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Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact on Optimizing Shelf Arrangements

  • Erjen van Nierop

    ()

    (Faculty of Economics and Business, Department of Marketing, University of Groningen, 9700 AV Groningen, The Netherlands)

  • Dennis Fok

    ()

    (Econometric Institute, Erasmus University Rotterdam 3062 PA Rotterdam, The Netherlands)

  • Philip Hans Franses

    ()

    (Econometric Institute, Erasmus University Rotterdam 3062 PA Rotterdam, The Netherlands)

In this paper, we propose and operationalize a new method for optimizing shelf arrangements. We show that there are important dependencies between the layout of the shelf and stock-keeping unit (SKU) sales and marketing effectiveness. The importance of these dependencies is further shown by the substantive profit gains we obtain with our proposed shelf optimization approach. The basis of our model is a standard sales equation that explains sales using item-specific marketing effect parameters and intercepts. In a Hierarchical Bayes (HB) fashion, we augment this model with a second layer that relates the effect parameters to shelf and SKU descriptors. We also take into account potential endogeneity of facings. After estimating the parameters of the two-level model using Bayesian methodology, we carefully investigate the dependencies of SKU sales and SKU marketing effectiveness on the shelf layout. Next, we search for the shelf arrangement that maximizes the expected total profit using simulated annealing (SA). We appear to be able to increase profits for all the stores analyzed, and our approach appears to outperform well-known rules of thumb.

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File URL: http://dx.doi.org/10.1287/mksc.1080.0365
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Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 27 (2008)
Issue (Month): 6 (11-12)
Pages: 1065-1082

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Handle: RePEc:inm:ormksc:v:27:y:2008:i:6:p:1065-1082
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  1. Jorge M. Silva-Risso & Randolph E. Bucklin & Donald G. Morrison, 1999. "A Decision Support System for Planning Manufacturers' Sales Promotion Calendars," Marketing Science, INFORMS, vol. 18(3), pages 274-300.
  2. Alan L. Montgomery, 1997. "Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data," Marketing Science, INFORMS, vol. 16(4), pages 315-337.
  3. Andrew Lim & Brian Rodrigues & Xingwen Zhang, 2004. "Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization," Management Science, INFORMS, vol. 50(1), pages 117-131, January.
  4. Albert van Dijk & Harald J. van Heerde & Peter S.H. Leeflang & Dick R. Wittink, 2004. "Similarity-Based Spatial Methods to Estimate Shelf Space Elasticities," Quantitative Marketing and Economics, Springer, vol. 2(3), pages 257-277, 09.
  5. Yang, Ming-Hsien & Chen, Wen-Cher, 1999. "A study on shelf space allocation and management," International Journal of Production Economics, Elsevier, vol. 60(1), pages 309-317, April.
  6. Tansev Geylani & Anthony J. Dukes & Kannan Srinivasan, 2007. "Strategic Manufacturer Response to a Dominant Retailer," Marketing Science, INFORMS, vol. 26(2), pages 164-178, 03-04.
  7. Alain Bultez & Philippe Naert, 1988. "SH.A.R.P.: Shelf Allocation for Retailers' Profit," Marketing Science, INFORMS, vol. 7(3), pages 211-231.
  8. Lan Luo & P. K. Kannan & Brian T. Ratchford, 2007. "New Product Development Under Channel Acceptance," Marketing Science, INFORMS, vol. 26(2), pages 149-163, 03-04.
  9. Yang, Ming-Hsien, 2001. "An efficient algorithm to allocate shelf space," European Journal of Operational Research, Elsevier, vol. 131(1), pages 107-118, May.
  10. Marcel Corstjens & Peter Doyle, 1981. "A Model for Optimizing Retail Space Allocations," Management Science, INFORMS, vol. 27(7), pages 822-833, July.
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