Conference Paper

Optimizing E-Commerce Price Negotiation Using an Intelligent Price Negotiator (IPN) with NLP and K-Means Clustering

With the current competition in today's e-commerce market, pricing and negotiation are both neded in imoprving buyers interaction and also maximizing price. Current models for pricing don not takee into consideration to individual customer preference and current market conditions, which leads to to sub-par sales. This paper proposes an Intelligent Price Negotiator (IPN) that collaborates both Natural Language Processing (NLP) and K-Means Clustering to optimize price negotiation. The IPN system applies NLP to translate customer interactions, while K-Means Clustering sorts different segments customers based on diffrent bais which includes purchasing behavior, price sensitivity, and historical transaction activity. The combination of these techniques make price offers more personal, improve conversion rates, and enhance the customers shopping experience. Results gotten from data analysis demonstrate that the proposed IPN model improves consumer satisfaction, priortize pricing effectiveness, and increases retailer profitability as against the traditional fixed price methods. This study contributes to the body of AI-driven e-commerce products, emphasizing the effectiveness of NLP and machine learning for both price optimization and also, customer communication.