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Leveraging Online Search Data as a Source of Marketing Insights

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  • Rex Yuxing Du
  • Tsung-Yiou Hsieh

Abstract

Every year billions of users around the world submit trillions of queries through online search engines such as Google, Bing, Baidu, and Yandex. Over the years, aggregated and anonymized search volume data on keywords contained in all these queries have formed an epic database of human intentions that continues to expand every day. Thanks to platforms such as Google Trends, Google Ads Keyword Planner, Microsoft Advertising Keyword Planner, Baidu Index, and Yandex Wordstat, advertisers can readily assess search engine users’ collective interests over time and across geographic areas to optimize their search engine marketing efforts. In this monograph, we illustrate how online search volume data, indexed or otherwise, can be leveraged as a powerful source of marketing insights for purposes beyond search engine marketing. We do so by offering a brief tutorial on Google Trends and Google Ads Keyword Planner, two popular (and free) platforms for gathering online search trend and volume data, respectively. We review prior studies that have examined the use of aggregate online search data as (1) predictors for nowcasting and forecasting, (2) dependent variables in market response modeling, and (3) proxies for otherwise hard-to-measure constructs. In each of these three areas, we provide specific examples of applications to illustrate the power and versatility of online search data. We conclude by offering several ideas for future research where we see the full potential of online search data is still to be uncovered.

Suggested Citation

  • Rex Yuxing Du & Tsung-Yiou Hsieh, 2023. "Leveraging Online Search Data as a Source of Marketing Insights," Foundations and Trends(R) in Marketing, now publishers, vol. 17(4), pages 227-291, August.
    • Handle: RePEc:now:fntmkt:1700000070
    DOI: 10.1561/1700000070
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    References listed on IDEAS

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    1. Stephens-Davidowitz, Seth, 2014. "The cost of racial animus on a black candidate: Evidence using Google search data," Journal of Public Economics, Elsevier, vol. 118(C), pages 26-40.
    2. Guiyang Xiong & Sundar Bharadwaj, 2014. "Prerelease Buzz Evolution Patterns and New Product Performance," Marketing Science, INFORMS, vol. 33(3), pages 401-421, May.
    3. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
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