Fuzzy-Based Mining Framework of Browsing Behavior to Enhance E-commerce Website Performance: Case Study from Kelkoo.com

Abstract

Existing data mining techniques has been used to find out which online products are relevant in terms of having high sales. There has not been much work done to ensure online customer satisfaction by analyzing its click stream data to enhance e-business. This paper thus proposes a fuzzy data mining model for extracting membership functions from navigational data for identifying fuzzy orientation of customer's behavior on the website features. Features selection technique is also applied to properly track and analyze the adequate e-customer's click data. The usefulness of the proposed approach has been studied by applying it to the European leader in e-commerce advertising, "Kelkoo" which helps merchants advertise their products to consumers. The results have made possible to propose some improvements of the website's features or to choose the most suited e-commerce advertising website to publish their products.