Understand the buying behavior of E-shop customers through appropriate analytical methods

Abstract

Customer satisfaction represents a crucial goal for every seller. In e-commerce, it is possible to increase this factor by a better understanding of customers purchasing behavior based on collected historical data. In a period of a continually growing amount of data, it is not an easy task to effectively pre-process and analyses. Our motivation was to understand the buying behavior of the on-line e-shop customer through appropriate analytical methods. The result is a knowledge set that retailers could use to deliver products to specific customers, to meet their expectations, and to increase his revenues and reputation. For recommendations generation, we used a collaborative filtering method and matrix factorization associated with Singular Value Decomposition (SVD) algorithm. For segmentation, we selected the K-Means algorithm and the RFM method. All methods produced interesting and potentially useful results that will be evaluated and deployed into practice.