Predicting Consumers’ Purchase Decision by Clickstream Data: A Machine Learning Approach

碩士 === 國立臺灣大學 === 經濟學研究所 === 106 === 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 clickstrea...

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Bibliographic Details
Main Authors:	Po Chu Chen, 陳伯駒
Other Authors:	林明仁
Format:	Others
Language:	en_US
Published:	2018
Online Access:	http://ndltd.ncl.edu.tw/handle/cdk6y2

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spelling	ndltd-TW-106NTU053890552019-05-30T03:50:57Z http://ndltd.ncl.edu.tw/handle/cdk6y2 Predicting Consumers’ Purchase Decision by Clickstream Data: A Machine Learning Approach 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 Po Chu Chen 陳伯駒碩士國立臺灣大學經濟學研究所 106 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. 林明仁 2018 學位論文 ; thesis 36 en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
description	碩士 === 國立臺灣大學 === 經濟學研究所 === 106 === 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.
author2	林明仁
author_facet	林明仁 Po Chu Chen 陳伯駒
author	Po Chu Chen 陳伯駒
spellingShingle	Po Chu Chen 陳伯駒 Predicting Consumers’ Purchase Decision by Clickstream Data: A Machine Learning Approach
author_sort	Po Chu Chen
title	Predicting Consumers’ Purchase Decision by Clickstream Data: A Machine Learning Approach
title_short	Predicting Consumers’ Purchase Decision by Clickstream Data: A Machine Learning Approach
title_full	Predicting Consumers’ Purchase Decision by Clickstream Data: A Machine Learning Approach
title_fullStr	Predicting Consumers’ Purchase Decision by Clickstream Data: A Machine Learning Approach
title_full_unstemmed	Predicting Consumers’ Purchase Decision by Clickstream Data: A Machine Learning Approach
title_sort	predicting consumers’ purchase decision by clickstream data: a machine learning approach
publishDate	2018
url	http://ndltd.ncl.edu.tw/handle/cdk6y2
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Predicting Consumers’ Purchase Decision by Clickstream Data: A Machine Learning Approach

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