Analysis and prediction of e-customers' behavior by mining clickstream data - Silahtaroğlu, Gökhan; Dönertaşlı, Hale (IEEE, 2015)
In a regular retail shop the behavior of customers may yield a lot to the shop assistant. However, when it comes to online shopping it is not possible to see and analyze customer behavior such as facial mimics, products they check or touch etc. In this case, clickstreams or the mouse movements of e-customers may provide some hints about their buying behavior. In this study, we have presented a model to analyze clickstreams of e-customers and extract information and make predictions about their shopping behavior on a digital market place. After collecting data from an e-commerce market in Turkey, we performed a data mining application and extracted online customers' behavior patterns about buying or not. The model we present predicts whether customers will or will not buy their items added to shopping baskets on a digital market place. For the analysis, decision tree and multi-layer neural network prediction data mining models have been used. Findings have been discussed in the conclusion.
IEEE International Conference on Big Data -- OCT 29-November 01, 2015 -- Santa Clara, California
WOS: 000380404600176
Keywords: Data Mining; Clickstream; E- Customer; Customer Behavior; Digital Market; Prediction