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Does Online Search Predict Sales? Evidence from Big Data for Car Markets in Germany and the UK

Author

Listed:
  • Georg von Graevenitz
  • Christian Helmers
  • Valentine Millot
  • Oliver Turnbull

Abstract

We use online search data to predict car sales in the German and UK automobile industries. Search data subsume several distinct search motives, which are not separately observable. We develop a model linking search motives to observable search data and sales. The model shows that predictions of sales relying on observable search data as a proxy for prepurchase search will be biased. We show how to remove the biases and estimate the effect of pre-purchase search on sales. To assist identification of this effect, we use the introduction of scrappage subsidies for cars in 2008/2009 as a quasi-natural experiment. We also show that online search data are (i) highly persistent over time, (ii) potentially subject to permanent shocks, and (iii) correlated across products, but to different extent. We address these challenges to estimation and inference by using recent econometric methods for large N, large T panels.

Suggested Citation

  • Georg von Graevenitz & Christian Helmers & Valentine Millot & Oliver Turnbull, 2016. "Does Online Search Predict Sales? Evidence from Big Data for Car Markets in Germany and the UK," Working Papers 71, Queen Mary, University of London, School of Business and Management, Centre for Globalisation Research.
    • Handle: RePEc:cgs:wpaper:71
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    File URL: http://cgr.sbm.qmul.ac.uk/CGRWP71.pdf
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    References listed on IDEAS

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    1. Cuntz, Alexander & Bergquist, Kyle, 2022. "Exclusive content and platform competition in Latin America," Information Economics and Policy, Elsevier, vol. 60(C).
    2. Carolina Castaldi & Sandro Mendonca, 2021. "Regions and trademarks. Research opportunities and policy insights from leveraging trademarks in regional innovation studies," Papers in Evolutionary Economic Geography (PEEG) 2138, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Dec 2021.

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    More about this item

    Keywords

    Online search; Google Trends; Serial correlation; Non-stationarity; Common Correlated Effects; Large Panels;
    All these keywords.

    JEL classification:

    • D - Microeconomics

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