بخش‌بندی خوشه‌ای و مدل ارتباطی ال آر اف ام مشتریان صنعت بیمه | ||
| کاوش‌های مدیریت بازرگانی | ||
| مقاله 20، دوره 13، شماره 25، شهریور 1400، صفحه 557-585 اصل مقاله (868.19 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/bar.2021.15232.3764 | ||
| نویسندگان | ||
| ابوالقاسم ابراهیمی1؛ کاظم عسکری فر2؛ امین نیکبخت* 3 | ||
| 1دانشیار گروه مدیریت، دانشکده اقتصاد، مدیریت و علوم اجتماعی دانشگاه شیراز | ||
| 2استادیار گروه مدیریت، دانشکده اقتصاد، مدیریت و علوم اجتماعی دانشگاه شیراز | ||
| 3کارشناس ارشد مدیریت بازرگانی، دانشکده اقتصاد، مدیریت و علوم اجتماعی، دانشگاه شیراز | ||
| چکیده | ||
| در صنعت بیمه شناخت و دسته‌بندی نظام‌مند مشتریان نه‌تنها برای بازاریابان، بلکه برای کل سازمان یک دغدغه اساسی است. هدف این پژوهش به عنوان یک تحقیق کاربردی، توصیفی و کمّی، خوشه‌بندی مشتریان با رویکرد داده‌کاوی (کی میانگین) می‌باشد. جامعه آماری پژوهش مشتریان بیمه پاسارگاد در شهر شیراز بوده که 800 نفر به صورت تصادفی انتخاب و با رعایت اصول اخلاق پژوهشی، داده‌های چهار عامل مدت ارتباط، تازگی، تعداد تراکنش‌ها و حجم خرید نمونه آماری، از سامانه استخراج و با روش کی-میانگین به چهار خوشه کلیدی، دست و دلباز، متناوب و نامطمئن تقسیم شدند. در نهایت ارزش وزنی طول عمر آنها تعیین شد. یافته‌ها نشان می‌دهد 2/24 درصد از مشتریان در گروه مشتریان کلیدی با بالاترین ارزش طول عمر قرار دارند که حفظ و نگهداری از آنها برای شرکت بیمه بسیار ضروری به نظر می‌رسد. مشتریان متناوب با بیشترین سهم از کل مشتریان بیمه با 4/33 درصد، در رتبه دوم ارزش طول عمر قرار گرفتند که شرکت‌های بیمه برای بالابردن ارزش مالی مبادلات این گروه نیازمند برنامه‌ریزی است. مشتریان دست و دلباز با 8/25درصد و مشتریان نامطمئن با 6/16 درصد به ترتیب در ردههای بعدی گروه فراوانی مشتریان در خوشهها قرار دارند. تعیین سهم و اهمیت خوشه‌های مشتریان بر اساس ارزش وزنی طول عمر مشتری از نتایج این پژوهش است که ضمن نگهداشت مشتریان دست و دلباز، توجه خاص به دو گروه مشتریان کلیدی و متناوب را به مدیران و برنامه‌ریزان بازاریابی صنعت بیمه توصیه می‌کند. | ||
| کلیدواژه‌ها | ||
| بیمه؛ ارزش طول عمر مشتریان؛ مدل ال ار اف ام؛ الگوریتم کی–میانگین (K-Means)؛ داده‌کاوی | ||
| عنوان مقاله [English] | ||
| The segmentation of insurance industry customers using clustering techniques and the LRFM communication model | ||
| نویسندگان [English] | ||
| Abolqasem Ebrahimi1؛ Kazem Askarifar2؛ Amin Nikbakht3 | ||
| 1Associate Professor, in Business Management, Shiraz University, Shiraz, Iran | ||
| 2Assistant Professor, in Business Management, Shiraz University, Shiraz, Iran | ||
| 3Master of Business Management, Shiraz University, Shiraz, Iran | ||
| چکیده [English] | ||
| Introduction: In the insurance industry, customers’ systematic identification and clustering is a major concern not only for marketers but for the entire organization, for this reason, Customer segmentation helps target organizations to customize their services and prioritize products based on their profitability. Methodology: This research is an applied, descriptive and quantitative study aiming to cluster customers by using k-means clustering. The data were collected from 800 customers of Pasargad insurance company in the city of Shiraz using the random sampling technique. The data on length, recency, frequency and monetary issues were collected by considering research ethics principles. Customers were clustered into four groups including key, prodigal, intermittent and uncertain by using the K-means method. Eventually, the customers’ lifetime value was determined Results and Discussion: Clustering has been carried out in four categories, including key clients whose contribution to a sample of 800 insurance customers is 24.2%. This group of customers has high financial value characteristics and high purchase frequencies. They are ranked first in terms of lifetime value. Based on the findings, the indicator of the volume of financial exchange is an index that graduates the other indices placing a client in the position of key a customer. Prodigal customers featured with high financial characteristics, low shopping frequencies and a 25.8% share of insurance customers are in the second category and ranked second in terms of lifetime value. The third group of customers, having a share of 33.4% of the insurance customers, low purchasing value characteristics and high purchase frequency, are frequent customers who are in the third rank of life value. The last group of customers is uncertain ones who account for a significant 16.6% share of customers. They have monetary value characteristics and low purchasing frequency and are ranked last in terms of lifetime value. They are among the customers who have no significant trade volumes and the lowest value of the purchasing iteration index, regardless of the time indicators associated with these customers. This puts them in the cluster of uncertain customers with a 16.6% share in the selected statistical sample. This is because they have different and irregular financial behaviors during a certain period. So, it may not be profitable to give them services. Conclusion: Determining the share and importance of customer groups based on customer lifetime value is one of the results of this study. While keeping prodigal customers, it is recommended to managers and marketing planners of the insurance industry to pay special attention to key and intermittent customers. From a managerial perspective, customer segmentation is a very important issue in the insurance industry. It can be a subject for studies and applied planning in every sector. Also, the specialization of insurance industry services in proportion to the customers' lifetime value, expectations and preferences based on scientific segmentation and customer data is one of the managerial recommendations. Another aspect that can be suggested to the managers of the insurance industry based on the results of this study is paying attention to the characteristics of customers in each cluster. Among these four groups, the cluster of key customers has a significant volume of transactions and length of the period of communication and repetition of insurance transactions. It also requires insurance companies to pay special attention to these customers. Next to this group are prodigal customers who have mostly low repetition of their insurance transactions, while the volume of turnover of this group is significant for the insurance industry. The importance of this group increases when these people have the lowest share in the overhead costs of insurance services for insurance companies, and, at the same time, their premiums are relatively higher than other groups. This makes managers pay more attention to this group. However, due to the low contact of these people with the employees of insurance companies, it is possible that they will receive less attention in relational marketing issues and promotional measures of this group. Accordingly, it is necessary for the managers of the insurance company to recognize generous customers and make special plans for them, especially in relationship marketing. In addition, given that a good number of the insurance company customers are uncertain clients, special planning is necessary to maintain and increase their loyalty. Another group identified in this study is that of the intermittent customers. This group of customers receive a relatively large amount of insurance services, while the premiums received from this group are not significant compared to the other groups. Identifying this type of customers and defining ways to retain them while reducing referrals to this group of customers is essential. | ||
| کلیدواژه‌ها [English] | ||
| Insurance, Customer lifetime value, LRFM model, K-means algorithm, Data mining | ||
| مراجع | ||
|
Abreu, J. E. C. (2019). Customer lifetime value in insurance. PhD diss, NOVA Information Management School (NIMS).
Alizadeh Zoeram, A., & Karimi Mazidi, A. R. (2018). New Approach for Customer Clustering by Integrating the LRFM Model and Fuzzy Inference System. Iranian Journal of Management Studies, 11(2), 351-378. [in Persian]
Bansal, G., Anand, A., & Yadavalli, V. S. S. (2020). Predicting effective customer lifetime: an application of survival analysis for telecommunication industry. Communications in Statistics-Theory and Methods, 49(10), 2305-2320.
Baradarn, V. & Farrokhi, Z. (2013). The Segmentation of Customers in the Banking Industry Using the Developed RFMC Model. Brand Management Quarterly Journal. The first year, the second issue, 2(1), 135-154.
Barman, D., & Chowdhury, N. (2019). A Novel Approach for the Customer Segmentation Using Clustering Through Self-Organizing Map. International Journal of Business Analytics (IJBAN), 6(2), 23-45.
Baum, M. and Singh, I. (2008). Winning the customer service battle. (1st Ed.). Chicago: A.T. Kearney, Inc.
Benedetto, C. A. Di, & Kim, K. H. (2016). Customer Equity and value management of global brands: Bridging theory and practice from financial and marketing perspectives. Introduction to a Journal of Business Research Special Section. Journal of Business Research, 69(9), 3721–3724.
Bergström, S. (2019). Customer segmentation of retail chain customers using cluster analysis.
Blattberg, R. C., Malthouse, E. C., & Neslin, S. A. (2009). Customer lifetime value: Empirical generalizations and some conceptual questions. Journal of Interactive Marketing, 23(2), 157-168.
Camino-Mogro, S., Armijos-Bravo, G., & Cornejo-Marcos, G. (2019). Competition in the insurance industry in Ecuador: An econometric analysis in life and non-life markets. The Quarterly Review of Economics and Finance, 71, 291-302.
Çavdar, A. B., & Ferhatosmanoğlu, N. (2018). Airline customer lifetime value estimation using data analytics supported by social network information. Journal of Air Transport Management, 67, 19-33.
Chang, H. H., & Tsay, S. F. (2004). Integrating of SOM and K-mean in data mining clustering: An empirical study of CRM and profitability evaluation. Journal of Information Management, 11(4), 161–203.
Central Insurance of Iran (2020). Statistical Yearbook of Insurance Industry 2019. Tehran: Central Insurance of Iran.
Channa, H. S. (2019). Customer Lifetime Value: An Ensemble Model Approach in Data Management, Analytics and Innovation (pp. 353-363). Springer, Singapore.
Cheng, C. H., & Chen, Y. S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications, 36, 4176–4184.
Dahana, W. D., Miwa, Y., & Morisada, M. (2019). Linking lifestyle to customer lifetime value: An exploratory study in an online fashion retail market. Journal of Business Research, 99, 319-331.
Dalla Pozza, I., Goetz, O., & Sahut, J. M. (2018). Implementation effects in the relationship between CRM and its performance. Journal of Business Research, 89, 391-403.
de Jong, G., Behrens, C., & van Ommeren, J. (2019). Airline loyalty (programs) across borders: A geographic discontinuity approach. International Journal of Industrial Organization, 62, 251-272.
Ekinci, Y., Uray, N. and Ülengin, F. (2014). A customer lifetime value model for the banking industry: a guide to marketing actions. European Journal of Marketing, 48, 761-784.
Di Benedetto, C. A., & Kim, K. H. (2016). Customer equity and value management of global brands: Bridging theory and practice from financial and marketing perspectives: Introduction to a Journal of Business Research Special Section. Journal of Business Research, 69(9), 3721-3724.
Emani,A & Abasi, M. (2017). Clustering of Customers Based on RFM Model Using Fuzzy C-Measure Algorithm (Case Study: Zahedan Welfare Chain Store). Journal of Public Management Research, 37, 251-276.
Epstein, M. J., Friedl, M., & Yuthas, K. (2008). Managing customer profitability. Journal of Accountancy, 206(6), 54.
Estrella-Ramón, A., Sánchez-Pérez, M., Swinnen, G., & VanHoof, K. (2017). A model to improve management of banking customers. Industrial Management & Data Systems, 17(2), 250-266.
Fader, P.S., Hardie, B.G.S. & Lee, K.L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430.
Gavval, R., & Ravi, V. (2020). Clustering Bank Customer Complaints on Social Media for Analytical CRM via Multi-objective Particle Swarm Optimization. In Nature Inspired Computing for Data Science (pp. 213-239). Springer, Cham.
Glady, N,Baesens, B. and Croux, C. (2009). A modified Pareto/NBD approach for predicting customer lifetime value. Expert Systems with Applications. 36 (2), 2062-2071.
Golshenas, M., Khanlari, A. (2016). Customer segmentation based on life cycle value and artificial neural networks (Case study: Customers of a trading company to broadcast). Pars Manager Marketing Quarterly, 3, 1-12. [in Persian]
Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N. & Sriram, S. (2006). Modeling customer lifetime value. Journal of service research, 9(2), 139-155.
Gupta, S., Lehmann, D. R., & Stuart, J. A. (2004). Valuing customers. Journal of Marketing Research, 41(1), 7–18.
Gustriansyah, R., Suhandi, N., & Antony, F. (2020). Clustering optimization in RFM analysis based on k-means. Indones. J. Electr. Eng. Comput. Sci, 18(1), 470-477.
Hiziroglu, A., Sisci, M., Cebeci, H. I., & Seymen, O. F. (2018, January). An Empirical Assessment of Customer Lifetime Value Models within Data Mining. In Doctoral Consortium/Forum@ DB&IS (pp. 33-42).
Hosseini, S. M. S., Maleki, A., & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37, 5259–5264. [in Persian]
Hyun, S. S., & Perdue, R. R. (2017). Understanding the dimensions of customer relationships in the hotel and restaurant industries. International Journal of Hospitality Management, 64, 73-84.
IMF (2021). Global Financial Stability Report, April 2016: Potent Policies for a Successful Normalization. IMF report, available at https://www.elibrary.imf.org/view/IMF082/23157-9781513506777/23157-9781513506777/ch03.xml?language=en, retrived on 2/5/2021.
Kafashpour, A, Tavakoli, A, Alizadeh, Z. (2010). Customer segmentation based on the value of their lifetime using the LRFM model, Public Management Research, 15, 63-84. [in Persian]
Kao, Y. T., Wu, H. H., Chen, H. K., & Chang, E. C. (2011). A case study of applying LRFM model and clustering techniques to evaluate customer values. Journal of Statistics and Management Systems, 14(2), 267-276.
Kim, S. Y., Jung, T. S., Suh, E. H., & Hwang, H. S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert systems with applications, 31(1), 101-107.
Kim, Y. P., Boo, S., & Qu, H. (2018). Calculating tourists' customer equity and maximizing the hotel's ROI. Tourism Management, 69, 408-421.
Kumar, V., Lemon, K. N., & Parasuraman, A. (2006). Managing customers for value: An overview and research agenda. Journal of Service Research, 9(2), 87–94.
Lee, W. I., Shih, B. Y., & Chung, Y. S. (2008). The exploration of consumers’ behavior in choosing hospital by the application of neural network. Expert Systems with Applications, 34, 806–816.
Li, D. C., Dai, W. L., & Tseng, W. T. (2011). A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business. Expert Systems with Applications, 38(6), 7186-7191.
Li, Y., Huang, J., & Song, T. (2019). Examining business value of customer relationship management systems: IT usage and two-stage model perspectives. Information & Management, 56(3), 392-402.
Marisa, F., Ahmad, S. S. S., Yusof, Z. I. M., Hunaini, F., & Aziz, T. M. A. (2019). Segmentation Model of Customer Lifetime Value in Small and Medium Enterprise (SMEs) using K-Means Clustering and LRFM Model. International Journal of Integrated Engineering, 11(3). Marketing, 23(2), 157–168.
Martin, G. C. (2015). The important of customer equity and branding: A research note. Journal of Business & Economics Research, 13(4), 153–154.
McCarthy, D., Fader, P., & Hardie, B. (2016). V (CLV): Examining variance in models of customer lifetime value. Available at SSRN 2739475.
Mohamadi, A., Alavi, S., Pourmourdini, A. (2019). Investigating the Effect of Loyalty Plans on the Loyalty of Asia Insurance Customers Using the Salomon Four-Party Plan. Journal of Business Management, 11(2), 259-276. [in Persian]
Monalisa, S., & Kurnia, F. (2019). Analysis of DBSCAN and K-means algorithm for evaluating outlier on RFM model of customer behaviour. Telkomnika, 17(1), 110-117.
Morisada, M., Miwa, Y., & Dahana, W. D. (2019). Identifying valuable customer segments in online fashion m arkets: An implication for customer tier programs. Electronic Commerce Research and Applications, 33, 100822.
Moslehi, SA. N, Kafashpour, A, Naji Azimi, Z. (2014). Using the LRFM model To segment customers based on the value of their life cycle. Public Management Research, 25, 119-140. [in Persian]
Nastasoiu, A., & Vandenbosch, M. (2019). Competing with loyalty: How to design successful customer loyalty reward programs. Business Horizons, 62(2), 207-214.
Óskarsdóttir, M., Baesens, B., & Vanthienen, J. (2018). Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values. Big data, 6 1, 53-65.
Peker, S., Kocyigit, A., & Eren, P. E. (2017). LRFMP model for customer segmentation in the grocery retail industry: A case study. Marketing Intelligence & Planning, 35(4), 544-559.
Pfeifer, P. E., Haskins, M. E., & Conroy, R. M. (2005). Customer lifetime value, customer profitability, and the treatment of acquisition spending. Journal of Managerial Issues, 17(1), 11–25.
Pramono, P. P., Surjandari, I., & Laoh, E. (2019). Estimating Customer Segmentation based on Customer Lifetime Value Using Two-Stage Clustering Method. In 2019 16th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1-5). IEEE.
Razmi, J. and Ghanbari, A. (2009). a new model for Calculating customer lifetime value ", Journal of Information Technology Management, 1(2), 35-50. [in Persian]
Safari Kahreh, M., Khodadad Hosseini, H. and Azar, A. (2015). Customer long-term value analysis for segmentation and profitability management of study customers: One of the commercial banks in Iran. Journal of Management Research in Iran, 8(4), 87-110. [in Persian]
Safari, F., Safari, N., & Montazer, G. A. (2016). Customer lifetime value determination based on RFM model. Marketing Intelligence & Planning. 34(4), 446-461.
Santouridis, I., & Veraki, A. (2017). Customer relationship management and customer satisfaction: The mediating role of relationship quality. Total Quality Management and Business Excellence, 28(9–10), 1122–1132.
Shahin, A., & Shahiverdi, S. M. (2015). Estimating customer lifetime value for new product development based on the Kano model with a case study in automobile industry. Benchmarking: An International Journal. 22(5), 857-873.
Sohrabi, B., & Khanlari, A. (2007). Customer lifetime value (CLV) measurement based on RFM model. The Iranian Accounting and Auditing Review. 14(47), 7-20. [in Persian]
Wei, J. T., Lin, S. Y., Weng, C. C., & Wu, H. H. (2012). A case study of applying LRFM model in market segmentation of a children’s dental clinic. Expert Systems with Applications, 39(5), 5529-5533.
Wu, H. H., Lin, S. Y., & Liu, C. W. (2014). Analyzing patients’ values by applying cluster analysis and LRFM model in a pediatric dental clinic in Taiwan. The Scientific World Journal, 2014.
Yousefizadeh, A., Sorayai, A. (2018). investigating and Customer clustering based on RFM model and design a model to provide services to key customers. Journal of Executive Management, 20 (10), 175-198.
Yu, X., & Yuan, C. (2019). How consumers’ brand experience in social media can improve brand perception and customer equity. Asia Pacific Journal of Marketing and Logistics, 31(5), 1233-1251. | ||
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