Authors : Dileep Kumar Padidem and C. Nalini
Abstract: The dynamic nature of web creates large massive volumes of information in structured and semi structured nature. The dynamic nature of web and its growing importance as an economic platform is in need of new methods and tools to improve business efficiency in this ecommerce world. So many research results has been produced using web analytics study which observes customers behavior through click stream behavior and market basket analysis which will not provide critical path of site visitors behavior and abstracted view of underlying customer processes. We propose of applying Business Process Methodologies (BPM) to event logs of ecommerce websites to study the challenges and potential benefits of such an approach. The method of general web access pattern is extracted and analyzed using knowledge discovery techniques to understand the usage patterns of the customers. This study have a clear insight of process mining, observation of web usage by customers (click stream data ) as sequence of tasks and analysis and study on classification of users four important shopping behavior as bargain shopper, surgical shopper, enthusiast shopper and power shopper. The workflow model of these four types of shoppers and their real time behavior are analyzed using process mining tool.
Dileep Kumar Padidem and C. Nalini, 2017. A Study on Classification of Users Shopping Behavior Process Model Using Click Stream Data. Journal of Engineering and Applied Sciences, 12: 9548-9553.