Reinforcement Learning Applications for Dynamic Pricing in Microsoft Dynamics 365 Commerce
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P137Keywords:
Reinforcement Learning, Dynamic Pricing, Microsoft Dynamics 365 Commerce, Markov Decision Process (MDP), Deep Q-Network (DQN), Contextual Bandits, Retail Analytics, Enterprise Commerce Systems, Price Optimization, Artificial Intelligence in RetailAbstract
Dynamic pricing has become a critical capability in modern digital commerce platforms, enabling organizations to optimize revenue, profit margins, and inventory turnover in rapidly changing market environments. Traditional rule-based and statistical pricing models often fail to adapt effectively to complex and dynamic consumer behavior. This paper explores the application of reinforcement learning (RL) techniques for dynamic pricing within Microsoft Dynamics 365 Commerce. The study formulates the pricing process as a Markov Decision Process (MDP), where pricing decisions are treated as sequential actions that influence future demand and revenue outcomes. Various reinforcement learning approaches, including contextual multi-armed bandits and Deep Q-Networks, are evaluated for their suitability in enterprise retail environments. The proposed framework integrates RL models with the existing pricing engine of Microsoft Dynamics 365 Commerce using API-based overrides and cloud-based machine learning services. Experimental evaluation demonstrates that reinforcement learning-driven pricing strategies outperform static and rule-based pricing methods in terms of revenue uplift, margin optimization, and inventory efficiency. The paper also discusses implementation challenges such as regulatory compliance, fairness considerations, cold-start problems, and real-time system constraints. The findings suggest that reinforcement learning can provide a scalable and adaptive pricing solution for enterprise commerce systems, offering significant competitive advantage in digital retail ecosystems.
References
[1] C. Yin and J. Han, “Dynamic pricing model of e-commerce platforms based on deep reinforcement learning,” Comput. Model. Eng. Sci., vol. 127, no. 1, pp. 291–307, 2021.
[2] S. Suresh Kumar, M. Margala, S. Siva Shankar, and P. Chakrabarti, “A novel weight-optimized LSTM for dynamic pricing solutions in e-commerce platforms based on customer buying behaviour,” Soft Comput., 2023.
[3] A. Agnihotri and I. I. Raj, “Advanced Deep Reinforcement Learning Framework for Dynamic Pricing Optimization in E-commerce Marketplaces,” in 2024 ICCCNT, pp. 1–6.
[4] H. Liu, “Reinforcement Learning for E-Commerce Dynamic Pricing,” in Int. Conf. on Economic Management and Green Development, 2023.
[5] A. Kastius and R. Schlosser, “Dynamic pricing under competition using reinforcement learning,” J. Revenue Pricing Manag., vol. 21, pp. 50–63, 2022.
[6] Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning, Liu et al., field experiment modeling real e-commerce pricing with DRL and MDP state/action formalism (2018).
[7] Andrii Holovko and Taras Firman, Batch Reinforcement Learning for Dynamic Pricing in E-Commerce, methods for offline RL pricing modeling.
[8] S. Maryam Ameli et al., “Deep Reinforcement Learning for Dynamic Pricing Strategies: Empirical Evidence from E-Commerce Platforms,” Int. J. Sci. Eng. Appl., vol. 14, no. 11, 2025.
[9] Pavan Mullapudi, “A Reinforcement Learning Approach to Dynamic Pricing,” Int. J. Sci. Technol., 2025.
[10] Dynamic Price Optimization for Ecommerce Platform, Scitepress paper showing RL-based AI pricing increases revenue and conversion.
[11] Neha Chopra, Amit Patel, Neha Singh, and Vikram Sharma, “Leveraging Reinforcement Learning and Neural Networks for Optimized Dynamic Pricing Strategies in E-Commerce,” Int. J. AI Advancements, 2020.
[12] Amit Sharma, Neha Patel, and Rajesh Gupta, “Leveraging Reinforcement Learning and Bayesian Optimization for Dynamic Pricing Strategies in E-commerce,” Eur. Adv. AI J., 2022.
[13] Amit Sharma, Neha Patel, and Rajesh Gupta, “Leveraging Reinforcement Learning and Gradient Boosting for Optimized AI-Driven Dynamic Pricing Strategies in B2C Markets,” Eur. Adv. AI J., 2021.
[14] Zhu C., Xin J., and Zhang D., “A Deep Reinforcement Learning Approach to Dynamic E-Commerce Pricing Under Supply Chain Disruption Risk,” Annals of Applied Sciences, 2024.
[15] Tuğçe Yavuz and Onur Kaya, “Deep Reinforcement Learning Algorithms for Dynamic Pricing and Inventory Management of Perishable Products,” Appl. Soft Comput. (Elsevier), 2024.
