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GfK Marketing Intelligence Review

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1865-5866
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On Storks and Babies: Correlation, Causality and Field Experiments

Anja Lambrecht
  • Corresponding author
  • Associate Professor of Marketing, London Business School, United Kingdom of Great Britain and Northern Ireland
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/ Catherine E. Tucker
Published Online: 2016-10-28 | DOI: https://doi.org/10.1515/gfkmir-2016-0012

Abstract

The explosion of available data has created much excitement among marketing practitioners about their ability to better understand the impact of marketing investments. Big data allows for detecting patterns and often it seems plausible to interpret them as causal. While it is quite obvious that storks do not bring babies, marketing relationships are usually less clear. Apparent “causalities” often fail to hold up under examination. If marketers want to be sure not to walk into a causality trap, they need to conduct field experiments to detect true causal relationships. In the present digital environment, experiments are easier than ever to execute. However, they need to be prepared and interpreted with great care in order to deliver meaningful and genuinely causal results that help improve marketing decisions.

Keywords: Correlation; Causality; Field Experiments; Field Tests; Causal Inference

References

  • Lambrecht, A.; Tucker C. (2015): “When Does Retargeting Work? Information Specificity in Online Advertising,” Journal of Marketing Research, Vol. 50 (5), pp. 561-576.Web of ScienceGoogle Scholar

  • “When Personalized Ads Really Work.,” https://hbr.org/2013/06/marketers-serve-no-ad-before-iGoogle Scholar

  • “Field Experiments in Marketing,” working paper. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2630209Google Scholar

  • Lewis, R. A.; Rao, J. M. (2015): “The Unfavorable Economics of Measuring the Returns to Advertising,” Quarterly Journal of Economics, Vol. 130 (4), pp. 1941-1973.Web of ScienceGoogle Scholar

About the article

Published Online: 2016-10-28

Published in Print: 2016-11-01


Citation Information: GfK Marketing Intelligence Review, ISSN (Online) 1865-5866, DOI: https://doi.org/10.1515/gfkmir-2016-0012.

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© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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