Predictive Analytics for Dynamic Pricing in Real-Time Retail Environments

  • Authors

    • Noah Wright Data Engineering Lead SAP, Germany. Author
    • Isabella Moore HR Director Siemens, Germany. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2022PII6N3Q

    Published 10-04-2022

  • Predictive Analytics, Dynamic Pricing, Real-Time Retail, Machine Learning, Demand Forecasting, Pricing Optimization, Retail Technology, Customer Behavior, AI In Retail, Pricing Algorithms

    Issue

    Vol. 5 No. 2 (2022)

    Section

    Articles

    How to Cite

    [1]
    N. Wright and I. Moore, “Predictive Analytics for Dynamic Pricing in Real-Time Retail Environments”, IJMLPA, vol. 5, no. 2, pp. 01–12, Oct. 2022, doi: 10.67228/3142788X/IJMLPA-2022PII6N3Q.

  • Abstract

    In the rapidly evolving landscape of retail, dynamic pricing has emerged as a critical strategy for maximizing revenue and enhancing customer satisfaction. The integration of predictive analytics into real-time retail environments enables businesses to adjust prices based on demand, customer behavior, market trends, and competitor actions. This paper explores the intersection of predictive analytics and dynamic pricing, examining the technological frameworks, algorithms, and data sources that facilitate intelligent pricing decisions in real-time. We review current methodologies, present a conceptual model for real-time predictive pricing, and analyze the practical challenges, including data privacy, ethical considerations, and system scalability. The study concludes with insights into future trends, such as hyper-personalization and AI-driven pricing, offering a strategic roadmap for retailers seeking to stay competitive in a data-driven market.

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