A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis
Customer retention is invariably the top priority of all consumer businesses, and certainly it is one of the most critical challenges as well. Identifying and gaining insights into the most probable cause of churn can save from five to ten times in terms of cost for the company compared with finding...
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doaj-95da829e941c485b8da36f2a8776f7aa2020-11-25T01:37:50ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732019-01-01201910.1155/2019/92528379252837A New Approach for Construction of Geodemographic Segmentation Model and Prediction AnalysisHoang Viet Long0Le Hoang Son1Manju Khari2Kanika Arora3Siddharth Chopra4Raghvendra Kumar5Tuong Le6Sung Wook Baik7Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, VietnamVNU Information Technology Institute, Vietnam National University, Hanoi, VietnamDepartment of Computer Science and Engineering, AIACT&R, New Delhi, IndiaDepartment of Computer Science and Engineering, AIACT&R, New Delhi, IndiaDepartment of Computer Science and Engineering, AIACT&R, New Delhi, IndiaComputer Science and Engineering Department, LNCT College, Bhopal, MP, IndiaDigital Contents Research Institute, Sejong University, Seoul 143-747, Republic of KoreaDigital Contents Research Institute, Sejong University, Seoul 143-747, Republic of KoreaCustomer retention is invariably the top priority of all consumer businesses, and certainly it is one of the most critical challenges as well. Identifying and gaining insights into the most probable cause of churn can save from five to ten times in terms of cost for the company compared with finding new customers. Therefore, this study introduces a full-fledged geodemographic segmentation model, assessing it, testing it, and deriving insights from it. A bank dataset consisting 11,000 instances, which consists of 10,000 instances for training and 10,000 instances for testing, with 14 attributes, has been used, and the likelihood of a person staying with the bank or leaving the bank is computed with the help of logistic regression. Base on the proposed model, insights are drawn and recommendations are provided. Stepwise logistic regression methods, namely, backward elimination method, forward selection method, and bidirectional model are constructed and contrasted to choose the best among them. Future forecasting of the models has been done by using cumulative accuracy profile (CAP) curve analysis.http://dx.doi.org/10.1155/2019/9252837 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hoang Viet Long Le Hoang Son Manju Khari Kanika Arora Siddharth Chopra Raghvendra Kumar Tuong Le Sung Wook Baik |
spellingShingle |
Hoang Viet Long Le Hoang Son Manju Khari Kanika Arora Siddharth Chopra Raghvendra Kumar Tuong Le Sung Wook Baik A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis Computational Intelligence and Neuroscience |
author_facet |
Hoang Viet Long Le Hoang Son Manju Khari Kanika Arora Siddharth Chopra Raghvendra Kumar Tuong Le Sung Wook Baik |
author_sort |
Hoang Viet Long |
title |
A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis |
title_short |
A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis |
title_full |
A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis |
title_fullStr |
A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis |
title_full_unstemmed |
A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis |
title_sort |
new approach for construction of geodemographic segmentation model and prediction analysis |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2019-01-01 |
description |
Customer retention is invariably the top priority of all consumer businesses, and certainly it is one of the most critical challenges as well. Identifying and gaining insights into the most probable cause of churn can save from five to ten times in terms of cost for the company compared with finding new customers. Therefore, this study introduces a full-fledged geodemographic segmentation model, assessing it, testing it, and deriving insights from it. A bank dataset consisting 11,000 instances, which consists of 10,000 instances for training and 10,000 instances for testing, with 14 attributes, has been used, and the likelihood of a person staying with the bank or leaving the bank is computed with the help of logistic regression. Base on the proposed model, insights are drawn and recommendations are provided. Stepwise logistic regression methods, namely, backward elimination method, forward selection method, and bidirectional model are constructed and contrasted to choose the best among them. Future forecasting of the models has been done by using cumulative accuracy profile (CAP) curve analysis. |
url |
http://dx.doi.org/10.1155/2019/9252837 |
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