Data-Driven Pricing Strategies

Abstract

In store environments with a lot of competition, dynamic and competitive pricing has become an important way to make the most revenue while gaining the largest percentage of the market. Cost-plus and steady margin pricing models are becoming less useful because they are unable to take into account how competitors are reacting in real time, how demand changes over time, and how price sensitivity varies by market group. Long Short-Term Memory (LSTM) for precise price prediction as well as Reinforcement Learning (RL) for real-time pricing optimisation are integrated in this research's data-driven dynamic pricing architecture. To pick features such as demand elasticity, promotional impacts, and temporal patterns, a publicly accessible retail dataset was preprocessed and enhanced with rival pricing, sales volumes, and seasonal indicators. Results from comparative tests showed that the suggested Machine Learning (ML) framework performed noticeably better than conventional pricing. LSTM reduced mistakes by almost 51% as compared to static models,