Using Google Trends to predict and forecast avocado sales

Di Wu (), Zhenning Xu () and Seung Bach ()
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Di Wu: California State University
Zhenning Xu: California State University
Seung Bach: California State University

Journal of Marketing Analytics, 2023, vol. 11, issue 4, No 7, 629-641

Abstract: Abstract Making a successful sales prediction or forecasting in retail markets remains challenging despite years of practice and efforts. In this study, we attempt to address this challenge by incorporating the Google Trends search data into traditional time series models that feature geodemographic and industrial-level variables for the purpose of predicting Hass avocado sales in different regions of the United States. The results imply that, for conventional Hass avocados, the use of Google Trends search data can produce better predictions than the models without Google Trends search data. Moreover, using categorized Google Trends search data can improve predictive results even more. However, the models with Google Trends search data fail to improve the predictive power for the consumption of organic Hass avocados. The results suggest that categorized Google Trends search data can be helpful in improving prediction and forecasting for various business stakeholders in general.

Keywords: Google Trends; Predictive analytics; Forecasting; Geodemographics; Data visualization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1057/s41270-023-00232-8

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