AUTOMATION OF PRICING MANAGEMENT IN RETAIL BASED ON DATA ANALYTICS

Kitaeva Iuliia

Abstract


This article examines the changing processes of pricing management in retail trade under the influence of digital transformation and the growth of data volumes. The potential of using data analytics and intelligent algorithms for automating pricing decisions is investigated. Pricing models are analyzed, including heuristic rules, regression methods, and machine learning algorithms, particularly reinforcement learning. Particular attention is given to the technological infrastructure that enables the integration of data sources and the implementation of pricing strategies in real time. The effects of introducing analytical tools are identified, including increased margin, accelerated inventory turnover, and improved adaptability of companies under market volatility.

JEL: L81, M31, C61


Keywords


dynamic pricing, automation, data analytics, machine learning, pricing algorithms, digital transformation

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References


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DOI: http://dx.doi.org/10.46827/ejefr.v9i5.2086

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