Paper Title
Predicting Gender Of Online Customer Using Artificial Neural Networks

Customer age and gender are very important parameters for both retailing and marketing. It is well known that they both play very important roles in purchasing habits. In this study, we propose a model to predict the gender of an online customer by analysing his/her mouse movements. To accomplish this purpose, we have developed a novel data cube model. The model consists of six dimensions which are customer demographic data, customer visits, mouse movements, online shopping cart, external data and time dimension. To detect customer gender we used artificial neural network model. Our results show that using the derivatives of the data cube and the model, gender of an online customer may be predicted with up to 80% of success rate. In the study we have also applied a data mining decision tree analysis in order to find the most significant parameters for detecting an online customer’s gender. Our analysis shows that time spent on the site, average time intervals between clicks, items clicked and order of the clicks are important and can be used to predict online user gender and it may be used for promotional and marketing purposes. Keywords - Gender, Prediction, Artificial Neural Networks, Data Mining, Marketing.