Hotel Websites, Web 2.0, Web 3.0 and Online Direct Marketing: The Case of Austria

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Hotel Websites, Web 2.0, Web 3.0 and Online Direct Marketing: The Case of Austria

  1. 1. Hotel Websites, Web 2.0, Web 3.0 and Online Direct Marketing The Case of Austria Ioannis Stavrakantonakis Research & Development Engineer University of Innsbruck Semantic Technology Institute (STI) Innsbruck
  2. 2. Outline  Motivation  Methodology  Analysis Results  Conclusion
  3. 3. From 179M results to © http://www.flickr.com/photos/kelehen/9513283770/
  4. 4. ‘97 2012 based on the Google Search timeline [1]
  5. 5. ‘97 ‘96 2012 based on the Google Search timeline [1]
  6. 6. ‘97 ‘96 2012 based on the Google Search timeline [1] Social Web Web 2.0
  7. 7. 2012 ‘97 based on the Google Search timeline [1] ‘96 Social Web Web 2.0 RDFa Microformats Microdata Semantic Web Web 3.0
  8. 8. 2012 ‘97 based on the Google Search timeline [1] ‘96 Social Web Web 2.0 RDFa Microformats Microdata Semantic Web Web 3.0
  9. 9. Where do the Hotel websites stand in this picture?
  10. 10. Outline  Motivation  Methodology  Analysis Results  Conclusion
  11. 11. Methodology Statistics tools Scripting NoSQL database Dataset specification & Crawling Research questions Inspired by the Pyramid of Data Science [2]
  12. 12. Main research questions 1 To what extent do hotels in Austria exploit the Web 2.0 and 3.0 solutions? 2 Is there any correlation between the hotels’ star rating with the usage of Web 2.0 and 3.0 technologies?
  13. 13. Dataset & crawling Dataset Crawling Integration >2000 Hotels Web Crawler Combination (URL, geocoordinates, stars, name, etc.) -Specific Criteria Aggregated Crawled data in Austria -Python (Scrapy) -Distilling information from the data in a database (NoSQL) + Seed data (initial data regarding the hotels)
  14. 14. Criteria Social Networks CMS Web 2.0 Images Sharing Networks Syndication feeds Videos Review Sites
  15. 15. Criteria schema .org Vocabularies Microformats Web 3.0 Open Graph Protocol Formats Microdata RDFa Machine-readable descriptions that add meaning to the content
  16. 16. Why these criteria? Web 3.0 Search engines understand the content of the pages. “These rich snippets help users recognize when your site is relevant to their search, and may result in more clicks to your pages.” [4]
  17. 17. Analysis Results © http://debbigunnsphotos.blogspot.co.at/2013/04/meth-lab-explosion.html
  18. 18. Distribution of Content Management Systems Drupal 1% Other 27% WordPress 7% 44% Microsoft FrontPage 6% Joomla! 14% Use a CMS system TYPO3 45%
  19. 19. Distribution of Content Management Systems 87 different CMS systems Drupal 1% Other 27% WordPress 7% 44% Microsoft FrontPage 6% Joomla! 14% Use a CMS system TYPO3 45%
  20. 20. Social Web (Web 2.0) Uptake the in the 53% of Webhotels(havingdataset exploit the opportunities of 2.0 at least 1 link) 80 70 67.94 % Hotels 60 48.57 50 40 25.46 30 20 10 15.12 13.47 9.04 0.43 24.24 20.33 0.17 1.3 0 Web 2.0 Channels 1.3
  21. 21. Semantic Web (Web 3.0) Uptake Not exploiting Web 3.0 95% Web 3.0 ready 5%
  22. 22. Web 2.0/3.0 – Stars correlation 70 50 % Hotels 60 57.9 60 42.38 40 Web 2.0 30 20 10 Web 3.0 16.67 6.67 3.78 5.76 2.5 0 1&2 3 4 Hotel category - star rating 5
  23. 23. Outline  Motivation  Methodology  Results  Conclusion
  24. 24. Conclusion • Uptake of Web2.0, Web 3.0 in the hotel sector of Austria has great space for improvement. Causes of low Web 3.0 integration: a) CMS diversity. b) Educational factors in development agencies. • In case the reported situation remains as-is in the future, the online direct marketing will keep underperforming.
  25. 25. Questions? ioannis.stavrakantonakis@sti2.at istavrak.com @istavrak
  26. 26. References 1. Google Search timeline: http://insidesearch.blogspot.co.at/2013/09/fifteenyears-onand-were-just-getting.html 2. The Pyramid of Data Science: http://datacommunitydc.org/blog/2013/08/thepyramid-of-data-science/ 3. Clark, L. (2011, Apr 12). The Semantic Web, Linked Data and Drupal, Part 1: Expose your data using RDF. Retrieved from IBM-developerWorks: http://www.ibm.com/developerworks/library/wa-rdf/ 4. Google, About rich snippets and structured data: https://support.google.com/webmasters/answer/99170?hl=en&ref_topic=10 88472

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