Recommendation types

Cross-sell

The purpose of this type of recommendation is to offer additional items to the ones the customers are viewing and get them to add more items to their shopping carts. The system compares the items which were viewed by other users of a similar profile to the item that is currently being viewed by a specific user. On this basis, the system prepares item recommendations that may encourage the user to add a item to the cart.

Similar items

This type of recommendation involves preparing suggestions on the basis of a single item. The purpose of the recommendation is to help customers make purchase decisions faster by showing related offers and recommending similar items to the ones they have viewed. Therefore, to prepare the recommendations, the system analyzes page visits, items and/or transactions. This is the easiest type of recommendation to implement because it operates only on the information sourced from the item 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 item).

Cart recommendations

This type of recommendations allow you to create item offers on the basis of the items your customers have added to the cart. This way you can increase the chances that the customers will buy more items, as the items recommended are complementary to the ones they have in their carts.

Personalized recommendations

This type of recommendation lets you suggests the items on the basis of user buying preferences and their behavioral profile. In other words, the users see from the very first page the items which are proposed on the basis of what they have viewed or bought and the analysis of the item feed. As a result, the customer doesn’t get lost in item 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 item feeds (e.g. the system can propose a set of items,like a computer, TV and books, because the customer purchased or viewed similar items in the past).

Visual similarity

The purpose of this recommendation type is to help customers make purchase decisions faster by showing visually similar items (shape, color, style, etc.). To prepare recommendations, the system analyzes item feeds and especially their images. On this basis, it prepares offers of similar items.

Last seen

This type of recommendation is used to display the items which have been viewed recently by a particular user. To prepare recommendations, the system analyzes page.visit events and in response it displays the items which have been viewed by a particular customer.

Top items

The purpose of this type of recommendation is to display top items according to a metric you select, such as bestsellers of the last 30 days, items which have been viewed the most during the last 30 days, etc.

Item comparison

This type of recommendation is an extension of the similar items recommendation - apart from displaying items similar to the context item (currently viewed item), it shows the attributes of similar and viewed items in a table which makes them easy to compare with the current item.

Recent interactions

This type of recommendation allows to promote items towards which the customers performed a specific action which you select. Apart from these most common, such as a visit to the item page, adding the item to the cart, marking the item as favorite, or purchase, you can select any measurable event related to the items you offer.

Section

This type of recommendation allows you to display a section with personalized item features such as a category, brand, style, collection, and so on. The motif (item feature/attribute) of the section is personalized as well as the items selected for the section. This way you can personalize the whole page.

Attributes

This type of recommendation allows you to promote the features of the items, such as brand, styles, categories or any kind of attributes which are selected for each customer individually. The recommendation includes only the selected attributes, not items.

Recommendation model summary

The table that explains how recommendations of each type are generated (the source and context needed):

Scenario AI engine Metric based Customer context Item context Multiple item context No context
Personalized Green checkmark Green checkmark
Similar items Green checkmark Green checkmark
Visual similarity Green checkmark Green checkmark
Cross-sell Green checkmark Green checkmark
Cart recommendations Green checkmark Green checkmark
Last seen Green checkmark Green checkmark
Top items Green checkmark Green checkmark
Item comparison Green checkmark
Recent interactions Green checkmark Green checkmark Green checkmark
Section Green checkmark Green checkmark
Attributes Green checkmark Green checkmark

Application of recommendations


The table below presents the application of recommendation types in business scenarios:

Scenario Home page Category/brand page Item page Add to cart Checkout Zero search results Post-purchase
Personalized Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark
Similar items Green checkmark
Visual similarity Green checkmark
Cross-sell Green checkmark Green checkmark
Cart recommendations Green checkmark Green checkmark
Last seen Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark
Top items Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark
Item comparison Green checkmark
Recent interactions Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark
Section Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark
Attributes Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark Green checkmark

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