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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
Outline
Motivation
Methodology
Analysis Results
Conclusion
From 179M results to
© http://www.flickr.com/photos/kelehen/9513283770/
based on the Google Search
timeline [1]
‘97 2012
based on the Google Search
timeline [1]
‘97 2012
‘96
Social WebWeb 2.0
based on the Google Search
timeline [1]
‘97 2012
‘96
Social WebWeb 2.0
Semantic WebWeb 3.0RDFa
based on the Google Search
timeline [1]
MicroformatsMicrodata
‘97 2012
‘96
Social WebWeb 2.0
Semantic WebWeb 3.0RDFa
based on the Google Search
timeline [1]
MicroformatsMicrodata
‘97 2012
‘96
Where do the Hotel websites stand in this picture?
Outline
Motivation
Methodology
Analysis Results
Conclusion
Methodology
Dataset
specification & Crawling
NoSQL database
Scripting
Statistics tools
Inspired by the Pyramid of Data Science [2]
Research questions
Main research questions
To what extent do hotels in Austria exploit the Web 2.0 and 3.0 solutions?
Is there any correlation between the hotels’ star rating with the usage of Web 2.0 and 3.0 technologies?
1
2
Dataset & crawling
Dataset Crawling Integration
>2000 Hotels
(URL, geo-coordinates,
stars, name, etc.)
in Austria
Web Crawler
-Specific Criteria
-Python (Scrapy)
-Distilling information from
the data in a database (NoSQL)
Combination
Aggregated Crawled data
+
Seed data (initial data regarding
the hotels)
Criteria
Web 2.0
SocialNetworks
SharingNetworks
Images
Videos
ReviewSites
Syndicationfeeds
CMS
Criteria
Web 3.0
Vocabularies
RDFa
Formats
Machine-readable descriptions that add
meaning to the content
Microformats
Microdata
schema.org
Open GraphProtocol
Why these criteria?
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]
Web 3.0
Analysis Results
© http://debbigunnsphotos.blogspot.co.at/2013/04/meth-lab-explosion.html
Drupal
1% WordPress
7%
TYPO3
45%
Joomla!
14%
Microsoft
FrontPage
6%
Other
27%
44%
Use a CMS
system
Distribution of Content Management Systems
Drupal
1% WordPress
7%
TYPO3
45%
Joomla!
14%
Microsoft
FrontPage
6%
Other
27%
44%
Use a CMS
system
Distribution of Content Management Systems
different
CMS systems87
67.94
9.04
0.43
13.47
48.57
0.17 1.3
25.4620.33
15.12
1.3
24.24
0
10
20
30
40
50
60
70
80
% H
ote
ls
Web 2.0 Channels
Social Web (Web 2.0) Uptake
of the hotels in the dataset exploit the opportunities
of Web 2.0 (having at least 1 link)53%
Semantic Web (Web 3.0) Uptake
Not exploiting
Web 3.0
95%
Web 3.0 ready
5%
Web 2.0/3.0 – Stars correlation
16.67
42.38
57.9 60
6.673.78 5.76
2.5
0
10
20
30
40
50
60
70
1 & 2 3 4 5
% H
ote
ls
Hotel category - star rating
Web 2.0
Web 3.0
Outline
Motivation
Methodology
Results
Conclusion
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.
References1. Google Search timeline: http://insidesearch.blogspot.co.at/2013/09/fifteen-
years-onand-were-just-getting.html
2. The Pyramid of Data Science: http://datacommunitydc.org/blog/2013/08/the-pyramid-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=1088472
