Machine Learning Approaches for Adaptive Pricing in E-Commerce to Optimize Profit and Customer Experience
DOI:
https://doi.org/10.64751/Keywords:
Adaptive pricing, E-commerce analytics, Machine learning, Dynamic pricing strategies, Customer behavior analysis, Profit optimization, Demand forecasting, Data-driven decision making, Price optimization algorithms, Recommender systems, Consumer satisfaction, Retail analyticsAbstract
Dynamic pricing is a strategic approach in e-commerce where product prices are adjusted in
real-time based on demand, competition, customer behavior, and market conditions.
Traditional static pricing models fail to respond quickly to rapid market fluctuations, leading
to reduced profitability or customer dissatisfaction. This project proposes a machine learningbased
dynamic pricing system that predicts optimal product prices by analyzing historical
sales data, competitor pricing, seasonal trends, customer purchase patterns, and demand
elasticity. The model aims to balance two critical objectives: maximizing profitability and
maintaining customer satisfaction. By integrating predictive analytics, demand forecasting,
and reinforcement learning techniques, the system dynamically updates prices while ensuring
fairness and transparency. The proposed system improves revenue, enhances customer trust,
and supports sustainable business growth in competitive e-commerce environments.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
