This is the Way. Market Basket Analysis.

Recently, some people asked me interesting questions about applying the Market Basket Analysis algorithm for product recommendation. First, this question needs to be corrected, as Market Basket Analysis does not equal specific algorithms. Market Basket Analysis (MBA) is a generic term of data analytics that retailers usually use to increase sales performance through a deeper understanding of customer purchasing patterns. It usually involves analyzing the massive amount of purchase history from the customers and turning the data into valuable/actionable insight by Prescriptive and Predictive Analytics.

Retailers applied Market Basket Analysis techniques to various scenarios, such as marketing campaigns & promotions, business & demand forecasting, product recommendations, etc.

There are two types of Market Basket Analysis in principle.

1. Predictive: Based on purchasing history, determine the relationship and product recommendation for cross-selling.
2. Prescriptive: Based on purchasing history, analyze the data in multiple dimensions, such as the hottest product in a specific period and group of customers. Then based on such actionable insight, compose new product offers to drive higher sales performance.

There are various approaches to achieving different recommendation objectives. In most cases, most people would relate the Market Basket Analysis to association rules based recommendations. It is the approach to determine the associations between the products that customers purchase. Then based on the product associations and provide product recommendations. In this story, I will share how to apply association rules based recommendations with the Apriori algorithm.

Simple illustration of association rule generation.

Association Rules is an if-then statement that would tell us the probability of relationships between products (items) in the transaction dataset. If we have a rule “X → Y”, X stands for antecedent (if), and Y stands for consequent (then). There are some key metrics/measures in association rules.

• Support is the ratio of X related transactions to all transactions. For example, if out of 1000 transactions, 100 purchase bread, then support for bread will be 100/1000 = 0.1 (10%).
• Confidence divides the number of X and Y…