Data Mining in Churn Analysis for Hotel Customers
碩士 === 銘傳大學 === 資訊工程學系碩士班 === 107 === With the development of the online world and the increasing scale of online users, the online booking hotel market has great potential to be discovered. Due to the increasing size of the booking market, it has gradually attracted the attention and use of local p...
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ndltd-TW-107MCU003920092019-09-17T03:40:08Z http://ndltd.ncl.edu.tw/handle/un4vn5 Data Mining in Churn Analysis for Hotel Customers 資料探勘於旅店客戶流失之研究 TSAI, YU-RUI 蔡育叡 碩士 銘傳大學 資訊工程學系碩士班 107 With the development of the online world and the increasing scale of online users, the online booking hotel market has great potential to be discovered. Due to the increasing size of the booking market, it has gradually attracted the attention and use of local people. According to the data, the market share of Ctrip.com currently accounts for more than 50% of the market in China's online hotel bookings, which is quite large. It is of great significance to study Ctrip.com, a leading online travel booking service company in China, to identify the key factors of its user loss and prevent the business crisis caused by customer loss. Based on the theory of customer churn, this paper first preprocesses the data and then uses the existing features to create new features. Then it uses the XGBoost algorithm in the data mining method, the random forest algorithm, and the neural network algorithm, constructing the model of customer churn based on Ctrip.com data, and evaluating the performance of these models. Secondly, according to the optimal model constructed, the key factors leading to the loss of Ctrip customers are analyzed. Finally, based on the results of the analysis, suggestions for reducing the loss of Ctrip customers are proposed. LEE, YUE-SHI 李御璽 2019 學位論文 ; thesis 53 zh-TW |
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碩士 === 銘傳大學 === 資訊工程學系碩士班 === 107 === With the development of the online world and the increasing scale of online users, the online booking hotel market has great potential to be discovered. Due to the increasing size of the booking market, it has gradually attracted the attention and use of local people. According to the data, the market share of Ctrip.com currently accounts for more than 50% of the market in China's online hotel bookings, which is quite large. It is of great significance to study Ctrip.com, a leading online travel booking service company in China, to identify the key factors of its user loss and prevent the business crisis caused by customer loss.
Based on the theory of customer churn, this paper first preprocesses the data and then uses the existing features to create new features. Then it uses the XGBoost algorithm in the data mining method, the random forest algorithm, and the neural network algorithm, constructing the model of customer churn based on Ctrip.com data, and evaluating the performance of these models. Secondly, according to the optimal model constructed, the key factors leading to the loss of Ctrip customers are analyzed. Finally, based on the results of the analysis, suggestions for reducing the loss of Ctrip customers are proposed.
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author2 |
LEE, YUE-SHI |
author_facet |
LEE, YUE-SHI TSAI, YU-RUI 蔡育叡 |
author |
TSAI, YU-RUI 蔡育叡 |
spellingShingle |
TSAI, YU-RUI 蔡育叡 Data Mining in Churn Analysis for Hotel Customers |
author_sort |
TSAI, YU-RUI |
title |
Data Mining in Churn Analysis for Hotel Customers |
title_short |
Data Mining in Churn Analysis for Hotel Customers |
title_full |
Data Mining in Churn Analysis for Hotel Customers |
title_fullStr |
Data Mining in Churn Analysis for Hotel Customers |
title_full_unstemmed |
Data Mining in Churn Analysis for Hotel Customers |
title_sort |
data mining in churn analysis for hotel customers |
publishDate |
2019 |
url |
http://ndltd.ncl.edu.tw/handle/un4vn5 |
work_keys_str_mv |
AT tsaiyurui datamininginchurnanalysisforhotelcustomers AT càiyùruì datamininginchurnanalysisforhotelcustomers AT tsaiyurui zīliàotànkānyúlǚdiànkèhùliúshīzhīyánjiū AT càiyùruì zīliàotànkānyúlǚdiànkèhùliúshīzhīyánjiū |
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1719250853282447360 |