A Comprehensive Review of Dynamic Pricing Strategies: Optimizing Market Competitiveness and Consumer Satisfaction for Sri Lankan Consumer Brands

Abstract

Dynamic pricing has been widely successful in enhancing customer satisfaction and maximizing revenue across global markets. This paper aims to delve into creating dynamic pricing models specifically designed for Sri Lankan consumer brands, paying close attention to the distinctive features of the local market and consumer buying habits. The paper examines how dynamic pricing has been used worldwide in retail and electric vehicle charging sectors to develop a model that suits the Sri Lankan context. The approach involves statistical analysis to select key features, gathering data from external market sources and internal brand records, and evaluating the model using machine learning techniques like ensemble methods and deep learning. The effectiveness of these models is gauged using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), comparing dynamic pricing strategies with traditional static models. The findings indicate that dynamic pricing can enhance sales, customer satisfaction, and operational efficiency for Sri Lankan brands, although legal and ethical considerations must be addressed in the implementation process. Future research should focus on validating the findings empirically, refining the model through practical experiments, and integrating additional variables. This review provides valuable insights into the utilization of dynamic pricing in emerging markets and contributes to the broader academic discussion.