Abstract
Customer recognition provides an opportunity to the customers to think about services in service companies. Classifying customers into different categories based on their satisfaction helps these insurance companies to better manage their capital that results in more profit. Researchers have used different categorization methods to identify and classify customers based on their level of satisfaction with services. The purpose of this article is to present a new method for customer classification based on the satisfaction with services in the insurance company. It overcomes the inefficiencies of a classification method called Selectability/Rejectability Measures Approach for nominal classification and provides more accurate results. This method uses service quality criteria to better consideration of customers’ perceptions and expectations. Finally, a numerical example is provided to justify the proposed method. The input data is obtained from a survey in which 384 complete questionnaires collected from the customers are examined.
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References
Abdullah, M. A., & Azam, S. F. (2015). Mediating relationship of financial practice between financial knowledge and business success: An empirical study on Malaysian small enterprises. Australian Academy of Business and Economics Review, 1(1), 1–23.
Akhbar, M. M., & Parvez, N. (2009). Impact of service quality, trust, and customer satisfaction on customer loyalty. ABAC Journal, 29(1), 24–38.
Alawni, M. S., Yusoff, R. Z., Al-Swidi, A. K., & Al-Matari, E. M. (2015). The relationship between communication, customer knowledge and customer loyalty in Saudi Arabia insurance industry companies. Mediterranean Journal of Social Sciences, 6(1), 318.
Altuntas, S., Dereli, T., & Yilmaz, M. K. (2012). Multi-criteria decision making methods based weighted SERVQUAL scales to measure perceived service quality in hospitals: A case study from Turkey. Total Quality Management & Business Excellence, 23(11–12), 1379–1395.
Aneesh, A., Dileeplal, J., & Abraham, M. (2014). An integrated fuzzy weighted SERVQUAL-QFD approach for service quality improvement. International Journal of Engineering Research, 3(12), 774–776.
Angilella, S., Greco, S., Lamantia, F., & Matarazzo, B. (2004). Assessing non-additive utility for multicriteria decision aid. European Journal of Operational Research, 158(3), 734–744.
Awasthi, A., Chauhan, S. S., Omrani, H., & Panahi, A. (2011). A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating transportation service quality. Computers & Industrial Engineering, 61(3), 637–646.
BaykasoğLu, A., KaplanoğLu, V., DurmuşOğLu, Z. D., & ŞAhin, C. (2013). Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection. Expert Systems with Applications, 40(3), 899–907.
Beckham, C., & Pal, C. (2017). Unimodal probability distributions for deep ordinal classification. arXiv preprint arXiv:1705.05278.
Büyüközkan, G., & Çifçi, G. (2012). A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Systems with Applications, 39(3), 2341–2354.
Büyüközkan, G., Çifçi, G., & Güleryüz, S. (2011). Strategic analysis of healthcare service quality using fuzzy AHP methodology. Expert Systems with Applications, 38(8), 9407–9424.
Chan, C. C. H. (2008). Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer. Expert Systems with Applications, 34(4), 2754–2762.
Cheng, X., Gong, B., & Zhang, H. (2012). Customer value assessment using the fuzzy ahp and topsis methods: Application in bank. Journal of Information & Computational Science, 9(12), 3431–3438.
Chien-Chang, C. (2012). Evaluating the quality of airport service using the fuzzy multi-criteria decision-making method: A case study of Taiwanese airports. Expert Systems, 29(3), 246–260.
Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications, 34(1), 313–327.
Crompton, J. L., & Mackay, K. J. (1989). Users’ perceptions of the relative importance of service quality dimensions in selected public recreation programs. Leisure Sciences, 11(4), 367–375.
DehghanNayeri, M., & Mehregan, M. R. (2018). Nonlinear multi attribute satisfaction analysis (N-MUSA): Preference disaggregation approach to satisfaction. Iranian Journal of Management Studies, 11(1), 1–22.
Dincer, H., Yüksel, S., & Martinez, L. (2019). Balanced scorecard-based Analysis about European Energy Investment Policies: A hybrid hesitant fuzzy decision-making approach with Quality Function Deployment. Expert Systems with Applications, 115(January), 152–171.
Doumpos, M., & Zopounidis, C. (2018). Disaggregation approaches for multicriteria classification: An overview. In Preference disaggregation in multiple criteria decision analysis (pp. 77–94). Springer, ISBN 978-3-319-90599-0.
Fraley, A., & Kurt, T. (1999). Increasing customer value by integrating data mining and campaign management software. Data Management, 49–53.
Gerson, R. F. (1993). Measuring customer satisfaction. MenloPark, CA: Crisp Publication Inc.
Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2017). A new hybrid simulation-based assignment approach for evaluating airlines with multiple service quality criteria. Journal of Air Transport Management, 63, 45–60.
Ghorbani, M., Arabzad, S. M., & Tavakkoli-Moghaddam, R. (2014). Service quality-based distributor selection problem: a hybrid approach using fuzzy ART and AHP-FTOPSIS. International Journal of Productivity and Quality Management, 13(2), 157–177.
Guchhait, R., Sarkar, M., Sarkar, B., & Pareek, S. (2017). Single-vendor multi-buyer game theoretic model under multi-factor dependent demand. International Journal of Inventory Research, 4(4), 303–332.
Griffith, J. (2006). A compositional analysis of the organizational climate-performance relation: Public schools as organizations. Journal of Applied Social Psychology, 36(8), 1848–1880.
Hasanpour, Y., Nemati, S., & Tavoli, R. (2018). Clustering system group customers through fuzzy C-means clustering. In 2018 4th Iranian conference on signal processing and intelligent systems (ICSPIS) (pp. 161–165). IEEE.
Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37(4), 543–558.
Kang, C. W., Ramzan, M. B., Sarkar, B., & Imran, M. (2018). Effect of inspection performance in smart manufacturing system based on human quality control system. The International Journal of Advanced Manufacturing Technology, 94(9–12), 4351–4364.
Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications Policy, 28(9), 751–765.
Khan, I., Jemai, J., Lim, H., & Sarkar, B. (2019). Effect of electrical energy on the manufacturing setup cost reduction, transportation discounts, and process quality improvement in a two-echelon supply chain management under a service-level constraint. Energies, 12(19), 1–32.
Kuppelwieser, V., Abdelaziz, F. B., & Meddeb, O. (2018). Unstable interactions in customers’ decision making: An experimental proof. Annals of Operations Research. https://doi.org/10.1007/s10479-018-2944-6.
Lau, H., Nakandala, D., Samaranayake, P., & Shum, P. (2016). A hybrid multi-criteria decision model for supporting customer-focused profitability analysis. Industrial Management & Data Systems, 116(6), 1105–1130.
Laussel, D., Long, N. V., & Resende, J. (2019). Quality and price personalization under customer recognition: A dynamic monopoly model with constraint equillibria. Available at SSRN 3352373.
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.
Lierop, D. V., & El-Geneidy, A. (2016). Enjoying loyalty: The relationship between service quality, customer satisfaction, and behavioral intentions in public transit. Research in Transportation Economics, 59, 50–59.
Liou, J. J., & Tzeng, G. H. (2007). A non-additive model for evaluating airline service quality. Journal of Air Transport Management, 13(3), 131–138.
Luo, N., & Mu, Z. C. (2004). Bayesian network classifier and its application in CRM. Computer Application, 24(3), 79–81.
Lupo, T. (2013). A fuzzy ServQual based method for reliable measurements of education quality in Italian higher education area. Expert Systems with Applications, 40(17), 7096–7110.
Maleki, A. (2016). Examining the relationship between organizational learning culture, and customer satisfaction in insurance industry. European Online Journal of Natural and Social Sciences, 5(3), 647–663.
Mardani, A., Jusoh, A., Zavadskas, E. K., Khalifah, Z., & Nor, K. M. D. (2015). Application of multiple-criteria decision-making techniques and approaches to evaluating of service quality: A systematic review of the literature. Journal of Business Economics and Management, 16(5), 1034–1068.
Mari, S. I., Memon, M. S., Ramzan, M. B., Qureshi, S. M., & Iqbal, M. W. (2019). Interactive fuzzy multi criteria decision making approach for supplier selection and order allocation in a resilient supply chain. Mathematics, 7(2), 1–16.
Meesala, A., & Paul, J. (2018). Service quality, consumer satisfaction and loyalty in hospitals: Thinking for the future. Journal of Retailing and Consumer Services, 40(January), 261–269.
Miranda, S., Tavares, P., & Queiró, R. (2018). Perceived service quality and customer satisfaction: A fuzzy set QCA approach in the railway sector. Journal of Business Research, 89, 371–377.
Moon, I., Shin, E., & Sarkar, B. (2014). Min–max distribution free continuous-review model with a service level constraint and variable lead time. Applied Mathematics and Computation, 229(February), 310–315.
Oraby, S., Gundecha, P., Mahmud, J., Bhuiyan, M., &Akkiraju, R. (2017). How May I Help You?: Modeling Twitter customer service conversations using fine-grained dialogue acts. In Proceedings of the 22nd international conference on intelligent user interfaces (pp. 343–355). ACM.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41–50.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for measuring consumer perc. Journal of Retailing, 64(1), 12–40.
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.
Rezaei, J., Kothadiya, O., Tavasszy, L., & Kroesen, M. (2018). Quality assessment of airline baggage handling systems using SERVQUAL and BWM. Tourism Management, 66(June), 85–93.
Sahar, O., Latif, M. A., & Imran, M. (2017). Machine learning techniques for the evaluation of the software reliability growth models. Gomal University Journal of Research (Sciences), 33(1), 25–35.
Sahoo, D., & Ghosh, T. (2016). Healthscape role towards customer satisfaction in private healthcare. International Journal of Health Care Quality Assurance, 29(6), 600–613.
Sarkar, B. (2019). Mathematical and analytical approach for the management of defective items in a multi-stage production system. Journal of Cleaner Production, 218, 896–919.
Sarkar, B. (2016). Supply chain coordination with variable backorder, inspections, and discount policy for fixed lifetime products. Mathematical Problems in Engineering, Article ID 6318737, 1–14.
Sarkar, B. (2013). A production-inventory model with probabilistic deterioration in two-echelon supply chain management. Applied Mathematical Modelling, 37(5), 3138–3151.
Sarkar, B. (2012). An inventory model with reliability in an imperfect production process. Applied Mathematics and Computation, 218(9), 4881–4891.
Sarkar, B., Chaudhuri, K., & Moon, I. (2015). Manufacturing setup cost reduction and quality improvement for the distribution free continuous-review inventory model with a service level constraint. Journal of Manufacturing Systems, 34(January), 74–82.
Sarkar, B., Mondal, S. P., Hur, S., Ahmadian, A., Salahshour, S., Guchhait, R., et al. (2019). An optimization technique for national income determination model with stability analysis of differential equation in discrete and continuous process under the uncertain environment. RAIRO-Operations Research, 53(5), 1649–1674.
Shah, A. B., Shaikhh, M., & Khowaja, M. A. (2018). An empirical analysis of customer satisfaction in the restaurants of Hyderabad. Journal of Grassroots, 51(2), 334–344.
Shin, D., Guchhait, R., Sarkar, B., & Mittal, M. (2016). Controllable lead time, service level constraint, and transportation discounts in a continuous review inventory model. RAIRO-Operations Research, 50(4–5), 921–934.
Shokouhyar, S., Safari, S., & Mohsenian, F. (2018). Improving candy industry competitiveness: Retailers’ perception regarding customer satisfaction. Journal of Food Products Marketing, 24(6), 761–783.
Sidharta, E. A., Mentari, S., Wafaretta, V., & Nuraini, U. (2017). Attitude and perception towards Sharia insurance product. International Journal of Business and Commerce, 6(5), 11–23.
Smith, D. (October 2016). The dimensions of customer satisfaction in the Jamaican financial service industry. https://scholarworks.waldenu.edu/dissertations.
Stefano, N. M., CasarottoFilho, N., Barichello, R., & Sohn, A. P. (2015). A fuzzy SERVQUAL based method for evaluated of service quality in the hotel industry. Procedia CIRP, 30, 433–438.
Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745–7754.
Tarofder, A. K., Azam, S. F., & Jalal, A. N. (2017). Operational or strategic benefits: Empirical investigation of internet adoption in supply chain management. Management Research Review, 40(1), 28–52.
Tchangani, A. P. (2009). Selectability/rejectability measures approach for nominal classification. Journal of Uncertain Systems, 3(4), 257–269.
Tsai, J. Y., Ding, J. F., Liang, G. S., & Ye, K. D. (2018). Use of a hybrid MCDM method to evaluate key solutions influencing service quality at a port logistics center in Taiwan. Brodogradnja: Teorijaipraksabrodogradnjeipomorsketehnike, 69(1), 89–105.
Tsaur, S. H., Chang, T. Y., & Yen, C. H. (2002). The evaluation of airline service quality by fuzzy MCDM. Tourism Management, 23(2), 107–115.
Tseng, M. L. (2009). A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert Systems with Applications, 36(4), 7738–7748.
Ukpabi, D., Olaleye, S., Mogaji, E., & Karjaluoto, H. (2018). Insights into online reviews of hotel service attributes: A cross-national study of selected countries in Africa. In B. Stangl & J. Pesonen (Eds.), Information and communication technologies in tourism 2018 (pp. 243–256). Cham: Springer. https://doi.org/10.1007/978-3-319-72923-7_19.
Wei, J.-T., Lee, M.-C., Chen, H.-K., & Wu, H.-H. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques. Expert Systems with Applications, 40(18), 7513–7518.
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.
Xiao, J., Xie, L., He, C., & Jiang, X. (2012). Dynamic classifier ensemble model for customer classification with imbalanced class distribution. Expert Systems with Applications, 39(3), 3668–3675.
Xie, Y. Y., Li, X., Ngai, E. W. T., & Ying, W. Y. (2009). Customer churn prediction using improved balanced random forests. Expert Systems with Applications, 36(3), 5445–5449.
Yan, L., Miller, D. J, Mozer, M. C, & Wolniewicz, R. (2001). Improving prediction of customer behavior in nonstationary environments. In Proceeding of the international joint conference on neural networks (pp. 2258–2263). Washington DC: IEEE.
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Akhyani, F., Komeili Birjandi, A., Sheikh, R. et al. New approach based on proximity/remoteness measurement for customer classification. Electron Commer Res 22, 267–298 (2022). https://doi.org/10.1007/s10660-020-09402-7
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DOI: https://doi.org/10.1007/s10660-020-09402-7