Dynamic pricing: Definition, implications for managers, and future research directions

Abstract

Dynamic pricing has evolved with technology from earlier price negotiations. To maximize revenue and provide specialized shopping experiences, businesses today use algorithms and data analysis to adapt prices. We define dynamic pricing as price changes that are prompted by changes or differences in four key underlying market demand drivers: (1) People (i.e., individual consumers or consumer segments), (2) Product configurations, (3) Periods (i.e., time), and (4) Places (i.e., locations). The transition from static pricing (uniform prices) to dynamic pricing (changing prices) is evident from different examples, such as online retailers personalizing offers based on customer behavior, and algorithms using facial recognition for personalized pricing in physical stores.

Fueled by AI and machine learning algorithms, dynamic pricing is transforming industries from transportation to e-commerce, optimizing revenue and enhancing customer experiences. While it offers benefits like personalization, challenges include ethical concerns, cost of implementation, and customer dissatisfaction. Effective data organization and AI expertise are crucial, but potential pitfalls and regulatory oversight must also be considered. This paper examines the multidimensional application of dynamic pricing, highlights the adaptability and efficiency of dynamic pricing in forming profitable pricing strategies and maximizing revenue, and calls for continued research on the topic to balance revenue, customer satisfaction, and ethics.