AI is the capability of machines (particularly computer systems) to learn, reason and self-correct. The ability to acquire information and rules that can later be formulated into conclusions is an attempt at imitating human intelligence. This highly-advanced technology is becoming particularly useful in marketing.
Note that the nature of artificial intelligence is such that it becomes more and more effective and useful with time, on the condition that it is provided with large amount of data to analyze. Algorithms (types of recommendations) need time to learn to produce adequate and beneficial solutions. The more data it utilizes, the more accurate their offers are.
The quickest way to configure AI is to start with AI recommendation types that analyze only the product feed (e.g. visual similarity). Other recommendation types apart from the product feed analyze the behavioral and transactional data which need to be delivered, so the AI system can process them.
This type of recommendation lets you suggests the products on the basis of user buying preferences and their behavioral profile. In other words, the users see from the very first page the products which are proposed on the basis of what they have viewed or bought and the analysis of the product feed. As a result, the customer doesn’t get lost in product overload. When it comes to unidentified customers, they see the best items in stock (e.g. most promoted and sold from various categories). In order to prepare the recommendations, the system analyzes page visits, transactional data and product feeds (e.g. the system can propose a set of products,like a computer, TV and books, because the customer purchased or viewed similar products in the past).
This type of recommendation involves preparing suggestions on the basis of a single product. The purpose of the recommendation is to help customers make purchase decisions faster by showing related offers and recommending similar products to the ones they have viewed. Therefore, to prepare the recommendations, the system analyzes page visits, products and/or transactions. This is the easiest type of recommendation to implement because it operates only on the information sourced from the product feed. It doesn’t consider the customer’s behavioral profile (e.g. if the customer views a particular mobile of smartphones, the system will propose other smartphones because of the similar category of the product).
The purpose of this recommendation type is to help customers make purchase decisions faster by showing visually similar products (shape, color, style, etc.). To prepare recommendations, the system analyzes product feeds and especially their images. On this basis, it prepares offers of similar products.
The purpose of this type of recommendation is to offer additional products to the ones the customers are viewing and get them to add more items to their shopping carts. The system compares the products which were viewed by other users of a similar profile to the product that is currently being viewed by a specific user. On this basis, the system prepares product recommendations that may encourage the user to add a product to the cart.
This type of recommendations allow you to create product offers on the basis of the products your customers have added to the cart. This way you can increase the chances that the customers will buy more products, as the products recommended are complementary to the ones they have in their carts.
The purpose of this type of recommendation is to display one out of five product groups. You can determine which types of products you want to display, e.g. bestsellers of the last 30 days, products which have been viewed the most during the last 30 days, etc.
This type of recommendation is used to display the products which have been viewed recently by a particular user. To prepare recommendations, the system analyzes events from page.view and in response it displays the products which have been viewed by a particular customer.