Dynamic Pricing Strategies in Retail: How Customer Analytics Can Optimize Pricing Models

Authors: Divya Chockalingam

DOI: https://doi.org/10.5281/zenodo.15054790

Short DOI: https://doi.org/g884b5

Country: USA

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Abstract: Dynamic pricing has revolutionized the retail sector, allowing businesses to adjust prices in real-time based on customer behavior, competitor pricing, and market demand. This paper explores how customer analytics can optimize dynamic pricing models, improving revenue, customer satisfaction, and market competitiveness. By leveraging big data and machine learning, retailers can implement intelligent pricing strategies that respond to evolving consumer patterns. The study also highlights case studies of successful dynamic pricing implementations and discusses the challenges associated with these strategies, including ethical considerations and technological barriers.

Keywords: Dynamic Pricing, Customer Analytics, Retail Pricing Models, Machine Learning, Big Data, Revenue Optimization, Artificial Intelligence, E-Commerce Pricing, Real-Time Price Adjustment, Consumer Behavior Analysis

Paper Id: 232274

Published On: 2020-01-03

Published In: Volume 8, Issue 1, January-February 2020

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