Massively Categorical Variables: Revealing the Information in Zip Codes

Thomas Steenburgh, Andrew Ainslie () and Peder Hans Engebretson ()
Additional contact information
Andrew Ainslie: University of California, Los Angeles, Los Angeles, California 90095
Peder Hans Engebretson: ClearInfo, Denver, Colorado

Marketing Science, 2003, vol. 22, issue 1, 40-57

Abstract: We introduce the idea of a massively categorical variable, a variable such as zip code that takes on too many values to treat in the standard manner. We show how to use a massively categorical variable directly as an explanatory variable. As an application of this concept, we explore several of the issues that analysts confront when trying to develop a direct marketing campaign. We begin by pointing out that the data contained in many of the common sources are masked through aggregation in order to protect consumer privacy. This creates some difficulty when trying to construct models of individual level behavior. We show how to take full advantage of such data through a hierarchical Bayesian variance components (HBVC) model. The flexibility of our approach allows us to combine several sources of information, some of which may not be aggregated, in a coherent manner. We show that the conventional modeling practice understates the uncertainty with regard to its parameter values. We explore an array of financial considerations, including ones in which the marginal benefit is non-linear, to make robust model comparisons. To implement the decision rules that determine the optimal number of prospects to contact, we develop an algorithm based on the Monte Carlo Markov chain output from parameter estimation. We conclude the analysis by demonstrating how to determine an organization's willingness to pay for additional data.

Keywords: direct marketing; categorical variables; hierarchical bayes analysis; variance components; decision theory (search for similar items in EconPapers)
Date: 2003
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.22.1.40.12847 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:22:y:2003:i:1:p:40-57

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().