Leveraging clustering for improved marketing strategy in e-commerce: a customer lifetime value approach

Abstract

In today's dynamic business landscape, characterized by a shift towards service-focused economies, companies are experiencing a transformative paradigm. They are proactively adapting to a new era, emphasizing the cultivation of enduring customer relationships as the linchpin of sustainable profitability. This strategic shift underscores the pivotal role of marketing, which extends beyond traditional paradigms to serve as the cornerstone for enhancing a company's financial performance. Within this context, marketing endeavors are geared towards augmenting what we refer to as "Customer Lifetime Value" (CLV), a multifaceted concept akin to a mosaic, encapsulating the cumulative value derived from loyal customers over time. Various models, including the commonly used RFM (Recency, Frequency, Monetary) model, have been utilized in predicting customer lifetime value (CLV). The RFM model evaluates customers based on the recency, frequency, and monetary value of their transactions. Additionally, conventional methods like the widely used Elbow approach have been employed to determine the optimal number of clusters in CLV models. However, this study aims to explore CLV, particularly within the E-Commerce sector, by leveraging the analytical power of the Single Value Decomposition (SVD) clustering method. The paper underscores the critical significance of CLV models in navigating this intricate domain. These models serve as potent instruments for segmenting the market intelligently and optimizing resource allocation for marketing activities. In E-Commerce, where strategic decision-making is vital, businesses deploy these resources judiciously to acquire, retain, and cross-sell to customers, epitomizing the astute acumen required for E-Commerce success. In the realm of E-Commerce, it has been customary to assess Customer Lifetime Value through the prism of Recency, Frequency, and Monetary (RFM) variables. However, it is essential to recognize that the relative importance of these variables undergoes dynamic interactions influenced by product or service attributes and industry-specific idiosyncrasies within the E-Commerce domain. To encapsulate, this paper delves into the intricate facets of CLV, unveiling the potential of various CLV models empowered by the clustering method, using Single Value Decomposition approach to determine the most optimal clusters, as strategic assets for modern E-Commerce. These models serve as a compass for market segmentation and resource allocation, thereby sculpting the trajectory towards success for E-Commerce enterprises in the ever-evolving landscape of customer-centric commerce.

Key words: Customer Lifetime Value, Recency, Frequency and Monetary Model, Customer Relationship Management, Prediction