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

Authors:
Alan S. Abrahams;Adrian B. Becker;Daniel Sabido;Rosskyn D'Souza;George Makriyiannis;Michal Krasnodebski
Affiliations:
BIT Department, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, United States;MIT Operations Research Center, 77 Mass Avenue, Building E40-130, Cambridge, MA 02139-43072, United States;University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA 19104, United States;University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA 19104, United States;University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA 19104, United States;University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA 19104, United States
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

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.