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Incorporating Google Trends Data Into Sales Forecasting

Author

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  • Tonya Boone
  • Ram Ganeshan
  • Robert L. Hicks

Abstract

Forecasters are learning that Internet search data can be valuable additions to their models. In this new study, Tonya, Ram, and Robert show specifically how to take search-based data from Google Trends and build them into an individual firm?s forecasting model. Their case study shows the potential for improved accuracy. Copyright International Institute of Forecasters, 2015

Suggested Citation

  • Tonya Boone & Ram Ganeshan & Robert L. Hicks, 2015. "Incorporating Google Trends Data Into Sales Forecasting," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 38, pages 9-14, Summer.
    • Handle: RePEc:for:ijafaa:y:2015:i:38:p:9-14
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    Citations

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    Cited by:

    1. 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.
    2. Cave, Martin & Pratt, Neil, 2016. "Taking account of service externalities when spectrum is allocated and assigned," Telecommunications Policy, Elsevier, vol. 40(10), pages 971-981.
    3. Jin, Tuofu & Eapen, Alex, 2022. "‘Delayed Forbearance’: Multipoint contact and mutual forbearance in inaugural and subsequent competitive actions," Journal of Business Research, Elsevier, vol. 149(C), pages 938-953.
    4. 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.
    5. Koku, P. Sergius & Farha, Allam Abu, 2020. "Other sources of FDIs in Sub-Saharan Africa: The case of Gulf Cooperation Council states," Journal of Business Research, Elsevier, vol. 119(C), pages 619-626.

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