Prediction of Bank Customers’ Partial Churn Using State Chain Model
Banks are endeavoring to gain more funds in a highly competitive environment. Given the higher costs of attracting new customers than retaining existing ones, most banks focus on maintaining their existing customers. Therefore, it is quite important for the banks to predict the customer churn in adv...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
Allameh Tabataba'i University Press
2019-08-01
|
| Series: | مطالعات مدیریت کسب و کار هوشمند |
| Subjects: | |
| Online Access: | https://ims.atu.ac.ir/article_10230_9ee03f1cbdf72da99325e31685f84b10.pdf |
| Summary: | Banks are endeavoring to gain more funds in a highly competitive environment. Given the higher costs of attracting new customers than retaining existing ones, most banks focus on maintaining their existing customers. Therefore, it is quite important for the banks to predict the customer churn in advance. In almost all related research works in banking, customers are divided into two types of static categories: “churners” and “loyal” customers. However, due to the nature of banking particularly in Iran, it is necessary to define churn in a dynamic manner in a variety of circumstances. In this study, the concept of state chain is introduced, which identifies changes in customers’ partial churn status over time. Using the sequence of chains and a combination of hierarchical clustering techniques as well as support vector machine, a model was developed to predict partial churn of bank customers. To construct a practical sample and to evaluate the prediction accuracy, 5 years of real European bank customers’ data as well as 3 years of customers’ data from three different Iranian banks were used. The results indicate a high level of prediction accuracy for the model in all 4 banks, particularly when longer sequences of states are used. |
|---|---|
| ISSN: | 2821-0964 2821-0816 |