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2 | Page Country Clustering Based on Search-Query Pattern Correlation

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This paper introduces a technique to cluster regions or countries for marketing purposes. It uses information gathered through aggregated website search-query patterns, available from web logs or …

This paper introduces a technique to cluster regions or countries for marketing purposes. It uses information gathered through aggregated website search-query patterns, available from web logs or analytics software. Clustering done using this technique helps grouping countries that similarly respond to a particular marketing campaigns potentially reducing marketing costs. The method collects aggregated geographical and search-pattern information from keywords and the visit volume of each keyword. It uses abstract keyword logs in web analytics software to identify and understand target market segments more effectively. It is argued that actual search logs could be better indication of market behavior than language or cultural boundaries. By analyzing web logs with this technique, marketers will be able to plan marketing campaigns strategically based on the empirical data on website visitors. This data is derived from huge long-tailed keyword search sources, and ultimately helps understand and adapt better to customer’s interests. The paper starts with an introduction on the underlying problem, the analysis method, and its four basic steps: preparing the data, creating a correlation matrix, identifying criteria and region clustering using the Louvain method of community structure over random networks. The results for a specific dataset are then presented and conclude with a list of future work steps. Results in this paper show that country-based keyword analysis can yield search-query patterns that unveil connections between culturally dissimilar regions.

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  • 1. Country Clustering based onSearch-Query Pattern CorrelationEdgar Anzaldúa MorenoRiaz Esmailzadeh
  • 2. About MyselfMSc Information TechnologyCarnegie Mellon UniversityWeb Persuasion ArchitectInternational Marketing Department
  • 3. About MyselfMSc Information TechnologyCarnegie Mellon UniversityWeb Persuasion ArchitectInternational Marketing DepartmentEnrolments
  • 4. About MyselfMSc Information TechnologyCarnegie Mellon UniversityWeb Persuasion ArchitectInternational Marketing DepartmentEnrolments Visits
  • 5. About MyselfMSc Information TechnologyCarnegie Mellon UniversityWeb Persuasion ArchitectInternational Marketing DepartmentEnrolments Visits Facebook  Fans
  • 6. Tracking People• Watch People Closely• Geographically Distant Countries• Keywords & Search Engines
  • 7. The World Today
  • 8. The World Today
  • 9. The World Today?
  • 10. Long Tails
  • 11. Long Tails
  • 12. Long Tails
  • 13. Long TailsCountry  ACountry  BCountry  CCountry  D0% 25% 75% 100%
  • 14. Long TailsCountry  ACountry  BCountry  CCountry  D0% 25% 75% 100%Country  ECountry  FCountry  GCountry  H
  • 15. Pearson
  • 16. Pearson
  • 17. Correlation
  • 18. Correlation
  • 19. Correlation
  • 20. Correlation
  • 21. Correlation
  • 22. Similarity
  • 23. Similarity
  • 24. Similarity
  • 25. Similarity
  • 26. Country Graph
  • 27. Country Graph
  • 28. Country Graph
  • 29. Country Graph
  • 30. Louvain
  • 31. Results
  • 32. Results vs. GDP
  • 33. Future Work• Search-Query Pattern Analysis• Extract User Interests based on Search-Queriesand Countries• Predict User Behavior• Semantic Translation• Typo Support
  • 34. Conclusions• Cultural Stereotypes don’t match User Interests• Data is not Information• There is still a lot of work to do
  • 35. Questions?http://eam.mx/getthepaperGet My Card