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Unfolding large-scale marketing data

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  • Ho, Ying
  • Chung, Yuho
  • Lau, Kin-nam

Abstract

Marketers use multidimensional unfolding to understand the relationship between customer preferences and product positioning through a joint display of customers and brands on a map. In today's information age, unfolding marketing data is challenging, as marketing data can be large in size and high-dimensional in nature. Moreover, the unfolding model is always subject to the curse of the degeneracy problem. We propose a new approach to unfold customer-by-brand transaction data and customer-by-customer network data in a reduced space. The proposed approach can recover the true configuration with reasonable accuracy, is scalable in terms of the number of estimated parameters, and can produce non-degenerate solution. We compare its performance with existing approaches by simulation experiments and real data analyses with interesting results.

Suggested Citation

  • Ho, Ying & Chung, Yuho & Lau, Kin-nam, 2010. "Unfolding large-scale marketing data," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 119-132.
    • Handle: RePEc:eee:ijrema:v:27:y:2010:i:2:p:119-132
    DOI: 10.1016/j.ijresmar.2009.12.009
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    References listed on IDEAS

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    1. Frank Busing & Patrick Groenen & Willem Heiser, 2005. "Avoiding degeneracy in multidimensional unfolding by penalizing on the coefficient of variation," Psychometrika, Springer;The Psychometric Society, vol. 70(1), pages 71-98, March.
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    3. Wayne DeSarbo & Vithala R. Rao, 1986. "A Constrained Unfolding Methodology for Product Positioning," Marketing Science, INFORMS, vol. 5(1), pages 1-19.
    4. Wayne S. Desarbo & Ajay K. Manrai, 1992. "A New Multidimensional Scaling Methodology for the Analysis of Asymmetric Proximity Data in Marketing Research," Marketing Science, INFORMS, vol. 11(1), pages 1-20.
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    Cited by:

    1. Tammo Bijmolt & Michel Velden, 2012. "Multiattribute perceptual mapping with idiosyncratic brand and attribute sets," Marketing Letters, Springer, vol. 23(3), pages 585-601, September.
    2. van de Velden, M. & de Beuckelaer, A. & Groenen, P.J.F. & Busing, F.M.T.A., 2011. "Nonmetric Unfolding of Marketing Data: Degeneracy and Stability," ERIM Report Series Research in Management ERS-2011-006-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    3. Tammo H.A. Bijmolt & Michel Wedel & Wayne S. DeSarbo, 2021. "Adaptive Multidimensional Scaling: Brand Positioning Based on Decision Sets and Dissimilarity Judgments," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(1), pages 1-15, June.
    4. Jianhui Zhang & Junxuan Zhu, 2014. "Research Intelligent Precision Marketing of E-commerce Based on the Big Data," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 5(1), pages 33-38, February.
    5. Dawn Iacobucci & Maria Petrescu & Anjala Krishen & Michael Bendixen, 2019. "The state of marketing analytics in research and practice," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 152-181, September.

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