LRFS: Online Shoppers’ Behavior-Based Efficient Customer Segmentation Model

Abstract

In the realm of digital commerce, online shopping has witnessed unprecedented growth globally, becoming a cornerstone of modern consumer behavior. This research introduces an advanced customer segmentation model, named LRFS, which builds upon the traditional LRF framework (Length of Relationship, Recency of Purchase, and Frequency of Purchase), specifically tailored for the e-commerce sector. The innovation of the LRFS model lies in the integration of a novel component, “S”, which quantifies the Staying Rate relative to the revenue generated by customers on a specific website. This addition aims to enhance the granularity and efficacy of customer segmentation by leveraging data extracted from Google Analytics. To operationalize the LRFS model, this study employs two renowned clustering algorithms, K-Means and K-Medoids, analyzing the dataset through the lens of three distinct dimensionality reduction techniques: PCA (Principal Component Analysis), t-SNE (t-Distributed Stochastic Neighbor Embedding), and Autoencoder. This methodological approach facilitates a robust comparative analysis between the LRFS model and its predecessors — LR, LF, and LRF — utilizing K-Means clustering to evaluate the precision of customer cluster assignments. The empirical findings of this research underscore the superiority of the LRFS model in achieving more accurate and insightful customer segmentation. Additionally, a composite Customer Classification and Customer Relationship Matrix was deployed to discern the nuanced traits of clustered groups, identifying the fusion of K-Medoids and t-SNE as the most effective strategy for capturing the full spectrum of customer dynamics. The research further elucidates several test cases and use case scenarios, demonstrating the practical applications of the LRFS model in conjunction with K-Means and PCA to refine marketing strategies and foster a deeper understanding of the online customer base. Through the development and application of the LRFS model, this study contributes significantly to the field of e-commerce by providing a more nuanced tool for businesses to tailor their marketing initiatives, ensuring alignment with the evolving preferences and behaviors of their online clientele.

Keywords

• Customer segmentation
• unsupervised machine learning
• K-means
• K-medoids
• RFM analysis
• LRFM analysis