Building a Dynamic Pricing Engine with Machine Learning for Retail

Akhilesh Kota

doi:10.32628/CSEIT2410612428

Authors

Akhilesh Kota Sam's West, Inc, USA Author

DOI:

https://doi.org/10.32628/CSEIT2410612428

Keywords:

Machine Learning, Dynamic Pricing, Real-time Processing, Microservices Architecture, Retail Optimization

Abstract

This technical article presents a comprehensive analysis of a machine learning-based dynamic pricing engine designed for retail environments. The system leverages advanced microservices architecture, real-time data processing, and sophisticated machine-learning algorithms to optimize pricing decisions across diverse market conditions. We explore the implementation of a scalable solution that combines high-frequency data processing with intelligent price optimization, incorporating competitive analysis, demand patterns, and inventory management. The architecture employs distributed computing principles, featuring robust data ingestion, advanced feature processing, and multi-model machine learning components. Our findings demonstrate significant improvements in revenue optimization, operational efficiency, and market competitiveness while maintaining high system reliability and security standards.

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