Can google trends improve sales forecasts on a product level?

被引：4

作者：

Fritzsch, Benjamin ^{[1
]}

Wenger, Kai ^{[2
]}

Sibbertsen, Philipp ^{[2
]}

Ullmann, Georg ^{[1
]}

机构：

[1] Inst Integrierte Prod Hannover gGmbH, Hannover, Germany

[2] Leibniz Univ Hannover, Sch Econ & Management, Hannover, Germany

来源：

APPLIED ECONOMICS LETTERS | 2020年 / 27卷 / 17期

关键词：

Google econometrics; forecasting; search query data; SEARCH DATA; UNEMPLOYMENT;

D O I：

10.1080/13504851.2019.1686110

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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.

引用

页码：1409 / 1414

页数：6

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