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An application of learning machines to sales forecasting under promotions

Author

Listed:
  • Gianni Di Pillo

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Vittorio Latorre

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Stefano Lucidi

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Enrico Procacci

    (ACT Solutions)

Abstract

This paper deals with sales forecasting in retail stores of large distribution. For several years statistical methods such as ARIMA and Exponential Smoothing have been used to this aim. However the statistical methods could fail if high irregularity of sales are present, as happens in case of promotions, because they are not well suited to model the nonlinear behaviors of the sales process. In the last years new methods based on Learning Machines are being employed for forecasting problems. These methods realize universal approximators of non linear functions, thus resulting more able to model complex nonlinear phenomena. The paper proposes an assessment of the use ofLearning Machines for sales forecasting under promotions, and a comparison with the statistical methods, making reference to two real world cases. The learning machines have been trained using several configuration of input attributes, to point out the importance of a suitable inputs selection.

Suggested Citation

  • Gianni Di Pillo & Vittorio Latorre & Stefano Lucidi & Enrico Procacci, 2013. "An application of learning machines to sales forecasting under promotions," DIAG Technical Reports 2013-04, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    • Handle: RePEc:aeg:report:2013-04
    as

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    File URL: http://www.dis.uniroma1.it/~bibdis/RePEc/aeg/report/2013-04.pdf
    File Function: Revised version, 2013
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    References listed on IDEAS

    as
    1. Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, vol. 16(4), pages 370-391.
    2. Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
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    Cited by:

    1. Sule Birim & Ipek Kazancoglu & Sachin Kumar Mangla & Aysun Kahraman & Yigit Kazancoglu, 2024. "The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods," Annals of Operations Research, Springer, vol. 339(1), pages 131-161, August.
    2. G. Di Pillo & V. Latorre & S. Lucidi & E. Procacci, 2016. "An application of support vector machines to sales forecasting under promotions," 4OR, Springer, vol. 14(3), pages 309-325, September.

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    More about this item

    Keywords

    Learning Machines; Neural networks; Radial basis functions; Support vector machines; Sales forecasting; Promotion policies; Nonlinear optimization;
    All these keywords.

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