Group | Name | Short description | |
|---|---|---|---|
| 1 | Promote products or content in the target of active promoted campaigns | ||
| 2 | Views > Price Range | Products which match (or are slightly higher than) the products price range of the previsouly viewed products | |
| 3 | Views > End-up Buying | Products which are often bought in the same session after viewing the same products the current visitor has already viewed | |
| 4 | Views > Defining Tags | Promote products or content with Discovery Tags matching previously viewed products. Each product gets a list of Discovery Tags representing the most common search terms used to find a product. Each product gets a list of Defining Tags representing which is a subset of the product attributes values that have been identified as important in the selection of a product | |
| 5 | Neural Collaborative Filtering for individual recommendations based on the purchases of the customer and leveraging the patterns in the purchase behaviors of other customers | ||
| 6 | Basket > Often Bought Together Basket > Collaborative Filtering Bought Together | Promote products often bought together with products already in the basket of the visitor (first practice with statistics, second variant with AI Collaborative Filtering) | |
| 7 | Views > Often Bought Together Views > Collaborative Filtering Bought Together | Promote products often bought together with products the visitor has already viewed (first practice with statistics, second variant with AI Collaborative Filtering) | |
| 8 | Views > Often Viewed in Same Session Views > Collaborative Filtering Viewed Same Session | Promote products often viewed in the same session as products the visitor has already viewed (first practice with statistics, second variant with AI Collaborative Filtering) | |
| 9 | Viewed Category, Brands & Landing Pages Used Facets Values Used Price Range Previous Search(es) Related Search(es) | Promote products matching the
| |
| 10 | Last Purchase > Often Bought After Last Purchase > Collaborative Filtering Bought After | Promote products often bought after the last purchase of the customer (first practice with statistics, second variant with AI Collaborative Filtering) | |
| 11 | Purchases > Often Bought By the Same Customer Purchases > Collaborative Filtering Bought By the Same Customer | Promote products often bought by the same customer (based on the purchase history of the customer) (first practice with statistics, second variant with AI Collaborative Filtering) | |
| 12 | Basket > Often Basketed Together Basket > Collaborative Filtering Basketed Together | Promote products often basketed together with products already in the basket of the visitor (first practice with statistics, second variant with AI Collaborative Filtering) | |
| 13 | Promote products or content in the same Price Quadrant as previously viewed products. | ||
| 14 | Promote products or content in the same Price Quadrant as previously bought products. | ||
| 15 | Purchases > Defining Tags | Promote products or content with Discovery Tags matching previously bought products. Each product gets a list of Discovery Tags representing the most common search terms used to find a product. Each product gets a list of Defining Tags representing which is a subset of the product attributes values that have been identified as important in the selection of a product. | |
| 16 | Wish-list > Products Wish-list > Products Attributes Wish-list > Products Discovery Tags Wish-list > Products Defining Tags | Promote products
See Discovery and Defining Tags definition above | |
| 17 | Basket > Products Attributes Basket > Products Discovery Tags Basket > Products Defining Tags | Promote products
See Discovery and Defining Tags definition above | |
| 18 | Best-selling trends within the Cluster the visitor is predicted to belong based on his prior product views. Customer Clustering distributes all customers into clusters based on their purchase history | ||
| 19 | Best-selling trends within the Cluster the customer belongs. Customer Clustering distributes all customers into clusters based on their purchase history |
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