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@article{175531,
        author = {Surendiran Mathivanan and Kritheshvar KRV and Sai Pavan and Sushmitha Bakthavachalam},
        title = {Optimizing Business Intelligence with Reinforcement Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3682-3686},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175531},
        abstract = {Traditional data analytics models struggle to adapt to changing business environments, often relying on historical trends rather than dynamic real-time decision-making. Reinforcement Learning (RL) provides a solution by enabling adaptive decision-making processes that continuously learn from market changes. This paper explores the application of RL to business intelligence, focusing on optimizing pricing strategies in e-commerce through supply-demand fluctuations. By employing algorithms such as Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO), the proposed system dynamically adjusts pricing based on customer behaviour, competitor actions, and seasonal trends. The study demonstrates the effectiveness of RL-based analytics over static models through performance benchmarks in revenue optimization, customer retention, and decision efficiency. Additionally, the role of Explainable AI (XAI) is examined to improve transparency in RL-driven business decisions. This research highlights RL's transformative potential in data analytics, paving the way for scalable, automated, and self-optimizing business intelligence solutions.},
        keywords = {Reinforcement Learning, Data Analytics, Adaptive Business Intelligence, Dynamic Pricing, Machine Learning, Explainable AI, Proximal Policy Optimization, Deep Q-Networks, Markov Decision Process.},
        month = {April},
        }