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Can Google Trends Improve Your Sales Forecast?
Author(s) -
Boone Tonya,
Ganeshan Ram,
Hicks Robert L.,
Sanders Nada R.
Publication year - 2018
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.12839
Subject(s) - sample (material) , computer science , bolster , quality (philosophy) , product (mathematics) , work (physics) , operations research , mathematics , mechanical engineering , philosophy , chemistry , geometry , epistemology , chromatography , engineering
In this issue, Cui et al. ([Cui, R., 2018]) show how the quantity and quality of user‐generated Facebook data can be used to enhance product forecasts. The intent of this note is to show how another type of user‐generated content—customer search data, specifically one obtained from Google Trends—can be used to reduce out‐of‐sample forecast errors. Based on our work with an online retailer, we bolster Cui et al. ([Cui, R., 2018]) result by showing that adding customer search data to time series models improves out‐of‐sample forecast errors.