Bayesian Multi-Resolution Spatial Analysis with Applications to Marketing

55 Pages Posted: 1 Jun 2011 Last revised: 21 Mar 2012

Eric Bradlow

University of Pennsylvania - Marketing Department

Sam K. Hui

New York University (NYU) - Department of Marketing

Date Written: February 29, 2012

Abstract

Marketing researchers have become increasingly interested in spatial datasets. A main challenge of analyzing spatial data is that researchers must a priori choose the size and make-up of the areal units, hence the resolution of the analysis. Analyzing the data at a resolution that is too high may mask “macro” patterns, while analyzing the data at a resolution that is too low may result in aggregation bias. Thus, ideally marketing researchers would want a “data-driven” method to determine the “optimal” resolution of analysis, and at the same time automatically explore the same dataset under different resolutions, to obtain a full set of empirical insights to help with managerial decision making.

In this paper, we propose a new approach for multi-resolution spatial analysis that is based on Bayesian model selection. We demonstrate our method using two recent marketing datasets from published studies: (i) the Netgrocer spatial sales data in Bell and Song (2007), and (ii) the Pathtracker® data in Hui et al. (2009b,c) that track shoppers’ in-store movements. In both cases, our method allows researchers to not only automatically select the resolution of the analysis, but also analyze the data under different resolutions to understand the variation in insights and robustness to the level of aggregation.

Keywords: Spatial Analysis, Graphical Methods, Statistical Modeling

Suggested Citation

Bradlow, Eric and Hui, Sam K., Bayesian Multi-Resolution Spatial Analysis with Applications to Marketing (February 29, 2012). Available at SSRN: https://ssrn.com/abstract=1856204 or http://dx.doi.org/10.2139/ssrn.1856204

Eric Bradlow

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States
215-898-8255 (Phone)

Sam K. Hui (Contact Author)

New York University (NYU) - Department of Marketing ( email )

Henry Kaufman Ctr
44 W 4 St.
New York, NY
United States

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