Customer Satisfaction Prediction in Online Goods Delivery Through Interpretable Predictive Models and Sentiment Analysis

In this paper, we explore the effectiveness of machine learning (ML) in forecasting customer satisfaction scores based on the sales dataset curated from Olist, a prominent Brazilian e-commerce enterprise. Customer satisfaction score is classified into four distinct categories: Poor, Average, Good, and Excellent, with a prevalence of Excellent ratings among the majority of sales orders. Motivated by the recognition that delivery duration and product, seller rating scores, derived from previous customer transactions, play pivotal roles in shaping customer satisfaction, we embark on a comprehensive analysis. In our investigation, we leverage advanced machine learning techniques, specifically Random Forest (RF), XGBoost (XGB), and Decision Tree (DT) to forecast customer satisfaction scores. Additionally, we incorporated sentiment analysis of review comments into our research. Notably, the XGBoost (XGB) model emerged as the top performer, achieving an average precision, recall, and macro F1-score of 0.53,0.52 and 0.53 respectively. This underscores the effectiveness of incorporating sentiment analysis alongside traditional ML models in predicting customer satisfaction in e-commerce settings.