Google analytics for measuring website performance

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Google analytics for measuring website performance
Authors: Beatriz Plaza [edit item]
Citation: Tourism Management  : . 2011.
Publication type: Journal article
Peer-reviewed: Yes
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Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Google analytics for measuring website performance is a publication by Beatriz Plaza.


[edit] Abstract

Performance 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.

[edit] Research questions

"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."

Research details

Topics: Commercial aspects [edit item]
Domains: Economics [edit item]
Theory type: Analysis [edit item]
Wikipedia coverage: Case [edit item]
Theories: "Undetermined" [edit item]
Research design: Statistical analysis [edit item]
Data source: Websites [edit item]
Collected data time dimension: Longitudinal [edit item]
Unit of analysis: N/A [edit item]
Wikipedia data extraction: N/A [edit item]
Wikipedia page type: N/A [edit item]
Wikipedia language: Not specified [edit item]

[edit] Conclusion

"According 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."

[edit] Comments

""[Wikipedia] entries are effective, showing an adequate return visit behaviour and length of sessions." p. 481 Websites (google analytics)"


Further notes[edit]

Facts about "Google analytics for measuring website performance"RDF feed
AbstractPerformance 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 teamAdded on initial load +
Collected data time dimensionLongitudinal +
Comments"[Wikipedia] entries are effective, showing an adequate return visit behaviour and length of sessions." p. 481 Websites (google analytics)
ConclusionAccording 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 sourceWebsites +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Google%2Banalytics%2Bfor%2Bmeasuring%2Bwebsite%2Bperformance%22 +
Has authorBeatriz Plaza +
Has domainEconomics +
Has topicCommercial aspects +
Peer reviewedYes +
Publication typeJournal article +
Published inTourism Management +
Research designStatistical analysis +
Research questionsThe 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.
Revid10,791 +
TheoriesUndetermined
Theory typeAnalysis +
TitleGoogle analytics for measuring website performance
Unit of analysisN/A +
Urlhttp://www.sciencedirect.com/science/article/pii/S0261517710000622 +
Wikipedia coverageCase +
Wikipedia data extractionN/A +
Wikipedia languageNot specified +
Wikipedia page typeN/A +
Year2011 +