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Massively Categorical Variables: Revealing the Information in Zip Codes


Author Info

  • Thomas J. Steenburgh

    (Yale University, New Haven, Connecticut 06520)

  • Andrew Ainslie

    (University of California, Los Angeles, Los Angeles, California 90095)

  • Peder Hans Engebretson

    (ClearInfo, Denver, Colorado)


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.

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Bibliographic Info

Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 22 (2003)
Issue (Month): 1 (August)
Pages: 40-57

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Handle: RePEc:inm:ormksc:v:22:y:2003:i:1:p:40-57

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Related research

Keywords: direct marketing; categorical variables; hierarchical bayes analysis; variance components; decision theory;


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Cited by:
  1. P. Baecke & D. Van Den Poel, 2010. "Improving purchasing behavior predictions by data augmentation with situational variables," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/658, Ghent University, Faculty of Economics and Business Administration.
  2. Ron Borzekowski & Raphael Thomadsen & Charles Taragin, 2005. "Competition and price discrimination in the market for mailing lists," Finance and Economics Discussion Series 2005-56, Board of Governors of the Federal Reserve System (U.S.).
  3. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
  4. van Dijk, Bram & Paap, Richard, 2008. "Explaining individual response using aggregated data," Journal of Econometrics, Elsevier, vol. 146(1), pages 1-9, September.
  5. Matthew Nagler, 2006. "An exploratory analysis of the determinants of cooperative advertising participation rates," Marketing Letters, Springer, vol. 17(2), pages 91-102, April.
  6. P. Baecke & D. Van Den Poel, 2012. "Including Spatial Interdependence in Customer Acquisition Models: a Cross-Category Comparison," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/788, Ghent University, Faculty of Economics and Business Administration.
  7. P. Baecke & D. Van Den Poel, 2012. "Improving Customer Acquisition Models by Incorporating Spatial Autocorrelation at Different Levels of Granularity," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/819, Ghent University, Faculty of Economics and Business Administration.


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