TY - JOUR

T1 - Can google trends improve sales forecasts on a product level?

AU - Fritzsch, Benjamin

AU - Wenger, Kai

AU - Sibbertsen, Philipp

AU - Ullmann, Georg

N1 - Funding Information: This paper has been composed as part of the research project “Development of a forecasting model to determine short- and medium-term sales using search engine data” (reference number Ul419/7 - 1), which is funded by the German Research Foundation (DFG). We are also grateful to Sennheiser electronic GmbH & Co. KG for sharing their sales data with us. We would like to thank the anonymous referee for his review. We highly appreciate his comments and suggestions.

PY - 2020/10/6

Y1 - 2020/10/6

N2 - Combining standard time series models with search query data can be helpful in predicting sales. We include the search volume of company as well as product-related keywords provided by Google Trends as new predictors in models to forecast sales on a product level. Using weekly data from January 2015 to December 2016 of two products of the audio company Sennheiser we find evidence that using Google Trends data can enhance the prediction performance of conventional models.

AB - Combining standard time series models with search query data can be helpful in predicting sales. We include the search volume of company as well as product-related keywords provided by Google Trends as new predictors in models to forecast sales on a product level. Using weekly data from January 2015 to December 2016 of two products of the audio company Sennheiser we find evidence that using Google Trends data can enhance the prediction performance of conventional models.

KW - forecasting

KW - Google econometrics

KW - search query data

UR - http://www.scopus.com/inward/record.url?scp=85074787212&partnerID=8YFLogxK

U2 - 10.1080/13504851.2019.1686110

DO - 10.1080/13504851.2019.1686110

M3 - Article

AN - SCOPUS:85074787212

VL - 27

SP - 1409

EP - 1414

JO - Applied Economics Letters

JF - Applied Economics Letters

SN - 1350-4851

IS - 17

ER -