Providing a Customer-Centric Knowledge Model Based on Individual Insurers' Loyalty Through an Information Registration Approach

Document Type : Original Research Manuscripts

Authors

1 PhD. Student, Department of Business Management, Rasht Branch, Islamic Azad University, Rasht, Iran.

2 Department of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

3 Department of Management and Accounting,, Rasht Branch, Islamic Azad University, Rasht, Iran.

4 Department of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran

Abstract
The research aimed to introduce a customer-oriented knowledge model based on the loyalty of individual insurers using an information recording approach. The qualitative research method was systematically conducted, employing the grounded theory approach through interviews with experts in the insurance industry. Fifteen individuals were selected through targeted sampling. The collected data were analyzed using NVivo software, and the relevant model was presented. According to Strauss-Corbin's categories, there are 6 causal conditions: suitability of insurance policy conditions, rating of customers based on special attention to specific customers, rating of customers based on "need level" for insurance services, empowerment of employees and managers, brand image, confidence, company credibility, service quality, and security. Additionally, there are 2 background conditions (establishment of knowledge management and organizational structure), intervening conditions (social missions), 6 strategies (discount/incentive schemes, organization of business processes, club launch customers, policyholder loyalty programs, establishing customer-centric strategies, and relationship marketing), and outcomes (loyalty, customer lifetime value, repeat purchases, and customer referrals). Data mining enables insurance companies to utilize existing data to identify customer behavior patterns. By identifying and developing services based on the actual needs of customers, insurance companies can enhance and refine their services. Through the utilization of existing data on policyholder loyalty, insurance companies can accurately pinpoint customer behavior patterns, needs, and preferences.

Keywords

Subjects

Abbasimehr, H., & Shabani, M. (2021). A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques. Journal of ambient intelligence and humanized computing, 12(1), 515-531. https://doi.org/10.1007/s12652-020-02015-w
Alves Gomes, M., & Meisen, T. (2023). A review on customer segmentation methods for personalized customer targeting in e-commerce use cases. Information Systems and e-Business Management, 1-44. https://doi.org/10.1007/s10257-023-00640-4
Casaló, L. V., Flavián, C., & Guinalíu, M. (2008). The role of satisfaction and website usability in developing customer loyalty and positive word‐of‐mouth in the e‐banking services. International journal of bank marketing, 26(6), 399-417.
Chalupa, S., & Petricek, M. (2024). Understanding customer's online booking intentions using hotel big data analysis. Journal of vacation marketing, 30(1), 110-122. https://doi.org/10.1177/13567667221122107
Chen, X., Sun, W., Wang, B., Li, Z., Wang, X., & Ye, Y. (2018). Spectral clustering of customer transaction data with a two-level subspace weighting method. IEEE Transactions on Cybernetics, 49(9), 3230-3241. https://doi.org/10.1109/TCYB.2018.2836804
De Marco, M., Fantozzi, P., Fornaro, C., Laura, L., & Miloso, A. (2021). Cognitive analytics management of the customer lifetime value: an artificial neural network approach. Journal of Enterprise Information Management, 34(2), 679-696. https://doi.org/10.1108/JEIM-01-2020-0029
Deng, Y., & Gao, Q. (2020). A study on e-commerce customer segmentation management based on improved K-means algorithm. Information Systems and e-Business Management, 18, 497-510. https://doi.org/10.1007/s10257-018-0381-3
Dogan, O., Ayçin, E., & Bulut, Z. (2018). Customer segmentation by using RFM model and clustering methods: a case study in retail industry. International Journal of Contemporary Economics and Administrative Sciences, 8.
 Dogan, O., Seymen, O. F., & Hiziroglu, A. (2022). Customer behavior analysis by intuitionistic fuzzy segmentation: comparison of two major cities in Turkey. International Journal of Information Technology & Decision Making, 21(02), 707-727. https://doi.org/10.1142/S0219622021500607
Farasatkhah, M. (2015). Qualitative research method in social sciences with emphasis on grounded theory. Tehran, Aghaz Publications. [in
Gee, R., Coates, G., & Nicholson, M. (2008). Understanding and profitably managing customer loyalty. Marketing Intelligence & Planning, 26(4), 359-374.
Griva, A. (2022). “I can get no e-satisfaction”. What analytics say? Evidence using satisfaction data from e-commerce. Journal of Retailing and Consumer Services, 66, 102954. https://doi.org/10.1016/j.jretconser.2022.102954
Griva, A., Bardaki, C., Pramatari, K., & Doukidis, G. (2021). Factors affecting customer analytics: Evidence from three retail cases. Information Systems Frontiers, 1-24. https://doi.org/10.1007/s10796-020-10098-1
Hjort, K., Lantz, B., Ericsson, D., & Gattorna, J. (2013). Customer segmentation based on buying and returning behaviour. International Journal of Physical Distribution & Logistics Management, 43(10), 852-865. https://doi.org/10.1108/IJPDLM-02-2013-0020
SINGH, I., NAYYAR, A., & DAS, S. (2019). A study of antecedents of customer loyalty in banking & insurance sector and their impact on business performance. Revista Espacios, 40(06).
Kanchanapoom, K., & Chongwatpol, J. (2023). Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation. Journal of Marketing Analytics, 11(2), 172-185. https://doi.org/10.1057/s41270-022-00158-7
Kassemeier, R., Alavi, S., Habel, J., & Schmitz, C. (2022). Customer-oriented salespeople’s value creation and claiming in price negotiations. Journal of the Academy of Marketing Science, 50(4), 689-712. https://doi.org/10.1007/s11747-022-00846-x
Kumar, V. (2024). Overview of Customer Engagement. In: Valuing Customer Engagement. Palgrave Executive Essentials. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-43296-5_1
Lawrence, J. M., Crecelius, A. T., Scheer, L. K., & Patil, A. (2019). Multichannel strategies for managing the profitability of business-to-business customers. Journal of Marketing Research, 56(3), 479-497.
Lee, J. S., Tsang, N., & Pan, S. (2015). Examining the differential effects of social and economic rewards in a hotel loyalty program. International Journal of Hospitality Management, 49, 17-27.
Mahammadi Torkamani, H., Pasban, M., Alavi Matin, Y., & Niki Esfahlan, H. (2024). Designing an Intelligent Pattern of Digital Consumer Behavior Based on Big Data. International Journal of Knowledge Processing Studies, 4(1), 40-51. https://doi.org/10.22034/kps.2023.385443.1104  
Mintz, O., Currim, I. S., & Deshpandé, R. (2023). National customer orientation: an empirical test across 112 countries. Marketing Letters, 34(2), 189-204.
Nguyen, S. P. (2021). Deep customer segmentation with applications to a Vietnamese supermarkets’ data. Soft Computing, 25(12), 7785-7793. https://doi.org/10.1007/s00500-021-05796-0
Nilashi, M., Samad, S., Minaei-Bidgoli, B., Ghabban, F., & Supriyanto, E. (2021). Online reviews analysis for customer segmentation through dimensionality reduction and deep learning techniques. Arabian Journal for Science and Engineering, 46(9), 8697-8709. https://doi.org/10.1007/s13369-021-05638-z
Patil, A., & Syam, N. (2018). How do specialized personal incentives enhance sales performance? The benefits of steady sales growth. Journal of Marketing, 82(1), 57-73.
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. https://doi.org/10.1108/MIP-11-2016-0210
Quan, N., Chi, N. T. K. C., Nhung, D., Ngan, N., & Phong, L. (2020). The influence of website brand equity, e-brand experience on e-loyalty: The mediating role of e-satisfaction. Management Science Letters, 10(1), 63-76.
Rahmati, M. H., Namamian, F., Hasani, S. R., & Baghfalaki, A. (2023). Modeling Brand Resilience in Iran's Handwoven Carpet Industry Using Background Knowledge and Data Mining in Critical Conditions. International Journal of Knowledge Processing Studies, (), -. https://doi.org/10.22034/kps.2023.406540.1147   
Tabianan, K., Velu, S., & Ravi, V. (2022). K-means clustering approach for intelligent customer segmentation using customer purchase behavior data. Sustainability, 14(12), 7243. doi.org/10.3390/su14127243
Moretta Tartaglione, A., Cavacece, Y., Russo, G., & Granata, G. (2019). A systematic mapping study on customer loyalty and brand management. Administrative Sciences, 9(1), 8.
Tsai, C. F., Hu, Y. H., & Lu, Y. H. (2015). Customer segmentation issues and strategies for an automobile dealership with two clustering techniques. Expert Systems, 32(1), 65-76. https://doi.org/doi.org/10.1111/exsy.12056  
Wu, Z., Jin, L., Zhao, J., Jing, L., & Chen, L. (2022). Research on segmenting e-commerce customer through an improved k-medoids clustering algorithm. Computational Intelligence and Neuroscience, 2022.
Yang, T., Zhang, J., Wang, L., & Zhang, J. (2022). A novel customer-oriented recommendation system for paid knowledge products. Journal of Systems Science and Systems Engineering, 31(5), 515-533. https://doi.org/10.1007/s11518-022-5540-x
Yu, S., Jiang, Z., Chen, D. D., Feng, S., Li, D., Liu, Q., & Yi, J. (2021, August). Leveraging tripartite interaction information from live stream e-commerce for improving product recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3886-3894).
Zhang, F., Qi, S., Liu, Q., Mao, M., & Zeng, A. (2020). Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks. Expert Systems with Applications, 149, 113346. https://doi.org/10.1016/j.eswa.2020.113346
Zhang, Q., Jia, Q., Wang, C., Li, J., Wang, Z., & He, X. (2021, July). Amm: Attentive multi-field matching for news recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1588-1592).

  • Receive Date 23 February 2024
  • Revise Date 24 March 2024
  • Accept Date 08 June 2024