AUTOMATION OF PRICING MANAGEMENT IN RETAIL BASED ON DATA ANALYTICS
DOI:
https://doi.org/10.46827/ejefr.v9i5.2086Cuvinte cheie:
dynamic pricing, automation, data analytics, machine learning, pricing algorithms, digital transformationRezumat
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
Referințe
Birim S, Kazancoglu I, Mangla SK, Kahraman A, Kazancoglu Y, 2024. The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods. Annals of Operations Research 339(1): 131-161. https://doi.org/10.1007/s10479-021-04429-x
Okokpujie K, Adetayo AT, Etuka SE, Okokpujie IP, 2024. Development of a Smart Real-Time Electronic Price Tag Management System Using Bluetooth, and Cloud Database Technologies. World Conference on Information Systems for Business Management. Singapore: Springer Nature Singapore 245-258. https://doi.org/10.1007/978-981-96-1206-2_19
Das P, Pervin T, Bhattacharjee B, Karim MR, Sultana N, Khan MS, Kamruzzaman FNU, 2024. Optimizing real-time dynamic pricing strategies in retail and e-commerce using machine learning models. The American Journal of Engineering and Technology 6(12): 163-177. Retrieved from https://theamericanjournals.com/index.php/tajet/article/view/5764
Tullii M, Gaucher S, Merlis N, Perchet V, 2024. Improved algorithms for contextual dynamic pricing. Advances in Neural Information Processing Systems 37: 126088-126117. Retrieved from https://arxiv.org/abs/2406.11316
Nazarova Ye, 2025. The influence of psychoanalytic practices on leadership and organizational culture. International Journal of Professional Science 4(1): 71-77.
Gupta H, Saxena DN, 2024. Leveraging Machine Learning for Real-Time Pricing and Yield Optimization in Commerce. International Journal of Research Radicals in Multidisciplinary Fields 144(3(2)): 501-525. Retrieved from https://www.researchgate.net/publication/393605068_Leveraging_Machine_Learning_for_Real-Time_Pricing_and_Yield_Optimization_in_Commerce
Dzreke SS, 2025. The symbiotic interplay between big data analytics (BDA) and artificial intelligence (AI) in the formulation and execution of sustainable competitive advantage: A multi-level analysis. Frontiers in Research 4(1): 35-56. https://doi.org/10.71350/30624533119
Rahman KO, Islam MS, Rezvi RI, Hasan M, Khanam A, Nasrullah F, Akash AH, 2025. AI-Driven Next-Gen US Retail: An Empirical Study on Optimizing Supply Chains by leveraging Artificial Intelligence, Business Intelligence, and Machine Learning. Journal of Computer Science and Technology Studies 7(1): 258-264. https://doi.org/10.32996/jcsts.2025.7.1.19
