Optimising e-commerce customer satisfaction with machine learning

Abstract

Customer insighs is the key to the success of e-commerce. Therefore, factors affecting customer satisfaction leading to product purchase and re-purchase should be studied extensively. This study intends to identify the key drivers that influence the satisfaction and the model which can predict the likelihood of customer satisfaction. The outcome would provide insights to prioritise factors that are significant, as well as to provide advice to a wide range of sellers. Four classification machine learning algorithms decision tree, random forest, artificial neural network and support vector machine are evaluated to classify customer satisfaction based on a 3-year historical data from an e-commerce retailer. There were a few challenges with the dataset, such as imbalanced, skewed and missing. Data pre-processing was conducted, and different techniques were evaluated. Of the algorithms evaluated, the best result is achieved by Random Forest with the highest accuracy and reasonable processing time. Meeting the estimated delivery date and the number of days taken to deliver an order is found to be the top two important factors affecting customer satisfaction.

A Smart Model to enhance Customer retention in E-commerce

“…The Results showed that Ada boost achieves the highest accuracy, clustering before prediction improve the prediction accuracy such as Ada Boost accuracy before segmentation was 94% but after segmentation increased to 95% and also found out that cluster I important to analyze because non churn rate was higher compared to cluster II and III customers. Anne-Nee Wong and Booma Poolan Marikannan in [23], highlighted the importance of customer feedback in ecommerce success. They introduced a model that analyzed factors affecting customer satisfaction using four classification algorithms-Random Forest, Support Vector Machine (SVM), Decision Tree, and Artificial Neural Network-applied to a Brazilian E-commerce dataset.…”