In the realm of online shopping and entertainment, we often encounter personalized recommendations like “You may also like this item” or “Recommended movies based on your preferences.” These suggestions stem from association rule mining algorithms, specifically market basket analysis. This method uncovers item relationships from historical data, shedding light on customer behavior and preferences.
Recently, I explored market basket analysis in my Big Data and Predictive Analytics course at Centennial (which uses SAS, while this series utilizes Python). Over the past two weeks, I’ve delved into this fascinating technique, planning a three-article series. The first will cover basics with simple examples, the second will use realistic data and some advanced techniques, and the final article will explore applications in movie recommendation analysis. However, these articles will not cover sequence analysis.
Market basket analysis dissects customer buying habits by analyzing item co-occurrence in shopping baskets, offering insights into preferences, product affinities, and potential upselling opportunities.
Please refer to my Linkedin article for more details: https://www.linkedin.com/pulse/unveiling-customer-behavior-through-marketing-basket-analysis-ju-szgqc