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Bayesian multi-resolution spatial analysis with applications to marketing

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  • Sam Hui

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  • Eric Bradlow
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    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 (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. Copyright Springer Science+Business Media, LLC 2012

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    File URL: http://hdl.handle.net/10.1007/s11129-012-9122-y
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    Bibliographic Info

    Article provided by Springer in its journal Quantitative Marketing and Economics.

    Volume (Year): 10 (2012)
    Issue (Month): 4 (December)
    Pages: 419-452

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    Handle: RePEc:kap:qmktec:v:10:y:2012:i:4:p:419-452

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    Web page: http://www.springerlink.com/link.asp?id=111240

    Related research

    Keywords: Spatial analysis; Bayesian modeling; Bayesian model selection;

    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Frenkel Ter Hofstede & Michel Wedel & Jan-Benedict E.M. Steenkamp, 2002. "Identifying Spatial Segments in International Markets," Marketing Science, INFORMS, vol. 21(2), pages 160-177, July.
    2. David Bell & Sangyoung Song, 2007. "Neighborhood effects and trial on the internet: Evidence from online grocery retailing," Quantitative Marketing and Economics, Springer, vol. 5(4), pages 361-400, December.
    3. Tal Garber & Jacob Goldenberg & Barak Libai & Eitan Muller, 2004. "From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success," Marketing Science, INFORMS, vol. 23(3), pages 419-428, August.
    4. Sam K. Hui & Eric T. Bradlow & Peter S. Fader, 2009. "Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping Path and Purchase Behavior," Journal of Consumer Research, University of Chicago Press, vol. 36(3), pages 478 - 493.
    5. Bradlow, E. & Bronnenberg, B.J.J.A.M. & Russell, G.J. & Arora, N. & Bell, D. & Deepak, S.D. & Hofstede, F. ter & Sismeiro, C. & Thomadsen, R. & Yang, S., 2005. "Spatial models in marketing," Open Access publications from Tilburg University urn:nbn:nl:ui:12-332173, Tilburg University.
    6. Eric Bradlow & Bart Bronnenberg & Gary Russell & Neeraj Arora & David Bell & Sri Duvvuri & Frankel Hofstede & Catarina Sismeiro & Raphael Thomadsen & Sha Yang, 2005. "Spatial Models in Marketing," Marketing Letters, Springer, vol. 16(3), pages 267-278, December.
    7. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    8. Sam K. Hui & Peter S. Fader & Eric T. Bradlow, 2009. "Path Data in Marketing: An Integrative Framework and Prospectus for Model Building," Marketing Science, INFORMS, vol. 28(2), pages 320-335, 03-04.
    9. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    10. van der Lans, Ralf & Pieters, Rik & Wedel, Michel, 2008. "Eye-Movement Analysis of Search Effectiveness," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 452-461, June.
    11. John Liechty & Rik Pieters & Michel Wedel, 2003. "Global and local covert visual attention: Evidence from a bayesian hidden markov model," Psychometrika, Springer, vol. 68(4), pages 519-541, December.
    12. Bronnenberg, B.J.J.A.M. & Dhar, S. & Dube, J.-P., 2007. "Consumer packaged goods in the United States: National brands, local branding," Open Access publications from Tilburg University urn:nbn:nl:ui:12-332116, Tilburg University.
    13. Duncan Fong & Wayne DeSarbo, 2007. "A Bayesian methodology for simultaneously detecting and estimating regime change points and variable selection in multiple regression models for marketing research," Quantitative Marketing and Economics, Springer, vol. 5(4), pages 427-453, December.
    14. James G. Booth & George Casella & James P. Hobert, 2008. "Clustering using objective functions and stochastic search," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 119-139.
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