Article
Construction of Insurance Consumers' Purchasing Behavior Model Based on Big Data Analysis
Authors
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Abstract
The social capital theory is selected as the theoretical basis, and the decision-making of household commercial insurance purchase is taken as the research object. By using the frontier theory of big data, an improved K-means clustering algorithm is proposed to segment customers first, then the results of the segmentation are predicted and analyzed. Finally, an insurance consumer purchase behavior model is proposed, and an empirical test is carried out according to the proposed model. Social capital and its three dimensions can promote the possibility in family commercial insurance, thus ultimately promoting residents' families to make purchase decisions on commercial insurance.
Keywords
Big data, Insurance, Purchasing behavior, 94A16
Citation
Ding, H. & Zuo, X. (2024). Construction of insurance consumers' purchasing behavior model based on big data analysis. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0006
H. Ding and X. Zuo, “Construction of insurance consumers' purchasing behavior model based on big data analysis,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, 2024, doi: 10.2478/amns-2024-0006.
Ding H, Zuo X. Construction of insurance consumers' purchasing behavior model based on big data analysis. Applied Mathematics and Nonlinear Sciences. 2024;9(1). doi:10.2478/amns-2024-0006.
Ding, H. and Zuo, X. (2024), ‘Construction of insurance consumers' purchasing behavior model based on big data analysis’, Applied Mathematics and Nonlinear Sciences, 9(1). Available at: https://doi.org/10.2478/amns-2024-0006.
Ding, Huanhuan, and Xiangbin Zuo. “Construction of Insurance Consumers' Purchasing Behavior Model Based on Big Data Analysis.” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, 2024. https://doi.org/10.2478/amns-2024-0006.
Ding, Huanhuan, and Xiangbin Zuo. “Construction of Insurance Consumers' Purchasing Behavior Model Based on Big Data Analysis.” Applied Mathematics and Nonlinear Sciences 9, no. 1 (2024). https://doi.org/10.2478/amns-2024-0006.
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Published by: Engineering Journals


