Adaptive Pricing Strategies in Digital Marketing
A Machine Learning Approach with Deep Q-Networks
Keywords:
Dynamic pricing, machine learning, Deep Q-Network model, Digital market-ing, Reinforcement learning, Neural networks.
Abstract
Dynamic pricing in digital marketing plays a crucial role in enabling businesses to adapt to the ever-changing market conditions and meet customer demands effectively. This paper presents an improved methodology for leveraging machine learning, specifically the Deep Q-Network (DQN) model, to optimize dynamic pricing decisions in the digital marketing domain. The DQN model architecture incorporates deep neural networks and reinforcement learning algorithms to learn and optimize pricing decisions. The model is trained using hyperparameters optimized through experimentation. The results demonstrate the superiority of the DQN model over a baseline strategy, with significant improvements in revenue, profit, conversion rate, customer lifetime value, market share, and price elasticity. The findings highlight the potential of machine learning in enhancing e-marketing strategies, allowing businesses to adapt pricing decisions in real-time based on customer behavior and market dynamics. This research contributes to the growing body of knowledge on dynamic pricing and provides valuable insights for businesses seeking to leverage advanced analytics in digital marketing.References
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[2] Kummara, M. R., Guntreddy, B. R., Vega, I. G., & Tai, Y. H. (2021). Dynamic pricing of ancillaries using machine learning: one step closer to full offer optimization. Journal of Revenue and Pricing Management, 20(6), 646-653.
[3] Kallio, H., Malo, P., Lainema, T., Bragge, J., Seppälä, T., & Penttinen, E. (2022). Generating Research Questions from Digital Trace Data: A Machine-Learning Method for Discovering Patterns in a Dynamic Environment. Communications of the Association for Information Systems, 51(1), 12.
[4] Loukili, M., & Messaoudi, F. (2024). Enhancing Cold-Start Recommendations with Innovative Co-SVD: A Sparsity Reduction Approach. Statistics, Optimization & Information Computing.
[5] Loukili, M., Messaoudi, F., & Azirar, H. (2024). E-Payment Fraud Detection in E-Commerce using Supervised Learning Algorithms. In Advances in Emerging Financial Technology and Digital Money (pp. 27-35). CRC Press.
[6] El Youbi, R., Messaoudi, F., & Loukili, M. (2023, November). Deep Learning for Dynamic Content Adaptation: Enhancing User Engagement in E-commerce. In The International Conference on Artificial Intelligence and Smart Environment (pp. 160-165). Cham: Springer Nature Switzerland.
[7] Dutta, G., & Mitra, K. (2017). A literature review on dynamic pricing of electricity. Journal of the Operational Research Society, 68(10), 1131-1145.
[8] El Youbi, R., Messaoudi, F., & Loukili, M. (2023, November). Machine Learning-driven Dynamic Pricing Strategies in E-Commerce. In 2023 14th International Conference on Information and Communication Systems (ICICS) (pp. 1-5). IEEE.
[9] Nunan, D., & Di Domenico, M. (2022). Value creation in an algorithmic world: Towards an ethics of dynamic pricing. Journal of Business Research, 150, 451-460.
[10] Loukili, M., Messaoudi, F., Ghazi, M. E., & Azirar, H. (2023, November). Predicting Future Sales: A Machine Learning Algorithm Showdown. In The International Conference on Artificial Intelligence and Smart Environment (pp. 26-31). Cham: Springer Nature Switzerland.
[11] Wang, F., & Aviles, J. (2023). Enhancing Operational Efficiency: Integrating Machine Learning Predictive Capabilities in Business Intellgence for Informed Decision-Making. Frontiers in Business, Economics and Management, 9(1), 282-286.
[12] Rao, I., Shirgire, P., Sanganwar, S., Vyawhare, K., & Vispute, S. R. (2022). An overview of agriculture data analysis using machine learning techniques and deep learning. In Second International Conference on Image Processing and Capsule Networks: ICIPCN 2021 2 (pp. 343-355). Springer International Publishing.
[13] Tesauro, G., & Kephart, J. O. (2002). Pricing in agent economies using multi-agent Q-learning. Autonomous agents and multi-agent systems, 5, 289-304.
[14] Jintian, W., & Lei, Z. (2009). Application of reinforcement learning in dynamic pricing algorithms. In IEEE International Conference on Automation and Logistics (pp. 419–423).
[15] Kephart, J. O., Hanson, J. E., & Greenwald, A. R. (2000). Dynamic pricing by software agents. Computer Networks, 32(6), 731–752.
[16] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., & Veness, J. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.
[17] Liu, J., Zhang, X., Hong, S. H., & Lu, R. (2018). Dynamic pricing on e-commerce platforms with deep reinforcement learning. International Conference on Learning Representations (ICLR).
[18] Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. arXiv preprint arXiv:1801.01290.
[19] Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Algorithmic collusion: Economic analysis and policy implications. Journal of Competition Law & Economics, 16(3), 274–293.
[20] Gao, Z., Gao, Y., Hu, Y., Jiang, Z., & Su, J. (2020). Application of deep Q-network in portfolio management. In 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), 268–275.
[21] Bondoux, S., Guntreddy, B. R., Vega, I. G., & Tai, Y. H. (2020). Dynamic pricing under competition using reinforcement learning. Journal of Revenue and Pricing Management, 20(6), 646–653.
Published
2025-07-13
How to Cite
Loukili, M., Messaoudi, F., Omar El Aalouche, El Youbi, R., & Riad Loukili. (2025). Adaptive Pricing Strategies in Digital Marketing. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2200
Issue
Section
Research Articles
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