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Efficient modelling of individual consumer preferences: facilitating agent-based online markets

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  • Frank A. LoPinto
  • Cliff T. Ragsdale

Abstract

As business-to-consumer e-commerce matures, better tools are needed to help consumers locate products they desire. Currently, user-driven searching, sorting and filtering are widely employed to align consumers and products. These processes could be improved by a quick and easy way to capture and model individual consumer preferences for particular product attributes. We present such a methodology using conjoint analysis and neural networks. This paper presents a study where preferences are elicited using a survey instrument, models of individual participants' preferences are constructed, and prediction accuracy is reported. This proposed methodology performs well for individuals in the example explored here.

Suggested Citation

  • Frank A. LoPinto & Cliff T. Ragsdale, 2010. "Efficient modelling of individual consumer preferences: facilitating agent-based online markets," International Journal of Electronic Marketing and Retailing, Inderscience Enterprises Ltd, vol. 3(1), pages 66-81.
    • Handle: RePEc:ids:ijemre:v:3:y:2010:i:1:p:66-81
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