Enhancing Retail Transactions: A Data-Driven Recommendation Using Modified RFM Analysis and Association Rules Mining

Abstract

Retail transactions have become an integral part of the economic cycle of every country and even on a global scale. Retail transactions are a trade sector that has the potential to be developed continuously in the future. This research focused on building a specified and data-driven recommendation system based on customer-purchasing and product-selling behavior. Modified RFM analysis was used by adding two variables, namely periodicity and customer engagement index; clustering algorithm such as K-means clustering and Ward’s method; and association rules to determine the pattern of the cause–effect relationship on each transaction and four types of classifiers to apply and to validate the recommendation system. The results showed that based on customer behavior, it should be split into two groups: loyal and potential customers. In contrast, for product behavior, it also comprised three groups: bestseller, profitable, and VIP product groups. Based on the result, K-nearest neighbor is the most suitable classifier with a low chance of overfitting and a higher performance index.

Methods and Applications of Data Mining in Business Domains

“…These papers collectively contribute to the field of data-driven applications and algorithm development. The first paper presents a data-driven recommendation system for retail, emphasizing tailored product recommendations and marketing strategies [8]. The second paper addresses the challenge of handling imbalanced data streams with concept drift, introducing the HSDW-MI algorithm [9].…”

Increasing Product Promotion by Implementing Association Rules in The Web-Based Second_Oldshoes Store Sales System

“…Tarigan et al (2021) conducted research on FP_Tree Association Rule Mining for e-commerce product advertising efforts to improve product brand sales strategies in Indonesia, highlighting the importance of association rules in improving e-commerce marketing strategies [11]. Chen and Gunawan (2023) focused on improving retail transactions through data-driven recommendation systems using Modified RFM Analysis and Association Rules Mining, highlighting the role of association rules in optimizing retail and customer recommendation systems [12].…”

Optimizing Decision-Making Through Customer-Centric Market Basket Analysis

“…These methods require setting a minimum support threshold to identify frequently purchased items. However, a low threshold can lead to an overwhelming number of weak association rules and high computational demands [3,4]. This study proposes a paradigm shift by focusing on customer segmentation instead.…”