Analyzing E-Commerce Dynamics: Customer Satisfaction, Revenue Prediction, and Sentiment Analysis in Retail

This paper presents an in-depth analysis of customer satisfaction and revenue prediction in the e-commerce sector, focusing on three key areas: the Brazilian e-commerce market, women’s fashion reviews, and a UK-based online retail store. Employing a combination of exploratory data analysis, predictive modeling, and sentiment analysis, the study aims to uncover regional patterns in customer satisfaction within Brazil, determine key factors influencing consumer sentiment in women’s fashion, and predict revenue trends in the UK market. The research methodology includes merging and preprocessing diverse datasets, implementing machine learning models for predictive analytics, and conducting sentiment analysis on customer reviews. The findings provide valuable insights into regional differences in e-commerce, the intricacies of consumer behavior in the fashion industry, and the effectiveness of various predictive models in revenue forecasting. This study contributes to the strategic decision-making processes in e-commerce, enhancing customer experience, and tailoring marketing strategies to meet consumer needs effectively.