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Predictive Analytics Improves Sales Forecasts for a Pop-up Retailer

Author

Listed:
  • Marlene A. Smith

    (Business Analytics, Business School, University of Colorado Denver, Denver, Colorado 80217)

  • Murray J. Côté

    (Department of Health Policy and Management, Texas A&M University, College Station, Texas 77843)

Abstract

Pop-up retailing involves short bursts of novel product offerings that are quickly withdrawn from the market. We describe an industry/university collaboration designed to improve sales forecasting for an organization that has adopted pop-up retailing as its exclusive business model. Early in the company’s history, the generation of sales forecasts relied heavily on expert opinion, a method that resulted in costly overstock of merchandise inventory. Accordingly, the organization developed a test market protocol in which small numbers of items are sold during a test market period to gauge future demand. Application of least absolute shrinkage and selection operator (lasso) and stochastic gradient boosting methodologies to the test market data along with other information about 508 products resulted in notable improvement in forecast accuracy over expert opinion. Specifically, the percentage of items that went unsold dropped by about 40% when using the predictive analytics tools in place of expert opinion. This striking result reflects, in part, the difficulty of using expert opinion to forecast sales of new, trendy merchandise in the absence of historical time-series sales information. By using the predictive analytics sales forecasts, the company now manufactures fewer products that never sell and, in general, manages its supply chain more effectively.

Suggested Citation

  • Marlene A. Smith & Murray J. Côté, 2022. "Predictive Analytics Improves Sales Forecasts for a Pop-up Retailer," Interfaces, INFORMS, vol. 52(4), pages 379-389, July.
    • Handle: RePEc:inm:orinte:v:52:y:2022:i:4:p:379-389
    DOI: 10.1287/inte.2022.1119
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    References listed on IDEAS

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    1. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    2. Felipe Caro & Jérémie Gallien & Miguel Díaz & Javier García & José Manuel Corredoira & Marcos Montes & José Antonio Ramos & Juan Correa, 2010. "Zara Uses Operations Research to Reengineer Its Global Distribution Process," Interfaces, INFORMS, vol. 40(1), pages 71-84, February.
    3. Rudkowski, Janice & Heney, Chelsea & Yu, Hong & Sedlezky, Sean & Gunn, Frances, 2020. "Here Today, Gone Tomorrow? Mapping and modeling the pop-up retail customer journey," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    4. Shuyun Ren & Hau-Ling Chan & Pratibha Ram, 2017. "A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty," Annals of Operations Research, Springer, vol. 257(1), pages 335-355, October.
    5. Ashish Sood & Gareth M. James & Gerard J. Tellis, 2009. "Functional Regression: A New Model for Predicting Market Penetration of New Products," Marketing Science, INFORMS, vol. 28(1), pages 36-51, 01-02.
    6. Marshall Fisher & Kumar Rajaram, 2000. "Accurate Retail Testing of Fashion Merchandise: Methodology and Application," Marketing Science, INFORMS, vol. 19(3), pages 266-278, June.
    7. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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