Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain
Customer churn is one of the challenges of business management in today's complex competitive environment. For this purpose, the organization must have an efficient system to detect and analyze the factors influencing customer churn. To conduct this research, an attempt has been made to build a...
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| Format: | Article |
| Language: | English |
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Ayandegan Institute of Higher Education,
2024-12-01
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| Series: | International Journal of Research in Industrial Engineering |
| Subjects: | |
| Online Access: | https://www.riejournal.com/article_198295_8a2e5d3d33fcc25794e445c8ae6c1737.pdf |
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| _version_ | 1832577725999611904 |
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| author | Mohammad Barzegar Aliakbar Hasani |
| author_facet | Mohammad Barzegar Aliakbar Hasani |
| author_sort | Mohammad Barzegar |
| collection | DOAJ |
| description | Customer churn is one of the challenges of business management in today's complex competitive environment. For this purpose, the organization must have an efficient system to detect and analyze the factors influencing customer churn. To conduct this research, an attempt has been made to build a hybrid model based on data mining approaches from information related to 5830 customers of a chain store (demographic information and information based on customer purchase records) with 17 qualitative and quantitative characteristics. The features of higher importance were identified to build the model in the first stage using the logistic regression algorithm. In the second stage, the support vector machine algorithm, a critical supervised learning algorithm, was used to classify the customers and rank the essential features. Finally, the proposed model has been implemented as a case study in the chain store industry. The results indicate the optimal efficiency of the proposed analysis method. This research has been done to identify the influential factors in customer churn and focus on providing new solutions to reduce churn in the retail industry. Also, the results show that age, marital status, and average monthly income from the set of demographic features and how to get to know the store, the share of online shopping, and special sales from the set of features related to customer transaction records are among the most important factors affecting customer churn. In addition, practical suggestions have been presented that can be used for tactical and strategic planning of chain stores to attract and retain customers. |
| format | Article |
| id | doaj-art-26359535c2b64177938ec17a0f159a55 |
| institution | Kabale University |
| issn | 2783-1337 2717-2937 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | International Journal of Research in Industrial Engineering |
| spelling | doaj-art-26359535c2b64177938ec17a0f159a552025-01-30T15:10:43ZengAyandegan Institute of Higher Education,International Journal of Research in Industrial Engineering2783-13372717-29372024-12-0113438439810.22105/riej.2024.427655.1404198295Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chainMohammad Barzegar0Aliakbar Hasani1Department of Industrial Engineering and Management, Shahrood University of Technology, Iran.Department of Industrial Engineering and Management, Shahrood University of Technology, Iran.Customer churn is one of the challenges of business management in today's complex competitive environment. For this purpose, the organization must have an efficient system to detect and analyze the factors influencing customer churn. To conduct this research, an attempt has been made to build a hybrid model based on data mining approaches from information related to 5830 customers of a chain store (demographic information and information based on customer purchase records) with 17 qualitative and quantitative characteristics. The features of higher importance were identified to build the model in the first stage using the logistic regression algorithm. In the second stage, the support vector machine algorithm, a critical supervised learning algorithm, was used to classify the customers and rank the essential features. Finally, the proposed model has been implemented as a case study in the chain store industry. The results indicate the optimal efficiency of the proposed analysis method. This research has been done to identify the influential factors in customer churn and focus on providing new solutions to reduce churn in the retail industry. Also, the results show that age, marital status, and average monthly income from the set of demographic features and how to get to know the store, the share of online shopping, and special sales from the set of features related to customer transaction records are among the most important factors affecting customer churn. In addition, practical suggestions have been presented that can be used for tactical and strategic planning of chain stores to attract and retain customers.https://www.riejournal.com/article_198295_8a2e5d3d33fcc25794e445c8ae6c1737.pdfcustomer churndata mininglogistic regressionsupport vector machinemachine learning |
| spellingShingle | Mohammad Barzegar Aliakbar Hasani Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain International Journal of Research in Industrial Engineering customer churn data mining logistic regression support vector machine machine learning |
| title | Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain |
| title_full | Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain |
| title_fullStr | Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain |
| title_full_unstemmed | Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain |
| title_short | Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain |
| title_sort | analyzing customer churn behavior using datamining approach hybrid support vector machine and logistic regression in retail chain |
| topic | customer churn data mining logistic regression support vector machine machine learning |
| url | https://www.riejournal.com/article_198295_8a2e5d3d33fcc25794e445c8ae6c1737.pdf |
| work_keys_str_mv | AT mohammadbarzegar analyzingcustomerchurnbehaviorusingdataminingapproachhybridsupportvectormachineandlogisticregressioninretailchain AT aliakbarhasani analyzingcustomerchurnbehaviorusingdataminingapproachhybridsupportvectormachineandlogisticregressioninretailchain |