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.
- Predictive: Based on purchasing history, determine the relationship and product recommendation for cross-selling.
- 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…
