Classifying web search queries to identify high revenue generating customers

Adan Ortiz-Cordova, Bernard J. Jansen

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1426-1441
Number of pages16
JournalJournal of the American Society for Information Science and Technology
Issue number7
StatePublished - Jul 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence


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