An Ecommerce Recommendation Engine Can Act Like A Personal Shopper For Your Visitors

An e-commerce recommendation engine does exactly what the name implies; it creates personalized recommendations for online shoppers based on their previous purchases. Recommendation engines using artificial intelligence (AI) are already in use; Facebook will suggest people that you may know using AI technology, which uses your other friends as a basis to decide who to suggest. YouTube also suggests videos based on your viewing habits. Recommendation engines may also make suggestions based on the prevailing norm; people who bought item A also bought item B, therefore, if you bought item A, you may like item B as well.

An e-commerce recommendation engine is extremely helpful for individuals who operate an online store. When customers go to checkout, they see additional options based on their initial purchase. Marketers are also using e-commerce recommendation engines to personalize emails, suggesting products that the recipient is likely to purchase.

The latest in AI technologies for e-commerce recommendation engines uses a combination of previous behaviors and real-time data, providing a more personalized service for consumers who are shopping online. For example, a person looking at different styles of black leather boots would find his or her shopping experience more personal if the boot selections were rearranged to show all of the black leather boots first.

AI technology is ideal for e-commerce merchants because it can act like a personal shopper. The technology learns in a way that is similar to the way people use their brain to learn. A good salesperson in a brick and mortar store will see that a shopper is drawn to tweed skirts in brown, so the salesperson will steer the customer toward similar skirts and suits. If other shoppers who bought brown skirts also bought white blouses, the salesperson will remember this and suggest white blouses as well. AI technologies work the same way as the salesperson’s brain does, incorporating historical data and real-time data to make personalized recommendations and act like a personal shopper would for his or her client’s.