RE-BUY - What else could be experimented with?

Group

Name

Short description

Group

Name

Short description

1

Visitor Journey Personalization (VJP)

Views > Price Range

Views > Higher Price Range

Products which match (or are slightly higher than) the products price range of the previsouly viewed products

2

Visitor Journey Personalization (VJP)

Views > End-up Buying

Products which are often bought in the same session after viewing the same products the current visitor has already viewed

3

Visitor Journey Personalization (VJP)

Views > Discovery Tags

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

4

Visitor Journey Personalization (VJP)

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)

5

Visitor Journey Personalization (VJP)

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)

6

Visitor Journey Personalization (VJP)

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)

7

Visitor Journey Personalization (VJP)

Views > Product Attributes

Promote products or content with attribute values matching previously viewed products

8

Marketing Alignment (MA)

Promoted Campaigns

Promote products or content in the target of active promoted campaigns

9

Visitor Journey Personalization (VJP)

Viewed Category, Brands & Landing Pages

Used Facets Values

Used Price Range

Used Higher Price Range

Previous Search(es)

Related Search(es)

Promote products matching the

  • viewed top product-listing pages (category pages, brand pages, campaign landing pages, …)

  • used facets values or price range (search / product listing filtering options)

  • slightly higher price range than previously used

  • prior searches (or related to the prior searches) of the visitor.

10

Visitor Journey Personalization (VJP)

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)

11

Visitor Journey Personalization (VJP)

Views > Low/High End

Promote products or content in the same Price Quadrant as previously viewed products.
Each product is connected to a price percentile according to its group (e.g.: category), lower percentiles (e.g.: <20%) are labeled Low-End while top percentiles (e.g.. >80%) are labeled High-End.

12

Visitor Journey Personalization (VJP)

Basket > Products Attributes

Basket > Products Discovery Tags

Basket > Products Defining Tags

Promote products

  • matching the values of attributes of products in the basket of the visitor

  • matching the Discovery/Defining Tags of products in the basket of the visitor

See Discovery and Defining Tags definition above

13

Visitor Journey Personalization (VJP)

Views > Smart Bestsellers on Clustering

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

14

Customer Journey Personalization (CJP)

Purchases > Smart Bestsellers on Clustering

Best-selling trends within the Cluster the customer belongs.

Customer Clustering distributes all customers into clusters based on their purchase history