A design of cross-selling products based on frequent itemset mining for coffee shop business

Abstract

Using a case study at the XYZ coffee shop in Bandar Lampung, Indonesia, this work conducts knowledge extraction on a sales dataset to create a cross-selling model for product bundling advice. Understanding Knowledge extraction was done using a frequent itemset mining technique based on an Apriori algorithm to extract a set of association rules between products. The study implemented a five-stage frequent itemset structure that encompasses business comprehension, data preparation, data exploration, model creation using the Apriori algorithm, and rules evaluation. The framework that comes from this research provides a set of bundling association rules across items for cross-selling strategies that involve many products and complex sales timeframes. Additionally, we recommended sales activities based on loyalty cards to enhance our dataset with consumer attributes and purchasing trends. Based on customized services and tailored offers based on consumers' past purchases and spending patterns, the loyalty card recommendation was created. Consequently, we may target the appropriate clients and items with our marketing initiatives