Can Google Trends Improve Your Sales Forecast?

Abstract

In this issue, Cui et al. () 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. () result by showing that adding customer search data to time series models improves out‐of‐sample forecast errors.

Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends

“…Whilst appreciating that correlation does not imply causation, these findings indicate that a 72% increase in web search patterns can signify a 72% increase in Google shopping patterns related to "Burberry". This justifies the consideration of web searches as a proxy for online consumer shopping behaviour to a certain extent and appears to be in line with Hastreiter (2016) and Boone et al (2017) assertions that Google Trends can identify purchase decisions. Moreover, Bloomberg (2017) reported that Google Trends was able to predict a slowdown in Salvatore Ferragamo SpA sales six to nine months before it happened in 2015, and that Prada reports one of the highest correlations between Google web searches and revenue growth.…”

“…With e-commerce expected to account for 36% of global fashion retail sales by 2020 (Meena 2018), all brands If a fashion company could find evidence of a significant positive correlation between its historical sales for a product and consumer interest in the said product, as indicated via fashion consumer Google Trends, then it is reasonable to assume that Google Trends could serve as a potential indicator for changes in future sales. It is noteworthy that Boone et al (2017) found evidence of Google Trends improving sales forecasts. Therefore, it would be useful if fashion brands can accurately forecast future seasonality movements in fashion consumer Google Trends, enabling better decisions on what to stock, when to stock and how much to stock, in addition to using consumer trends to determine when to reduce and increase the price points from a marketing perspective.…”

Sustainable Supply Chains in the Age of AI and Digitization: Research Challenges and Opportunities

“…() use the quantity and quality of Facebook comments to improve forecast errors, while Boone et al. () use consumer searchers on Google to improve product forecasts.…”

“…User-generated content from social media and Google searchers can now be incorporated into product forecasts. Cui et al (2018) use the quantity and quality of Facebook comments to improve forecast errors, while Boone et al (2018) use consumer searchers on Google to improve product forecasts.…”

“…This is certainly a controversial issue-one might argue that actionable insights do not require an explanation but add value. In Boone et al (2018), we were trying to predict sales of this product that was often eaten as an appetizer that was of Italian/French origin. In addition to using historical data, we used the Google searchers for the term "Antipasto" and "Hors d'Oeuvres" to help improve forecasts-the idea was that people searching those terms have an intent to purchase.…”