Avoiding the blind spots: Competitor identification using web text and linkage structure

Gautam Pant, Olivia R.L. Sheng

Research output: Contribution to conferencePaperpeer-review


The importance of identifying competitors and of avoiding "competitive blind spots" in marketplace has been well emphasized in research and practice. However, identification of competitors is non-trivial and requires active monitoring of a focal company's competitive environment. The difficulty in such identification is amplified manifold when there are many more than one focal company of interest. As the web presence of companies, their clients/consumers, and their suppliers continues to grow, it is increasingly realistic to assume that the real-world competitive relationships are reflected in the text and linkage structure of the relevant pages on the web. However, finding the appropriate web-based cues that effectively signal competitor relationships remains a challenge. Using web data collected for more than 2500 companies of the Russell 3000 index, we explore the notion that web cues can allow us to discriminate, in a statistically significant manner, between competitors and non-competitors. Based on this analysis, we present an automated technique that uses the most significant web-based cues and applies predictive modeling to identify competitors. We find that several web-based metrics on an average have significantly different values for companies that are competitors as opposed to non competitors. We also find that the predictive models built using web-based metrics that we suggest provide high precision, recall, F measure, and accuracy in identifying competitors.

Original languageEnglish (US)
StatePublished - 2009
Externally publishedYes
Event30th International Conference on Information Systems, ICIS 2009 - Phoenix, AZ, United States
Duration: Dec 15 2009Dec 18 2009


Other30th International Conference on Information Systems, ICIS 2009
Country/TerritoryUnited States
CityPhoenix, AZ


  • Competitor identification
  • Data mining
  • Web metrics

ASJC Scopus subject areas

  • Information Systems


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