Dynamic Pricing Strategies in Retail: Leveraging Real-Time Data Analytics for Competitive Advantage

Amarnath Immadisetty

doi:10.70589/JRTCSE.2025.13.1.8

Authors

Amarnath Immadisetty Sr Manager, Software Engineering, Lowe's, United States of America Author

DOI:

https://doi.org/10.70589/JRTCSE.2025.13.1.8

Keywords:

Dynamic pricing, real-time data analytics, consumer behavior, market conditions, competitor pricing, price optimization, advanced analytics

Abstract

Today, dynamic pricing is one of the very powerful strategies that retailers are striving for in this race of changing markets. These allow retailers to change and update prices through real-time data analytics, factoring immediate changes in consumer behavior, general market conditions, and competitor pricing. The article goes on to review how dynamic pricing models let an organization come most optimally at price decisions, using advanced analytics that are integrated with realtime data about sales trends, inventory levels, and customer preferences. It considers that all these strategies eventually show an effect on profitability, customer satisfaction, and market positioning and, therefore, brings out the possibility of greater responsiveness and personalized pricing. The article reviews industry case studies and current best practices in dynamic pricing adoption by retailers, considering both the challenges and opportunities. It would include data accuracy, algorithmic complexity, and ethical implications of personalized pricing. The article thus concludes with recommendations on how to implement effective dynamic pricing strategies that balance competitive advantage with consumer trust.

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