Optimizing E-Commerce Profits: A Comprehensive Machine Learning Framework for Dynamic Pricing and Predicting Online Purchases

Abstract

In the online realm, pricing transparency is crucial in influencing consumer decisions and driving online purchases. While dynamic pricing is not a novel concept and is widely employed to boost sales and profit margins, its significance for online retailers is substantial. The current study is an outcome of an ongoing project that aims to construct a comprehensive framework and deploy effective techniques, leveraging robust machine learning algorithms. The objective is to optimize the pricing strategy on e-commerce platforms, emphasizing the importance of selecting the right purchase price rather than merely offering the cheapest option. Although the study primarily targets inventory-led e-commerce companies, the model's applicability can be extended to online marketplaces that operate without maintaining inventories. The study endeavors to forecast purchase decisions based on adaptive or dynamic pricing strategies for individual products by integrating statistical and machine learning models. Various data sources capturing visit attributes, visitor details, purchase history, web data, and contextual insights form the robust foundation for this framework. Notably, the study specifically emphasizes predicting purchases within customer segments rather than focusing on individual buyers. The logical progression of this research involves the personalization of adaptive pricing and purchase prediction, with future extensions planned once the outcomes of the current study are presented. The solution landscape for this study encompasses web mining, big data technologies, and the implementation of machine learning algorithms.