Demand Forecasting with Deep Learning in Dynamic Pricing in E-commerce Ecosystems
DOI:
https://doi.org/10.63856/ijis/v2i5/00039Keywords:
Deep Learning, Demand Forecasting, Dynamic Pricing, E-commerce, Machine Learning, Pricing Strategy, Business Optimization.Abstract
With the rapidly changing environment in the e-commerce sector, companies are turning to sophisticated technologies to gain an advantage over others. Deep learning is one of these technologies; it has proven very promising for demand forecasting, particularly in dynamic pricing. The paper discusses how deep learning algorithms can be used to predict consumer demand and optimize pricing strategies in real time within e-commerce ecosystems. The paper, through a systematic review of the literature and an empirical case study, demonstrates how deep learning methods can be used to enhance dynamic pricing models, improve pricing accuracy, and increase profitability for businesses. These findings demonstrate that demand forecasting models based on deep learning are superior to standard forecasting models, and provide a significant benefit in decision-making. The paper ends by providing recommendations for ecommerce companies interested in adopting deep learning to implement dynamic pricing, and by discussing how the implementation may be advantageous and challenging.
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