Optimizing Retail Pricing Structures with Robotic Process Automation and Machine Learning Algorithms

Abstract

Retail pricing plays a crucial role in the success of businesses, impacting both revenue generation and customer satisfaction. Traditional pricing strategies often lack the agility and precision required to adapt to dynamic market conditions and consumer behavior. This research paper explores the integration of Robotic Process Automation (RPA) and Machine Learning (ML) algorithms in optimizing retail pricing structures. By automating repetitive tasks and leveraging advanced analytics, retailers can enhance pricing strategies to maximize profitability and competitiveness. This paper discusses the application of RPA and ML in pricing optimization, presents case studies of successful implementations, and outlines future trends in the field.