A cross-industry analysis of the spillover effect in paid search advertising


For the management of paid search advertising campaigns, metrics collected at keyword-level are often used in practice whereas the users’ search process is of secondary importance and thus wholly or partially neglected. In contrast to brand-related keywords (“T-mobile contract”), general keywords, often referred to as generic (“mobile phone contract”), seem at first glance to be economically unattractive. Extending the approach of Rutz and Bucklin, Journal of Marketing Research, 48(1):87–102 (2011), we investigate the role of generic search activities in paid search advertising across industries using dynamic linear models (DLM). The so-called spillover effect, i. e. the increase in brand-related awareness effected by displaying ads for generic keywords, is investigated by both analyzing individual consumer behavior on the basis of keyword-level data and linking findings on keyword- with findings on individual user-level data. We show that the spillover effect varies across industries and that its consideration, for example for the mobile phone provider investigated here, decreases KPIs such as the cost per order up to 42 %.

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  1. 1.

    See West and Harrison (1997) and Rutz and Bucklin (2011) for a detailed description of the sampling procedures.

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    The full results are available on request.

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    The results are available on request.


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We are indebted to Oliver J. Rutz and Thomas Otter for their support and helpful suggestions. We wish to thank the collaborating firms for providing the data used in this study as well as the anonymous reviewers for their valuable comments which have enabled us to make this work far more generalizable and accessible.

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Correspondence to Florian Nottorf.

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Responsible editor: Christopher Patrick Holland

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Nottorf, F., Funk, B. A cross-industry analysis of the spillover effect in paid search advertising. Electron Markets 23, 205–216 (2013). https://doi.org/10.1007/s12525-012-0117-z

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  • Paid search
  • Online advertising
  • User behavior
  • Bayesian statistics
  • Dynamic linear models
  • JEL classification
  • M37—Advertising
  • L86—Information and Internet Services