Renewal Prediction For Individual Property Insurance Using Binary Logistic Regression in PT XYZ Insurance
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Abstract
Individual property insurance is a crucial product that offers financial protection for personal assets such as residential buildings, commercial properties, and private dwellings against risks such as fire, theft, and natural disasters. However, retaining customers and encouraging the renewal of policies continues to be a critical challenge in the insurance industry. This study aims to predict renewal decisions of individual property insurance policyholders at PT XYZ Insurance using a binary logistic regression model and to identify key factors that influence these decisions. The model was built using 178 policyholder records and evaluated several independent variables, including premium, discount, and admin cost. The analysis indicates that discount and admin cost are statistically significant in influencing renewal behavior. Discount, with a p-value of 0.034, shows a negative relationship suggesting that higher discounts may reduce the likelihood of renewal. On the other hand, admin cost, with a p-value of 0.053, shows a positive association, indicating that higher operational spending may contribute to increased renewal probability. The premium variable was found to be statistically insignificant in this model. These findings underline the importance of pricing strategy and cost structure in influencing customer retention. The study provides practical implications for insurance companies to refine their marketing and pricing strategies, particularly by reassessing discount allocation and expense effectiveness. Future research may incorporate demographic, behavioral, and regional variables to enhance the robustness and accuracy of renewal prediction in the property insurance domain.
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