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Google analytics for measuring website performance
Abstract Performance measurement of tourism websitePerformance measurement of tourism websites is becoming a critical issue for effective online marketing. The aim of this article is to analyse the effectiveness of entries (visit behaviour and length of sessions) depending on their traffic source: direct visit, in-link entries (for instance, en.wikipedia.org), and search engine visits (for example, Google). For this purpose, time series analysis of Google Analytics data is made use of. This method could be interesting for any tourism website optimizer.resting for any tourism website optimizer.
Added by wikilit team Added on initial load  +
Collected data time dimension Longitudinal  +
Comments "[Wikipedia] entries are effective, showing an adequate return visit behaviour and length of sessions." p. 481 Websites (google analytics)
Conclusion According to the reading of the results inAccording to the reading of the results in Table 2, 0.43 out of every direct entry visit returns, 0.36 out of every search engine entry visits the site again, and only 0.24 out of every referee site visit returns. In other words, for our particular website, direct visits are the most effective ones, followed by search engine visits and only thirdly link-entries (Plaza, 2009). According to Table 3, the effectiveness of the in-links fromwww. ehu.esandwww.uv.esis null, whereas 0.21 out of every http://en. wikipedia.org driven entry visits the site again, and 0.29 out of every ‘Other In-links’ visit returns. In other words, for our particular website, http://en.wikipedia.org driven entries are effective, showing an adequate return visit behaviour and length of sessions; although ‘Other In-links’ are shown to be even more effective with 0.33 return visits per entry. Last, but not least, visits through Google are also shown to be effective, with 0.39 returnvisits perGoogle entry. The effectiveness of other search engines shows null for this particularwebsite (Table 3). In summary, for our particular website direct visits (Fig. 7) are the most effective ones, followed by Google entries (Fig. 7) and only thirdly http://en.wikipedia.org visits. Moreover, the performed time series analysis with Google Analytics shows 1) that return visits navigate deeper into the website and stay longer, and 2) that the less the bounce rate (error entries), the greater the visit duration (pages per visit and/or time at website). The importance of this article is not the particular website, but the methodology tested to arrive at these results.odology tested to arrive at these results.
Data source Websites  +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Google%2Banalytics%2Bfor%2Bmeasuring%2Bwebsite%2Bperformance%22  +
Has author Beatriz Plaza +
Has domain Economics +
Has topic Commercial aspects +
Peer reviewed Yes  +
Publication type Journal article  +
Published in Tourism Management +
Research design Statistical analysis  +
Research questions The aim of this article is to analyse the The aim of this article is to analyse the effectiveness of entries (visit behaviour and length of sessions) depending on their traffic source: direct visit, in-link entries (for instance, en.wikipedia.org), and search engine visits (for example, Google). For this purpose, time series analysis of Google Analytics data is made use of.s of Google Analytics data is made use of.
Revid 10,791  +
Theories Undetermined
Theory type Analysis  +
Title Google analytics for measuring website performance
Unit of analysis N/A  +
Url http://www.sciencedirect.com/science/article/pii/S0261517710000622  +
Wikipedia coverage Case  +
Wikipedia data extraction N/A  +
Wikipedia language Not specified  +
Wikipedia page type N/A  +
Year 2011  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:28:37  +
Categories Commercial aspects  + , Economics  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:28:03  +
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