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Classifying web search queries to identify high revenue generating customers

Author

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  • Adan Ortiz‐Cordova
  • Bernard J. Jansen

Abstract

Traffic from search engines is important for most online businesses, with the majority of visitors to many websites being referred by search engines. Therefore, an understanding of this search engine traffic is critical to the success of these websites. Understanding search engine traffic means understanding the underlying intent of the query terms and the corresponding user behaviors of searchers submitting keywords. In this research, using 712,643 query keywords from a popular Spanish music website relying on contextual advertising as its business model, we use a k‐means clustering algorithm to categorize the referral keywords with similar characteristics of onsite customer behavior, including attributes such as clickthrough rate and revenue. We identified 6 clusters of consumer keywords. Clusters range from a large number of users who are low impact to a small number of high impact users. We demonstrate how online businesses can leverage this segmentation clustering approach to provide a more tailored consumer experience. Implications are that businesses can effectively segment customers to develop better business models to increase advertising conversion rates.

Suggested Citation

  • Adan Ortiz‐Cordova & Bernard J. Jansen, 2012. "Classifying web search queries to identify high revenue generating customers," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(7), pages 1426-1441, July.
    • Handle: RePEc:bla:jamist:v:63:y:2012:i:7:p:1426-1441
    DOI: 10.1002/asi.22640
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    Cited by:

    1. Klapdor, Sebastian & Anderl, Eva M. & von Wangenheim, Florian & Schumann, Jan H., 2014. "Finding the Right Words: The Influence of Keyword Characteristics on Performance of Paid Search Campaigns," Journal of Interactive Marketing, Elsevier, vol. 28(4), pages 285-301.
    2. Damianos P. Sakas & Nikolaos Th. Giannakopoulos, 2021. "Harvesting Crowdsourcing Platforms’ Traffic in Favour of Air Forwarders’ Brand Name and Sustainability," Sustainability, MDPI, vol. 13(15), pages 1-25, July.
    3. Stoehr, Niklas & Braesemann, Fabian & Zhou, Shi, 2019. "Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends," SocArXiv bu5zs, Center for Open Science.

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