ANALYZING AND PREDICTING B2B CUSTOMER REPURCHASING INTENTIONS USING MACHINE LEARNING

Abstract

In the present business environment, organizations are realizing that customer repurchase intention is a pivotal aspect of the growth of their business. This research focuses on the repurchasing intention of the business-to-business (B2B) customers, the factors that influence this decision, and the use of models that could predict such a decision. Therefore, satisfaction, retention, relation, after sales service, perceived value, and loyalty to outline repurchase intentions are considered. Since this is an analytical research design, the key methods used in the research include SHAP analysis, random forest regression, and correlation heatmap analysis. The questionnaires were distributed among the existing customers; the research involved 175 industries at the Ahmedabad district of Gujarat out of which 76 responses were received which indicates the sample size percentage of 43%. The model also had a good predictive value with the R-squared value, which was equal to 0. 986 on the training dataset. The performance was 84.4% for the test dataset. Among the analysed factors, Perceived Value came out as the leading factor and manifested its great influence on the customer repurchasing process. The research states that the areas for improvement should be directed on Customer Satisfaction and Service & Support respectively in order to match customer repurchasing.