them, looking at the assortment for as long as you need. You calmly study the products in a comfortable atmosphere without intrusive consultants. But this is a hidden minus of online trading - there is no way to quickly get advice from the seller on choosing goods. Personal recommendations on the website and in newsletters generated by artificial intelligence help to close this issue . They are based on information about: human behavior on the website and in newsletters; client preferences; data about goods.
Recommendations help keep visitors on the site longer C Level Executive List and stimulate sales. And clients get a silent consultant who offers them exactly what they need. In our article, we will consider how recommendation blocks work, what is necessary for their configuration, and what results to expect from them. Benefit from product recommendations According to statistics , 59% of online shoppers believe that it is easier to find interesting products on sites with personalized offers. And 56% of visitors are more likely to return to such an online store. In general, the recommendations on the site help: accelerate conversion growth; extend session time and increase viewing depth; increase the amount of the average check; to improve the customer experience (the buyer saves time, quickly finds the necessary product, sees offers that meet his interests); promote the desired products/brands.
In newsletters, recommendation blocks increase CTR, stimulate orders and increase profits. Types of recommendation blocks By content, recommendation blocks are divided into two types - general and personal. The choice of the first or second option depends on the availability of information about the client. General blocks should be shown when there is not enough information about the visitor/subscriber and it is not clear what interests him. To attract new users, bestsellers, promotional offers and sales, top products from a specific category, etc. are displayed as recommendations. Personal - formed from the history of a person's interaction with the online store and mailings. Views of pages, individual products, orders, adding to favorites, clicks in newsletters, the amount of the average check are taken into account. In addition, you can recommend products selected based on the views and purchases of customers with similar interests.