Abstract
Understanding consumer online search behavior is crucial to optimize companies’ paid search advertising campaigns. Standard measures such as the click-through rate do not account for this search behavior over time, which may favor a certain group of search type and, therefore, may mislead managers in allocating their financial spending efficiently. We analyzed a large query log for the occurrence of user-specific interaction patterns within and across three different industries and were able to show that consumers’ online search behavior is indeed a multi-stage process that heavily depends on industry-specific characteristics. For example, whereas a product search within the clothing industry typically begins with general keywords (“sneakers”) and that search process becomes narrowed as it proceeds by including more specific, e.g. brand-related (“sneakers adidas”), keywords, this behavior is a relatively rare event in other industries (e.g., the healthcare industry). Our method to analyze consumer search processes helps companies to identify the role of specific activities within a respective industry and to allocate their financial spending in paid search advertising accordingly.
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Notes
- 1.
Since the users who represent the queries are mostly located in the United States of America, our work is mainly based on the US region.
- 2.
We would like to thank an anonymous reviewer for pointing out this more clearly.
- 3.
- 4.
The Harrah’s Entertainment Inc., for example, had about 21 hotels and hotel chains in 2006.
References
Ghose, A., Yang, S.: Comparing performance metrics in organic search with sponsored search advertising. In: Proceedings of the 2nd International Workshop on Data Mining and Audience Intelligence for Advertising, ADKDD ’08, pp. 18–26. ACM, New York (2008)
Jansen, B.J., Mullen, T.: Sponsored search: an overview of the concept, history, and technology. Int. J. Electron. Bus. 6, 114–131 (2008)
Search Engine Watch: Delving deep inside the searcher’s mind (2006). http://searchenginewatch.com/3406911
Rutz, O.J., Bucklin, R.E.: From generic to branded: a model of spillover in paid search advertising. J. Mark. Res. 48, 87–102 (2011)
Nottorf, F., Funk, B.: A cross-industry analysis of the spillover effect in paid search advertising. Electron. Mark. 23, 205–216 (2013)
Pass, G., Chowdhury, A., Torgeson, C.: A picture of search. In: Proceedings of the 1st International Conference on Scalable Information Systems, InfoScale ’06. ACM, New York (2006)
Bucklin, R.E., Sismeiro, C.: Click here for internet insight: advances in clickstream data analysis in marketing. J. Interact. Mark. 23, 35–48 (2009)
Broder, A.: A taxonomy of web search. SIGIR Forum 36, 3–10 (2002)
Jansen, B.J., Spink, A.: The effect on click-through of combining sponsored and non-sponsored search engine results in a single listing. In: Proceedings of 2007 Workshop on Sponsored Search Auctions, Banff, AB, Canada (2007)
Moe, W.W.: Buying, searching, or browsing: differentiating between online shoppers using in-store navigational clickstream. J. Consum. Psychol. 13, 29–39 (2003)
Johnson, E.J., Moe, W.W., Fader, P.S., Bellman, S., Lohse, G.L.: On the depth and dynamics of online search behavior. Manag. Sci. 50, 299–308 (2004)
Agarwal, A., Hosanagar, K., Smith, M.D.: Location, location, location: an analysis of profitibality of position in online advertising markets. J. Mark. Res. 48, 1057–1073 (2011)
Animesh, A., Viswanathan, S., Agarwal, R.: Competing “creatively” in sponsored search markets: the effect of rank, differentiation strategy, and competition on performance. Inf. Syst. Res. 22, 153–169 (2011)
Ghose, A., Yang, S.: An empirical analysis of search engine advertising: sponsored search in electronic markets. Manag. Sci. 55, 1605–1622 (2009)
Rutz, O.J., Trusov, M., Bucklin, R.E.: Modeling indirect effects of paid search advertising: which keywords lead to more future visits? Mark. Sci. 30, 646–665 (2011)
Chan, T.Y., Xie, Y., Wu, C.: Measuring the lifetime value of customers acquired from google search advertising. Mark. Sci. 30, 837–850 (2011)
Abhishek, V., Hosanagar, K., Fader, P.S.: On aggregation bias in sponsored search data: existence and implications. In: Proceedings of the 13th ACM Conference on Electronic Commerce (2011)
Interbrand & Business Week: Interbrand’s best global brands 2006 (2006)
Ghose, A., Yang, S.: Modeling cross-category purchases in sponsored search advertising (2010)
Yang, S., Ghose, A.: Analyzing the relationship between organic and sponsored search advertising: positive, negative, or zero interdependence? Mark. Sci. 29, 602–623 (2010)
Janiszewski, C.: The influence of display characteristics on visual exploratory search behavior. J. Consum. Res. 25, 290–301 (1998)
Howard, J.A., Sheth, J.N.: A theory of buyer behavior. Rivista internazionale di scienze economiche e commerciali 15, 589–618 (1968)
Narayana, C.L., Markin, R.J.: Consumer behavior and product performance: an alternative conceptualization. J. Mark. 39, 1–6 (1975)
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Nottorf, F., Mastel, A., Funk, B. (2014). Consumer Online Search Behavior: A Cross-Industry Analysis Based on User-Level Data. In: Obaidat, M., Filipe, J. (eds) E-Business and Telecommunications. ICETE 2012. Communications in Computer and Information Science, vol 455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44791-8_5
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