Inducing a marketing strategy for a new pet insurance company using decision trees

摘要

In this paper, we demonstrate the use of decision tree induction for the creation of a marketing strategy for a new pet insurance company, PetPlan USA. We employ both a traditional C4.5 decision tree approach, and a novel locally profit-optimal decision algorithm, called SBP, to discover the characteristics of profitable demographics for PetPlan to market to. We use publicly available data, including US census data, and veterinary clinic location data as our data sources. We evaluate our results, and give actionable recommendations for the managers of PetPlan USA. Our results indicate that entropy-based decision tree induction approaches, which focus on node purity (predominance of one category over another at each node in the tree), can produce lower profits compared to SBP, which is a novel profit-based decision tree approach.