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

Abstract

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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Notes

  1. 1.

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

  2. 2.

    The full results are available on request.

  3. 3.

    The results are available on request.

References

  1. Agarwal, A., Hosanagar, K. & Smith, M. D. (2011). Location, location, location: an analysis of profitability of position in online advertising markets. Journal of Marketing Research, 48(6), 1057–1073.

    Google Scholar 

  2. Animesh, A., Viswanathan, S., & Agarwal, R. (2011). Competing “creatively” in sponsored search markets: the effect of rank, differentiation strategy, and competition on performance. Information Systems Research, 22(1), 153–169.

    Article  Google Scholar 

  3. Arora, R. (1982). Validation of an s-o-r model for situation, enduring, and response components of involvement. Journal of Marketing Research, 19(4), 505–516.

    Article  Google Scholar 

  4. Athey, S. & Ellison, G. (2009): Position auctions with consumer search. http://ssrn.com/abstract=1454986.

  5. Barki, H., & Hartwick, J. (1989). Rethinking the concept of user involvement. MIS Quarterly, 13(1), 53–63.

    Article  Google Scholar 

  6. Barki, H., & Hartwick, J. (1994). Measuring user participation, user involvement, and user attitude. MIS Quarterly, 18(1), 59–82.

    Article  Google Scholar 

  7. Broder, A. (2002). A taxonomy of web search. SIGIR Forum, 36(2), 3–10.

    Article  Google Scholar 

  8. Cho, C. H., & Cheon, H. J. (2004). Why do people avoid advertising on the internet? Journal of Advertising, 33(4), 89–97.

    Article  Google Scholar 

  9. Danaher, P. J., & Mullarkey, G. (2003). Factors affecting online advertising recall: a study of students. Journal of Advertising Research, 43(3), 252–267.

    Article  Google Scholar 

  10. Edelman, B., & Ostrovsky, M. (2007). Strategic bidder behavior in sponsored search auctions. Decision Support Systems, 43(1), 192–198.

    Article  Google Scholar 

  11. Edwards, S. M., Li, H., & Lee, J. H. (2002). Forced exposure and psychological reactance: antecedents and consequences of the perceived intrusiveness of pop-up ads. Journal of Advertising, 31(3), 83–95.

    Article  Google Scholar 

  12. eReleases (2010). Statistics highlighting the importance of SEO press releases. Retrieved September 10, 2011, from http://www.ereleases.com/prfuel/statistics-seo-press-releases/.

  13. Feldman, J. S., Muthukrishnan, M. & Stein, C. (2007). Budget optimization in search-based advertising auctions. EC '07 Proceedings of the 8th ACM Conference on Electronic Commerce, pp. 40–49.

  14. Ghose, A., & Yang, S. (2009). An empirical analysis of search engine advertising: sponsored search in electronic markets. Management Science, 55(10), 1605–1622.

    Article  Google Scholar 

  15. Interactive Advertising Bureau (2011). Internet advertising revenues hit $7.3 billion in Q1′11: Highest first-quarter revenue level on record according to IAB and PWC. Retrieved September 10, 2011, from http://www.iab.net/about_the_iab/recent_press_releases/press_release_archive/press_release/pr-052611/.

  16. Jansen, B. J., & Spink, A. (2007). The effect on click-through of combining sponsored and non-sponsored search engine results in a single listing. In Proceedings of the 2007 Workshop on Sponsored Search Auctions, Banff, AB, Canada.

  17. Jansen, B. J., Spink, A., & Saracevic, T. (2000). Real life, real users, and real needs: a study and analysis of user queries on the web. Information Processing and Management, 2(36), 207–227.

    Article  Google Scholar 

  18. Jansen, B. J., Spink, A., Blakely, C., & Koshman, S. (2007). Defining a session on web search engines. Journal of the American Society for Information Science and Technology, 58(6), 862–871.

    Article  Google Scholar 

  19. Jerath, K., Ma, L., Park, Y. H., & Srinivasan, K. (2011). A ‘position paradox’ in sponsored search auctions. Management Science, 30(4), 612–627.

    Google Scholar 

  20. Kitts, B., & Leblanc, B. (2004). Optimal bidding on keyword auctions. Electronic Markets, 14(3), 186–201.

    Article  Google Scholar 

  21. Laurent, G., & Kapferer, J. N. (1985). Measuring consumer involvement profiles. Journal of Marketing Research, 22(1), 41–53.

    Article  Google Scholar 

  22. Li, J., Pan, R., & Wang, H. (2010). Selection of best keywords: a poisson regression model. Journal of Interactive Advertising, 11, 27–35.

    Article  Google Scholar 

  23. Manchanda, P., Dubé, J. P., Goh, K. Y., & Chintagunta, P. K. (2006). The effect of banner advertising on internet purchasing. Journal of Marketing Research, 43(1), 98–108.

    Article  Google Scholar 

  24. Moe, W. W. (2003). Buying, searching, or browsing: differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1–2), 29–39.

    Google Scholar 

  25. Nerlove, M., & Arrow, K. J. (1962). Optimal advertising policy under dynamic conditions. Economica, 29(114), 129–142.

    Article  Google Scholar 

  26. Nottorf, F., Mastel, A. & Funk, B. (2012). The user-journey in online search: an empirical study of the generic-to-branded spillover effect based on user-level data. In Proceedings of DCNET, ICE-B and OPTICS 2012: SciTePress, S. 145–154.

  27. Petris, G., Petrone, S., & Campagnoli, P. (2009). Dynamic linear models with R. New York: Springer.

    Google Scholar 

  28. Rusmevichientong, P., & Williamson, D. P. (2006). An adaptive algorithm for selecting profitable keywords for search-based advertising services. EC '06 Proceedings of the 7th ACM Conference on Electronic Commerce, pp. 260–269.

  29. Rutz, O. J., & Bucklin, R. E. (2007). A model of individual keyword performance in paid search advertising. http://ssrn.com/abstract=1024765.

  30. Rutz, O. J., & Bucklin, R. E. (2011). From generic to branded: a model of spillover in paid search advertising. Journal of Marketing Research, 48(1), 87–102.

    Article  Google Scholar 

  31. Rutz, O. J., Trusov, M., & Bucklin, R. E. (2011). Modeling indirect effects of paid search advertising: which keywords lead to more future visits? Marketing Science, 30(4), 646–665.

    Article  Google Scholar 

  32. Search Engine Watch (2004). Delving deep inside the searcher’s mind. Retrieved September 10, 2011, from http://searchenginewatch.com/article/2064150/Delving-Deep-Inside-the-Searchers-Mind/.

  33. Varian, H. R. (2007). Position auctions. International Journal of Industrial Organization, 25(6), 1163–1178.

    Article  Google Scholar 

  34. Varian, H. R. (2009). Online ad auctions. American Economic Review, 99(2), 430–434.

    Article  Google Scholar 

  35. West, M., & Harrison, J. (1997). Bayesian forecasting and dynamic models (2nd ed.). New York: Springer.

    Google Scholar 

  36. Yao, S., & Mela, C. F. (2009). Sponsored search auctions: research opportunities in marketing. Foundations and Trends in Marketing, 3(2), 75–126.

    Article  Google Scholar 

  37. Zaichkowsky, J. L. (1994). The personal involvement inventory: reduction, revision, and application to advertising. Journal of Advertising, 23(4), 59–70.

    Article  Google Scholar 

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Acknowledgments

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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Keywords

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