Internet Banking Member's Transaction Behavior Analysis The Application of Hierarchical Bayesian Model

碩士 === 國立臺北大學 === 統計學系 === 100 === In this study, using Internet banking member’s transaction database, the use of customers on the Internet banking transaction details, calculated relative and absolute activity indicators to distinguish each customer changes in activity on the Internet banking. Thi...

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Main Authors: Chen, Chien-Yao, 陳前堯
Other Authors: Wang, Hong-Long
Format: Others
Language:zh-TW
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/18234315922634309255
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spelling ndltd-TW-100NTPU03370412015-10-13T21:12:26Z http://ndltd.ncl.edu.tw/handle/18234315922634309255 Internet Banking Member's Transaction Behavior Analysis The Application of Hierarchical Bayesian Model 網路銀行會員交易行為分析 – 應用層級貝氏模型建構 Chen, Chien-Yao 陳前堯 碩士 國立臺北大學 統計學系 100 In this study, using Internet banking member’s transaction database, the use of customers on the Internet banking transaction details, calculated relative and absolute activity indicators to distinguish each customer changes in activity on the Internet banking. This study expected to more precise distinction between customer behavior, planning appropriate marketing projects, to improve the effectiveness and efficiency of the use of marketing resources. First use of transaction data to calculate the interpurchase time, using interpurchase time to measure customer activity, further calculate average interpurchase time and weighted average interpurchase time. Comparison the difference between average interpurchase time and weighted average interpurchase time can be calculated the Customer Activity Index (CAI), and CAI relatively active indicators can judge the trend of customer activity. Estimated customer activity, another method is hierarchical Bayesian estimation. Hierarchical Bayesian estimation can solve individual data scarcity problem and correct individual differences at the same time. Coupled with the use of Markov chain Monte Carlo (MCMC) methods to simulate the distribution of each customer’s interpurchase time, in addition to calculate each customer’s average interpurchase time, can still be learned every customer’s interpurchase time variability. The application of the results of this estimate and the aforementioned average interpurchase time, weighted average interpurchase time, and CAI, can produce customer segmentation. Then get the marketing implications, provide important reference information for marketing management. Wang, Hong-Long 王鴻龍 2012 學位論文 ; thesis 61 zh-TW
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language zh-TW
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sources NDLTD
description 碩士 === 國立臺北大學 === 統計學系 === 100 === In this study, using Internet banking member’s transaction database, the use of customers on the Internet banking transaction details, calculated relative and absolute activity indicators to distinguish each customer changes in activity on the Internet banking. This study expected to more precise distinction between customer behavior, planning appropriate marketing projects, to improve the effectiveness and efficiency of the use of marketing resources. First use of transaction data to calculate the interpurchase time, using interpurchase time to measure customer activity, further calculate average interpurchase time and weighted average interpurchase time. Comparison the difference between average interpurchase time and weighted average interpurchase time can be calculated the Customer Activity Index (CAI), and CAI relatively active indicators can judge the trend of customer activity. Estimated customer activity, another method is hierarchical Bayesian estimation. Hierarchical Bayesian estimation can solve individual data scarcity problem and correct individual differences at the same time. Coupled with the use of Markov chain Monte Carlo (MCMC) methods to simulate the distribution of each customer’s interpurchase time, in addition to calculate each customer’s average interpurchase time, can still be learned every customer’s interpurchase time variability. The application of the results of this estimate and the aforementioned average interpurchase time, weighted average interpurchase time, and CAI, can produce customer segmentation. Then get the marketing implications, provide important reference information for marketing management.
author2 Wang, Hong-Long
author_facet Wang, Hong-Long
Chen, Chien-Yao
陳前堯
author Chen, Chien-Yao
陳前堯
spellingShingle Chen, Chien-Yao
陳前堯
Internet Banking Member's Transaction Behavior Analysis The Application of Hierarchical Bayesian Model
author_sort Chen, Chien-Yao
title Internet Banking Member's Transaction Behavior Analysis The Application of Hierarchical Bayesian Model
title_short Internet Banking Member's Transaction Behavior Analysis The Application of Hierarchical Bayesian Model
title_full Internet Banking Member's Transaction Behavior Analysis The Application of Hierarchical Bayesian Model
title_fullStr Internet Banking Member's Transaction Behavior Analysis The Application of Hierarchical Bayesian Model
title_full_unstemmed Internet Banking Member's Transaction Behavior Analysis The Application of Hierarchical Bayesian Model
title_sort internet banking member's transaction behavior analysis the application of hierarchical bayesian model
publishDate 2012
url http://ndltd.ncl.edu.tw/handle/18234315922634309255
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