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New approach based on proximity/remoteness measurement for customer classification

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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

  1. 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.

    Google Scholar 

  2. Akhbar, M. M., & Parvez, N. (2009). Impact of service quality, trust, and customer satisfaction on customer loyalty. ABAC Journal, 29(1), 24–38.

    Google Scholar 

  3. 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.

    Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. 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.

    Article  Google Scholar 

  6. 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.

    Article  Google Scholar 

  7. 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.

    Article  Google Scholar 

  8. 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.

    Article  Google Scholar 

  9. Beckham, C., & Pal, C. (2017). Unimodal probability distributions for deep ordinal classification. arXiv preprint arXiv:1705.05278.

  10. 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.

    Article  Google Scholar 

  11. 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.

    Article  Google Scholar 

  12. 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.

    Article  Google Scholar 

  13. 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.

    Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. 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.

    Article  Google Scholar 

  16. 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.

    Article  Google Scholar 

  17. 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.

    Google Scholar 

  18. 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.

    Article  Google Scholar 

  19. 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.

  20. Fraley, A., & Kurt, T. (1999). Increasing customer value by integrating data mining and campaign management software. Data Management, 49–53.

  21. Gerson, R. F. (1993). Measuring customer satisfaction. MenloPark, CA: Crisp Publication Inc.

    Google Scholar 

  22. 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.

    Article  Google Scholar 

  23. 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.

    Article  Google Scholar 

  24. 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.

    Article  Google Scholar 

  25. 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.

    Article  Google Scholar 

  26. 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.

  27. 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.

    Article  Google Scholar 

  28. 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.

    Article  Google Scholar 

  29. 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.

    Article  Google Scholar 

  30. 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.

    Article  Google Scholar 

  31. 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.

    Article  Google Scholar 

  32. 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.

    Article  Google Scholar 

  33. 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.

  34. 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.

    Article  Google Scholar 

  35. 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.

    Article  Google Scholar 

  36. 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.

    Article  Google Scholar 

  37. Luo, N., & Mu, Z. C. (2004). Bayesian network classifier and its application in CRM. Computer Application, 24(3), 79–81.

    Google Scholar 

  38. 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.

    Article  Google Scholar 

  39. 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.

    Google Scholar 

  40. 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.

    Article  Google Scholar 

  41. 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.

    Article  Google Scholar 

  42. 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.

    Article  Google Scholar 

  43. 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.

    Article  Google Scholar 

  44. 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.

    Article  Google Scholar 

  45. 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.

  46. 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.

    Article  Google Scholar 

  47. 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.

    Google Scholar 

  48. 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.

    Article  Google Scholar 

  49. 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.

    Article  Google Scholar 

  50. 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.

    Google Scholar 

  51. Sahoo, D., & Ghosh, T. (2016). Healthscape role towards customer satisfaction in private healthcare. International Journal of Health Care Quality Assurance, 29(6), 600–613.

    Article  Google Scholar 

  52. 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.

    Article  Google Scholar 

  53. 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.

  54. Sarkar, B. (2013). A production-inventory model with probabilistic deterioration in two-echelon supply chain management. Applied Mathematical Modelling, 37(5), 3138–3151.

    Article  Google Scholar 

  55. Sarkar, B. (2012). An inventory model with reliability in an imperfect production process. Applied Mathematics and Computation, 218(9), 4881–4891.

    Article  Google Scholar 

  56. 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.

    Article  Google Scholar 

  57. 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.

    Article  Google Scholar 

  58. 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.

    Google Scholar 

  59. 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.

    Article  Google Scholar 

  60. 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.

    Article  Google Scholar 

  61. 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.

    Google Scholar 

  62. Smith, D. (October 2016). The dimensions of customer satisfaction in the Jamaican financial service industry. https://scholarworks.waldenu.edu/dissertations.

  63. 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.

    Article  Google Scholar 

  64. Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745–7754.

    Article  Google Scholar 

  65. 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.

    Article  Google Scholar 

  66. Tchangani, A. P. (2009). Selectability/rejectability measures approach for nominal classification. Journal of Uncertain Systems, 3(4), 257–269.

    Google Scholar 

  67. 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.

    Article  Google Scholar 

  68. 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.

    Article  Google Scholar 

  69. 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.

    Article  Google Scholar 

  70. 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.

    Chapter  Google Scholar 

  71. 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.

    Article  Google Scholar 

  72. 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.

    Article  Google Scholar 

  73. 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.

    Article  Google Scholar 

  74. 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.

    Article  Google Scholar 

  75. 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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