A Mathematical Model for Customer Behavior Prediction Based on Click Stream Analysis

Click stream analysis is known as an effective method for customer’s viewing route prediction in a particular web site. Predicting Customer viewing behavior provides considerable advantages in different areas such as e-commerce, e-business and customer relationship management. This paper aims to provide a 0-1 mathematical model based on Markov models for evaluating the most probable viewing route of a customer in a website. This problem can be formulated as an especial case of well-known Prize Collecting Traveling Salesman Problem (PCTSP) which is a NP-hard problem and its sub tour elimination constraints are increased drastically by increasing the model parameters. Also an effective algorithm is introduced in this paper to solve this NP-hard model. For model validation, the proposed model was implemented by using the log files of a university web site server for 20 different users. Comparison of the results with commonly used Giudici algorithm shows that the proposed model yields better and exacter solutions.