Proactively populate cart through combination of customer history analysis and filtering techniques
The E-commerce company wanted to introduce a prepopulate cart function that would automatically populate shopping carts for their new and existing customers. It may include identifying the most relevant customer attributes for specific customers, devices, channels, and purchase history.
Neal Analytics leveraged historical multi-variable segmentation that helped in segmenting customers based on various attributes. Implementing a content & collaborative filtering method helped infer product-customers relationships.
Content-based filtering is a popular technique that helps understand the users’ preferences based on keywords in the database. Here, content refers to the attributes of the products a user likes. So, the idea behind content-based filtering is to tag products using specific keywords and to recommend different products with similar attributes.
Example: Netflix provides you movie recommendations based on your choices.
While Collaborative filtering method helps filter out products that a user might like based on the reactions by similar users. It uses a large database of users and finds smaller sets of users having similar tastes to a particular user.
Example: Amazon displays products that are frequently bought together (Shaker bottle is frequently purchased with protein supplement).
The historical multi-variable segmentation based on various customer attributes helped the e-commerce company to predict likely next purchases of their customers.
Based on the segmentation, the most likely purchase predictions for customers were:
- Paper clips
- Protein bars
- Office equipment