Can google trends improve sales forecasts on a product level?

ABSTRACT

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.

KEYWORDS:

• Google econometrics
• forecasting
• search query data

JEL CLASSIFICATION:

• C12
• C22
• M11

Acknowledgments

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.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Deutsche Forschungsgemeinschaft [Ul419/7 - 1].