Idenfying preferred insurance products using multinomial logistic regression, classification trees, and random forest - Cuaresma, Jessa Luzelle S.
Abstract (Targeting is one of the strategies implemented by marketers to achieve efficiency in the promotion of its products. In the life insurance industry, targeting can be useful to achieve higher penetration in the market. However, it is still a challenge to adopt this methodology in the industry given the limitation on the knowledge on how the available data can be utilized. This study aims to explore the application of several predictive modeling techniques in identifying client characteristics and behaviors that determine their preference on life insurance products. Models that are considered are Classification Trees under CART, Multinomial Logistics Regression, and Random Forest. Results show that while the models are not capable of predicting minority classes accurately, they are able to generate insights on the predictor relationship which can be used by marketers in crafting strategy for distribution, promotion, and product development. Such insights include the preference of Unit-Linked products for protection, by an immediate family, and by the insured himself if he has high earnings).
June 2018
Thesis (M.S. Statistics)--University of the Philippines, Diliman.
Keywords: Classification trees; Multinomial logistic regression