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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

 

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

MG Field Name Type Range Description
RecipientId VARCHAR   Unique customer identifier.
ML-RecPurchased VARCHAR   Product the customer purchased.
ML-RecProduct VARCHAR   Product recommended for the customer.
ML-RecScore FLOAT 0-100 normalized output of scoring Metric that indicates the strength of the relationship between the two products – the higher the score, the more likely the customer is to purchase it.
ML-RecLow BOOL 0 or 1 Predefined segments.
Our recommended audience definitions today break out high/medium/low, where the top quintile = high; mid three quintiles = medium; bottom quintile = low. However, we can customize this during implementation.
ML-RecMed BOOL 0 or 1
ML-RecHigh BOOL 0 or 1
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