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Finance and Economics Vision

Volume 6 nos.1 April 2024 ISSN 2755-3272

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What Should a Car Insurance Company Focus on?

  • Ziwen Liao*†, Xinxin Zhong‡, Di Ma§
    *Engineering Department (Industrial Engineering), California State Polytechnic University, Pomona, Los Angeles, 91766, USA
    †Corresponding author. Email: zliao@cpp.edu
    ‡Engineering Department (Information Management of Business), University College London, London, WC1E 6BT, UK
    §Department of Mathematics, University of California of San Diego, La Jolla, 92093, USA

  • Following the pandemic’s economic impact, auto insurance companies require recovery. To assist companies in understanding their customers better and creating successful strategies, relevant data was collected. This data revealed correlations between customers’ lifetime value and 24 influencing factors. Out of these factors, nine were selected as the primary focus of the research. It is hypothesized that income, vehicle class, and driving location are likely to be the most influential factors in customers’ lifetime value. To validate this hypothesis, we will use R Studio software to analyze whether a significant correlation exists between customers’ lifetime value and these nine independent variables. The analysis methods include the t-test, simple regression model, multiple linear regression model, and logistic regression model. The findings suggest that monthly premiums, marital status, vehicle class, income, coverage, and location may contribute to customers’ lifetime value. 

      Key words: Auto-insurance companies, Marketing strategies, CLV (customers’ lifetime value)

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  • Following the pandemic’s economic impact, auto insurance companies require recovery. To assist companies in understanding their customers better and creating successful strategies, relevant data was collected. This data revealed correlations between customers’ lifetime value and 24 influencing factors. Out of these factors, nine were selected as the primary focus of the research. It is hypothesized that income, vehicle class, and driving location are likely to be the most influential factors in customers’ lifetime value. To validate this hypothesis, we will use R Studio software to analyze whether a significant correlation exists between customers’ lifetime value and these nine independent variables. The analysis methods include the t-test, simple regression model, multiple linear regression model, and logistic regression model. The findings suggest that monthly premiums, marital status, vehicle class, income, coverage, and location may contribute to customers’ lifetime value.