While the shopping experience is always enjoyable, it can also be stressful if consumers don’t know where to start, which is exactly what an e-commerce website’s recommendation engine hopes to solve. However, without AI assistance, these engines can only display "the items viewed/purchased by the most people" or "other items viewed/purchased by people who viewed/purchased this item". Such a solution cannot present consumers with products that best meet their needs, nor can it solve the problem of "difficulty in choosing". Consumers often have only a rough idea of the product they want to buy. For example, they want to buy a "black leather jacket", but they may not have enough information or time to browse all the products in the store, especially when the product list spans physical and This is especially true when it comes to online stores. In other words, consumers have no way of knowing what information they are missing, and when they have too many choices, they may eventually give up on checking out the items in their shopping carts , causing the brand to lose these customers.
The use of recommendation engines driven by AI technology can bahamas phone number data accurately target each consumer's preferences and generate more personalized recommendations when consumers browse the website. Take black leather jackets as an example. Rather than displaying products based on overall popularity or displaying all black leather jacket styles, if a store uses an AI recommendation engine, it can prioritize the display of products that are most likely to attract a specific consumer based on their interests and behaviors. black leather jacket.
Physical stores can use smartphones, tablets or smart screens to achieve the same effects as above; on the premise that the hardware equipment does not affect the shopping experience, AI technology optimization can provide a better shopping experience , and the hardware equipment will never cause The shopping experience is imperfect, and AI technology can optimize a better shopping experience. For example, if AI can more accurately predict that consumers will like the latest blue clutch bag, why rely on the store clerk to recommend a bag that he thinks is suitable for you? Where is the payment? Regardless of brick-and-mortar or online, as long as consumers interact with stores using AI personalization models more often, personalized product recommendations will be more accurate. Soon, AI will know consumers better than they know themselves.
When enterprise merchants carry out OMO layout, consumers can browse more products. The recommendation engine driven by AI technology plays a key role in recommending appropriate products to the most suitable consumers in the shortest time.