Bayesian multi-resolution spatial analysis with applications to marketing
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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 (Quantitative Marketing and Economics 5:361–400, 2007), and (ii) the Pathtracker® data in Hui et al. (Marketing Science 28:566–572, 2009b; Journal of Consumer Research 36:478–493, 2009c) 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.
KeywordsSpatial analysis Bayesian modeling Bayesian model selection
- Banerjee, S., Gelfand, A. E., & Carlin, B. P. (2003). Hierarchical modeling and analysis of spatial data. Chapman and Hall.Google Scholar
- Barbujani, G., Jacquez, G. M., & Ligi, L. (1990). Diversity of some gene frequencies in European and Asian populations V. Steep multilocus clines. American Journal of Human Genetics, 47, 867–875.Google Scholar
- Bocquet-Appel, J. P., & Bacro, J. N. (1994). Generalized wombling. Systematic Biology, 43(3), 442–448.Google Scholar
- Huang, Y., Hui, S., Inman, J., & Suher, J. (2012). Capturing the ‘First Moment of Truth’: Understanding point-of-purchase drivers of unplanned consideration and purchase. Working Paper.Google Scholar
- Jacquez, G. M., & Greiling, D. A. (2003). Geographic boundaries in breast, lung, and colorectal cancers in relation to exposure to air toxics in Long Island, New York. International Journal of Health Geographics, 2(4), available at http://www.ij-healthgeographics.com/content/2/1/4.
- Lawson, A. B., Biggeri, A., Bohning, D., Lesaffre, E., Viel, J.-F., & Bertollini, R. (1999). Disease mapping and risk assessment for public health decision making. Chichester: Wiley.Google Scholar
- Mollie. (1996). Bayesian mapping of disease. In W. Gilks, S. Richardson, & D. J. Spiegelhalter (Eds.), Markov chain Monte Carlo in practice. London: Chapman and Hall.Google Scholar
- Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17–33.Google Scholar
- Robert, C., & Casella, G. (2004). Monte Carlo statistical methods, 2nd Edn, Springer.Google Scholar
- Rossi, P. E., Allenby, G. M., & McCulloch, R. (2005). Bayesian statistics and marketing., Wiley.Google Scholar
- Sorensen, H. (2003). The science of shopping. Marketing Research, 15(3), 30–35.Google Scholar
- Theil, H. (1954). Linear aggregate of economic relations. Amsterdam: North-Holland.Google Scholar