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—An Assortmentwide Decision-Support System for Dynamic Pricing and Promotion Planning in DIY Retailing

Author

Listed:
  • Martin Natter

    (Johann Wolfgang Goethe-University Frankfurt, Mertonstr. 17, D-60054 Frankfurt/Main, Germany)

  • Thomas Reutterer

    (Vienna University of Economics and Business Administration, Augasse 2-6, A-1090 Vienna, Austria)

  • Andreas Mild

    (Vienna University of Economics and Business Administration, Nordbergstr. 15, A-1090 Vienna, Austria)

  • Alfred Taudes

    (Vienna University of Economics and Business Administration, Nordbergstr. 15, A-1090 Vienna, Austria)

Abstract

The main objective of this report is to describe a decision-support system for dynamic retail pricing and promotion planning. Our weekly demand model incorporates price, reference price effects, seasonality, article availability information, features, and discounts. Building on previous research, we quantify demand interdependencies and integrate the resulting profit-lifting effects into the optimal pricing model. The methodology was developed and implemented at bauMax, an Austrian do-it-yourself retailer. Along with the practical requirements, an objective function was employed that can be used as a vehicle for implementing a retailer's strategy. Eight pricing rounds with thousands of different stock-keeping units have each served as a testing ground for our approach. Based on various benchmarking methods, a positive impact on profit was reported. The currently implemented marketing decision-support system increased gross profit on average by 8.1 and sales by 2.1%.

Suggested Citation

  • Martin Natter & Thomas Reutterer & Andreas Mild & Alfred Taudes, 2007. "—An Assortmentwide Decision-Support System for Dynamic Pricing and Promotion Planning in DIY Retailing," Marketing Science, INFORMS, vol. 26(4), pages 576-583, 07-08.
    • Handle: RePEc:inm:ormksc:v:26:y:2007:i:4:p:576-583
    DOI: 10.1287/mksc.1060.0217
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    References listed on IDEAS

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    Cited by:

    1. Alfred Taudes & Christian Rudloff, 2012. "Integrating inventory control and a price change in the presence of reference price effects: a two-period model," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 75(1), pages 29-65, February.
    2. Karray, Salma & Martín-Herrán, Guiomar & Zaccour, Georges, 2020. "Pricing of demand-related products: Can ignoring cross-category effect be a smart choice?," International Journal of Production Economics, Elsevier, vol. 223(C).
    3. Sibdari, Soheil & Pyke, David F., 2010. "A competitive dynamic pricing model when demand is interdependent over time," European Journal of Operational Research, Elsevier, vol. 207(1), pages 330-338, November.
    4. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    5. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    6. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    7. Anett Weber & Winfried J. Steiner & Stefan Lang, 2017. "A comparison of semiparametric and heterogeneous store sales models for optimal category pricing," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 403-445, March.
    8. Kris Johnson Ferreira & Bin Hong Alex Lee & David Simchi-Levi, 2016. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 69-88, February.
    9. Reiner, Gerald & Fichtinger, Johannes, 2009. "Demand forecasting for supply processes in consideration of pricing and market information," International Journal of Production Economics, Elsevier, vol. 118(1), pages 55-62, March.
    10. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    11. Agarwal, Manoj K. & Ma, Zecong & Park, Chang Hee & Zheng, Yilong, 2022. "The impact of a manufacturer’s financial liquidity on its market strategies and pricing and promotion decisions in retail grocery markets," Journal of Business Research, Elsevier, vol. 142(C), pages 844-857.
    12. Gary L. Lilien & John H. Roberts & Venkatesh Shankar, 2013. "Effective Marketing Science Applications: Insights from the ISMS-MSI Practice Prize Finalist Papers and Projects," Marketing Science, INFORMS, vol. 32(2), pages 229-245, March.
    13. Ullah, Subhan & Attah-Boakye, Rexford & Adams, Kweku & Zaefarian, Ghasem, 2022. "Assessing the influence of celebrity and government endorsements on bitcoin’s price volatility," Journal of Business Research, Elsevier, vol. 145(C), pages 228-239.
    14. Vidal-Sanz, Jose M., 2009. "Optimal risk in marketing resource allocation," DEE - Working Papers. Business Economics. WB wb090868, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    15. Liane Nagengast & Christina Heidemann & Thomas Rudolph, 2013. "Der kombinierte Einsatz von Sortimentsreduktion und Regalkategorisierung zur Sortimentsoptimierung–," Schmalenbach Journal of Business Research, Springer, vol. 65(7), pages 666-687, December.
    16. Michael Hahsler & Radoslaw Karpienko, 2017. "Visualizing association rules in hierarchical groups," Journal of Business Economics, Springer, vol. 87(3), pages 317-335, April.

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