A Novel Approach to Collect and Analyze Market Customer Behavior Data on Online Shop

Abstract

Online shopping activities are currently experiencing a significant increase due to the development of the reach of the internet services and the changed activities from offline to online. The data analysis generated from online shopping activities is necessary to determine the right sales strategy. One of the types of data that needs to be analyzed is market consumer behavior data in online shops, generated in the form of clickstream data. Currently, there was not any research that examines the determination of clickstream data components, clickstream data recording mechanism, and how to analyze clickstream data components in online shops properly. This paper describes the architecture of the proposed online shop application, the module to record the clickstream data, and the method to analyze the clickstream data. Based on our evaluation, eight clickstream data components were successfully recorded in the database using Asynchronous JavaScript and XML (AJAX) technology. The clickstream data component that contains market customer behavior data is Multi-Criteria Decision Making (MCDM) data and has been analyzed using the Simple Additive Weighting (SAW) ranking method.