Modeling Indirect Effects of Paid Search Advertising: Which Keywords Lead to More Future Visits?

  • Authors:

  • Oliver J. Rutz;Michael Trusov;Randolph E. Bucklin

  • Affiliations:

  • School of Management, Yale University, New Haven, Connecticut 06511;Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742;Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095

  • Venue:
  • Marketing Science
  • Year:
  • 2011

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Abstract

Many online shoppers initially acquired through paid search advertising later return to the same website directly. These so-called “direct type-in” visits can be an important indirect effect of paid search. Because visitors come to sites via different keywords and can vary in their propensity to make return visits, traffic at the keyword level is likely to be heterogeneous with respect to how much direct type-in visitation is generated. Estimating this indirect effect, especially at the keyword level, is difficult. First, standard paid search data are aggregated across consumers. Second, there are typically far more keywords than available observations. Third, data across keywords may be highly correlated. To address these issues, the authors propose a hierarchical Bayesian elastic net model that allows the textual attributes of keywords to be incorporated. The authors apply the model to a keyword-level data set from a major commercial website in the automotive industry. The results show a significant indirect effect of paid search that clearly differs across keywords. The estimated indirect effect is large enough that it could recover a substantial part of the cost of the paid search advertising. Results from textual attribute analysis suggest that branded and broader search terms are associated with higher levels of subsequent direct type-in visitation.