Product Recommendation

The purpose of MessageGears’ Product Recommendation model is to discover relationships between products through a technique called collaborative filtering.

How does it work?

This model generates a ranked list of products to recommend to the user based on their past purchase behavior/product interest, which you can deploy for a wide variety of marketing and analytical use cases. Each product has a score associated with each other product – the higher the score, the stronger the relationship between the two products. You can then use the recommended product and accompanying score in segmentation or personalization for a wide variety of marketing use cases.

The HISTORICAL_WINDOW determines what transactions are included to calculate the relationships between the products. To include a transaction in the HISTORICAL_WINDOW, the purchasing customer must have made two or more purchases within the last 365 days.

Requirements

• Past purchase behavior/product interest
  • Data Input Schemas for Transaction/conversion data

Results

This model delivers back a long form table that contains a row for each product pairing, with a score that represents the strength of the product pairing. Each product is has a score with every other product.

Schema