Predicting Customer Churn in the Insurance Industry: Identifying the Influential Factors

Subject Areas : Journal of Investment Knowledge

samaneh  soltani Lifshagerd ¹ (Ph.D. Student in Business Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Kambiz  Shahroodi ² (Associate Professor, Department of Business Management, Faculty of Management and Accounting, Islamic Azad University of Rasht Branch, Rasht, Iran)
Ebrahim  Chirani ³ (Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.)

Received: 2019-09-23 Accepted : 2019-10-22 Published : 2021-09-23

Keywords: Customer Churn, Customer Relationship Management, Forecasting,

Abstract :

Iran insurance industry has recently faced with various problems regarding fluctuations in profitability, portfolio composition, the rate of loss, the rate of penetration, retention and satisfaction of insurers and market share, due to presence of numerous insurance companies in the competitive market. As a result, insurer maintenance has become a major goal for most of the insurance companies. Since in the insurance industry, like many other industries, the cost of searching for new insurers is far more expensive than retaining the current insurers, it is essential to identify the factors that drive insurers to churn. The purpose of this study is to investigate the literature and research background in the field of customer churn, which ultimately leads to identifying and classifying “influential factors in predicting customer churn in the insurance industry”. A systematic literature review method is used to collect and review previous studies by integrating automated and manual search strategies of all the related research articles in this field, published for the period 1389 to 1398 for Persian articles and 2010 to 2018 for English articles. The research findings identified 85 factors that affect customer churn, specifically in the insurance industry. They are classified into four categories; the factors related to the insurer, the factors related to the insuree, product/service related factors and factors regarding the relationship between the insurer and the insuree.

References:

• حسین خانی نوشین، حسینی مطلق سید مهدی و خاکزار بفروئی مرتضی، « شناسایی عوامل موثر بر رویگردانی مشتریان در صنعت بیمه»، همایش بین المللی بیمه و توسعه، 1393


• Amin, A., Al-Obeidatb, F., Shahab, B., Adnana, A., Loo, J., & Anwar, S. (2018). Customer churn prediction in telecommunication industry using data certainty. Journal of Business Research, 1 - 12.


• Amin , A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Haung, K. (2016). Customer Churn Prediction in Telecommunication Sector Using Rough Set Approach. Neurocomputing, http://dx.doi.org/10.1016/j.neucom.2016.12.009.


• De Caigny, A., Coussement, K., & W. De Bock, K. (2018). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research 269, 760-772.


• Chen, S.-H. (2016). The Gamma CUSUM Chart Method for Online Customer Churn Prediction. Electronic Commerce Research and Application 17, 99-111.


• Gordono, N., & Veglio, V. (2016). Customers Churn Prediction and Marketing Retention Strategies. An Application of Support Vector Machines Based on the AUC Parameter-selection Technique in B2B E-commerce Industry. Industrial Marketing Management, http://dx.doi.org/10.1016/j.indmarman.2016.08.003.


• Hendrikse, K. (2017). “Predicting Customer Churn in the Healthcare Insurance Market: A Case Study”, Master Thesis, Operations Management and Logistics, Eindhoven University of Technology, 2017, https://pure.tue.nl/ws/files/76306184/Master_Thesis_Kim_Hendrikse.pdf


• Holtrop, N., E. Wieringa, J., J. Gijsenberg, M., & C. Verhoef, P. (2016). No Future Without the Past? Churning in th Face of Customer Privacy. International Journal of Research in Marketing, doi: 10.1016/j.ijresmar.2016.06.001.


• Huigevoort, C. (2015). “Customer Churn Prediction for an Insurance Company”, Master Thesis, Operations Management and Logistics, Eindhoven University of Technology, 2015, https://pdfs.semanticscholar.org/2028/6dfd510e6cb5c374384f415aab12b4b87132.pdf


• Khodabandelou, S., & Zivari Rahman, M. (2017). Comparison of Supervised Machine Learning Techniques for Customer Churn Predicion based on Analysis of Customer Behavior. Journal of Systems and Information Technology, Vol. 19, Issue:1/2, 65-93.


• Lee, E.-B., Kim, J., & Lee, S.-G. (2017). Predicting Customer Churn in Mobile Industry Using Data Mining Technology. Industrial Management & Data Systems, Vol. 117 Iss pp. 90 - 109 . http://dx.doi.org/10.1108/IMDS-12-2015-0509.


• Oskarsdottir, M., Van Calster, T., Baesens, B., Lemahieu, W., & Vanthienen, J. (2018). Time series for early churn detection: Using similarity based classification for dynamic networks. Expert Systems With Applications, 106, 55-65.


• Postigo-Boix, M., & Melus-Moreno, J. (2018). A Social Model Based on Customers' Profiles for Analyzing the Churning Process in the Mobile Market of Data Plans. Physica A, https://doi.org/10.1016/j.physa.2017.12.121.


• Ravi, K., Ravi, V., & Krishna Prasad, P. (2017). Fuzzy Formal Concept Analaysis Based Opinion Mining for CRM in Financial Services. Applied Soft Computing.


• Sahlberg, E. E. (2018). “Modeling Customer Churn among Movers in the Finnish Insurance Market”, Master Thesis, Economics, University of Helsinki, 2018, https://pdfs.semanticscholar.org/f2c8/178a274a5d4f623b4e1269a245be2a88d7bf.pdf?_ga=2.167502334.130163482.1568527409-1782194187.1556699392


• Siemes, T. (2016). Churn Prediction Models Tested and Evaluated in the Dutch Indemnity Industry. Open University of the Netherlands Faculty Management, Science and Technology.