以點擊流資料預測線上購物行為

Abstract

近十年來，網路購物趨勢蓬勃發展，如何利用網路購物過程產生的豐富數據，成為網路零售重要議題。網路零售商無法觀察到顧客的性別、年齡等實體特徵，卻可能透過瀏覽數據，分析顧客偏好，藉此預測購物行為。本研究以線上葡萄酒零售商的點擊流資料，探討瀏覽行為、顧客特徵與購買結果之間的關係。透過K-Means模型，將顧客依照選擇的篩選商品條件分群，發現分群結果與顧客所在地和性別有顯著關聯。此外，選擇越多篩選商品條件的顧客，購買量與總價越高。邏輯迴歸結果顯示，利用低價格區間作為篩選商品條件的顧客購買機率最高。

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.