A Multi-Parameter Data-Driven Dynamic Pricing Framework Using Machine Learning for Revenue Optimization

We propose a multi-parameter data-driven dynamic pricing framework for revenue optimization, which integrates diverse influencing factors beyond traditional demand-based approaches. The framework employs a hybrid machine learning architecture, combining predictive analytics with real-time adaptive decision-making to dynamically adjust prices. A Long Short-Term Memory (LSTM) network captures temporal dependencies in demand variability, customer behavior, competitor pricing, inventory levels, and seasonality, while a feed-forward neural network translates these insights into actionable price adjustments. The model incorporates customer-centric optimization by balancing revenue objectives with satisfaction metrics, ensuring ethical pricing through fairness-aware constraints. Moreover, the framework addresses the limitations of static pricing strategies by continuously updating prices in response to streaming data, thereby improving responsiveness to market fluctuations. The novelty lies in the holistic integration of multi-parameter inputs and the hybrid learning approach, which enhances both accuracy and adaptability. Experimental validation demonstrates significant revenue improvements compared to conventional methods, highlighting the practical applicability of the proposed framework in real-world scenarios. This work contributes to the growing body of research on data-driven pricing by offering a scalable and ethically grounded solution for dynamic revenue optimization.

Dynamic Pricing, Multi-Parameter Optimization, Hybrid Machine Learning

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