Analysis of the Use of Natural Language Processing (NLP) on Customer Experience and Purchase Intention in Marketplaces in Indonesia

E-commerce has become a crucial aspect of modern consumer life, with rapid growth observed in the marketplace sector. In an effort to enhance customer experience and stimulate purchasing interest, this research focuses on the implementation of Natural Language Processing (NLP) for personalized product recommendations on marketplace platforms. The main objective of this research is to identify optimal ways to leverage natural language from customers in understanding individual preferences, thereby improving the relevance and accuracy of provided product recommendations. Through this approach, it is anticipated to strengthen customer purchasing interest, minimize user fatigue, and deliver a more satisfying shopping experience. The research methodology involves the collection of purchase transaction data, search histories, and customer reviews. Natural language processing techniques and sentiment analysis are employed to uncover hidden meanings in customer reviews, while machine learning algorithms are configured to understand individual shopping preference patterns. Anticipated outcomes include improved accuracy and relevance of product recommendations, resulting in a more personalized and satisfying shopping experience. Consequently, customers are expected to be more inclined towards repeat purchases and developing stronger bonds with the marketplace platform. This research not only unveils the potential for increased profits in the marketplace platform but also signifies a significant contribution in understanding the best ways to integrate Natural Language Processing for optimizing personalized product recommendations. The findings of this research are expected to serve as a foundation for the development of more effective and measurable personalization strategies in the e-commerce world.