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Demand forecasting with user-generated online information

Author

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  • Schaer, Oliver
  • Kourentzes, Nikolaos
  • Fildes, Robert
Abstract
Recently, there has been substantial research on the augmentation of aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies have reported increases in accuracy, many exhibit design weaknesses, including a lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, even though this may change, as consumers may search initially for pre-purchase information, but later for after-sales support. This study begins by reviewing the relevant literature, then attempts to support the key findings using two forecasting case studies. Our findings are in stark contrast to those in the previous literature, as we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better those that include online information. Our research underlines the need for a thorough forecast evaluation and argues that the usefulness of online platform data for supporting operational decisions may be limited.

Suggested Citation

  • Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:1:p:197-212
    DOI: 10.1016/j.ijforecast.2018.03.005
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    7. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    8. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.
    9. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
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