Predicting Online Purchasing Behavior Using Clickstream Data

• Main Authors: Ching-Lun Su, 蘇敬倫
• Other Authors: Chung-Ying Lee
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/nnmd7f

碩士 === 國立臺灣大學 === 經濟學研究所 === 106 === Online shopping has been booming in recent ten years. It is now a critical issue for online retailers how to make good use of the rich data generated in the process of online shopping. Online retailers cannot observe physical characteristics of the customers, such as gender and age. But they can use browsing data to analyze customers’ preferences and predict purchasing behavior. This study explores the relationships between browsing behavior, customer characteristics, and purchase results using clickstream data from the website of an online wine retailer. I use a K-Means model to cluster customers based on the filters they chose when browsing the website. I find the clustering results are significantly correlated with customers’ location and gender. Also, the more filters a customer choose before a purchase, the more wines they buy and the higher their order total. The results of logistic regressions show that customers who choose a low price range to filter products are most likely to buy.