A New Model for Predicting the Probability of Product Return in Online Shopping
One of the current important issues for online stores regarding online shopping is customer satisfaction from online shopping. Many of the customers who purchase their products online from electronic stores are not satisfied with the products they receive. Some of the reliable online stores follow the policy of "product returns" to increase their customers' satisfaction. According to this policy, if the customers are not satisfied with the products they have received, they can return them to the store under predetermined conditions. Therefore, the purpose of this paper is to present a model based on the factors influencing online customers' satisfaction during online shopping. The presented model in this study is based on the data obtained from eBay Store using data mining and SPSS Modeler. Using this method and well-known algorithms such as CHAID and C&R Tree, and C5.0, a model is created that can predict the order returns at high accuracy. The investigations showed that the values of cross-validation for the accuracy of the models created by CHAID, C&R Tree and C5.0 algorithms on the data set of the three algorithms were 78.6 – 80.1 percent, which confirmed the accuracy of the final model. Research results showed that prediction of the probability of product returns is, in fact, in line with the estimation of the maximum logical costs which determine the logical number of messages and call duration for any product with specific profit and cost so that product returns can be prevented.
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