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An extended Huff-model for robustly benchmarking and predicting retail network performance

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  • M. DE BEULE
  • D. VAN DEN POEL
  • N. VAN DE WEGHE

Abstract

spatial competition between stores of the same brand, brand attraction based on actual brand performance and spatially variable substitution. The model uses only publicly available or easily acquirable data as input, whereas model output is extensively validated on various levels. These levels include com- parison of modeled and real market shares on block, store and brand level for the Belgian food market. Results show that multi-objective optimization of model parameters yields comparable results on block level to other models in the literature but improved results on store and brand levels, thereby en- suring model robustness. This robustness also enables the application of the model for various business purposes as store location determination, lea et distribution optimization, store and store concept benchmarking, without loss of spatial generality.

Suggested Citation

  • M. De Beule & D. Van Den Poel & N. Van De Weghe, 2013. "An extended Huff-model for robustly benchmarking and predicting retail network performance," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/866, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:13/866
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    File URL: http://wps-feb.ugent.be/Papers/wp_13_866.pdf
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    References listed on IDEAS

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    1. Peter Davis, 2006. "Spatial competition in retail markets: movie theaters," RAND Journal of Economics, RAND Corporation, vol. 37(4), pages 964-982, December.
    2. Dinesh Kumar Gauri & Janos Gabor Pauler & Minakshi Trivedi, 2009. "Benchmarking Performance in Retail Chains: An Integrated Approach," Marketing Science, INFORMS, vol. 28(3), pages 502-515, 05-06.
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    5. Peter Davis, 2006. "Spatial competition in retail markets: movie theaters," RAND Journal of Economics, The RAND Corporation, vol. 37(4), pages 964-982, December.
    6. B Suman & P Kumar, 2006. "A survey of simulated annealing as a tool for single and multiobjective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1143-1160, October.
    7. Ghosh, Avijit, 1984. "Parameter nonstationarity in retail choice models," Journal of Business Research, Elsevier, vol. 12(4), pages 425-436, December.
    8. Tommi Orpana & Jouko Lampinen, 2003. "Building Spatial Choice Models from Aggregate Data," Journal of Regional Science, Wiley Blackwell, vol. 43(2), pages 319-348, May.
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    Cited by:

    1. Jay Mittal, 2017. "Valuing Visual Accessibility of Scenic Landscapes in a Single Family Housing Market: A Spatial Hedonic Approach," ERES eres2017_1, European Real Estate Society (ERES).
    2. Wieland, Thomas, 2014. "Räumliches Einkaufsverhalten und Standortpolitik im Einzelhandel unter Berücksichtigung von Agglomerationseffekten: Theoretische Erklärungsansätze, modellanalytische Zugänge und eine empirisch-ökonome," MPRA Paper 77163, University Library of Munich, Germany.
    3. Wieland, Thomas, 2015. "Nahversorgung im Kontext raumökonomischer Entwicklungen im Lebensmitteleinzelhandel: Konzeption und Durchführung einer GIS-gestützten Analyse der Strukturen des Lebensmitteleinzelhandels und der Nahve," MPRA Paper 77145, University Library of Munich, Germany.
    4. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.

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