使用機器學習技法預測消費者的購買行為:以網站的點擊資料為例

Abstract

近年來,許多商業型態逐漸由實體商店轉型成網路商店,即我們所熟 知的電子商務。隨著網路運算的進步,這些網路平台儲存了鉅量的訪 客登入及瀏覽資訊,亦稱為點擊資料。在本研究中,我們主要分析一 家線上酒店零售商的網站點擊資料、使用兩種常用的機器學習模型: 決策樹與隨機森林,預測消費者最終的購買行為。除了引入消費者在 網站上的搜尋特徵,我們另外建立了一種根據消費者之登入頁面順序、 進行訪客分類的分群結果,並利用此分群結果作為統整性的特徵納入 學習模型。經過重複採樣消除非平衡數據的問題後,我們兩個最終的 學習模型都達到高於 90% 的整體預測率,並且提供了廠商未來可能進 一步行銷的訪客類型。

In the recent years, numerous commerces have gradually shifted from physi- cal store to web-shops, so-called the e-commerce. These online stores contain lots of log files in the back-end which basically record the pages accessed by visitors, namely the clickstream data. In this study, we predict consumers’ purchase decision by analyzing the clickstream data from an online wine re- tailer. We impose two modern machine learning model, decision tree and ran- dom forest, to predict consumers’ final purchase intention. Besides the normal features based on visitors’ activities on the website, we construct a new feature that clusters different groups of visitors according to the sequence page-type accessed. After re-sampling to remedy the unbalanced data, our two models both show high predictive accuracy up to 90% and provides a new insight for retailer to target some specific visitors on website.