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. Outline
 Motivation
 Methodology
 Analysis Results
 Conclusion

4. From 179M results to
© http://www.flickr.com/photos/kelehen/9513283770/

5. ‘97
2012
based on the
Google Search
timeline [1]

6. ‘97
‘96
2012
based on the
Google Search
timeline [1]

7. ‘97
‘96
2012
based on the
Google Search
timeline [1]
Social Web
Web 2.0

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. 2012
‘97
based on the
Google Search
timeline [1]
‘96
Social Web
Web 2.0
RDFa
Microformats
Microdata
Semantic Web
Web 3.0

10. Where do the Hotel websites stand in this picture?

11. Outline
 Motivation
 Methodology
 Analysis Results
 Conclusion

12. Methodology
Statistics tools
Scripting
NoSQL database
Dataset
specification & Crawling
Research questions
Inspired by the Pyramid of Data Science [2]

13. 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?

14. 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)

15. Criteria
Social
Networks
CMS
Web
2.0
Images
Sharing
Networks
Syndication
feeds
Videos
Review
Sites

16. Criteria
schema
.org
Vocabularies
Microformats
Web
3.0
Open Graph
Protocol
Formats
Microdata
RDFa
Machine-readable
descriptions that add
meaning to the content

17. 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]

18. Analysis Results
© http://debbigunnsphotos.blogspot.co.at/2013/04/meth-lab-explosion.html

19. Distribution of Content Management
Systems
Drupal
1%
Other
27%
WordPress
7%
44%
Microsoft
FrontPage
6%
Joomla!
14%
Use a CMS
system
TYPO3
45%

20. 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%

21. 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

22. Semantic Web (Web 3.0) Uptake
Not exploiting
Web 3.0
95%
Web 3.0 ready
5%

23. 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

24. Outline
 Motivation
 Methodology
 Results
 Conclusion

25. 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.

26. Questions?
ioannis.stavrakantonakis@sti2.at
istavrak.com
@istavrak

27. 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
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