Journal Articles

Website usage metrics: A re-assessment of session data

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Metrics derived from user visits or sessions provide a means of evaluating Websites and an important insight into
online information seeking behaviour, the most important of them being the duration of sessions and the number of
pages viewed in a session, a possible busyness indicator. However, the identification of session
(termed often ‘sessionization’) is fraught with difficulty in that there is no way of determining from a transactional log file that a user has ended their session. No one logs out. Instead a session delimiter has to be applied and this is typically done on the basis of a standard period of inactivity. To date researchers have discussed the issue of a time out delimiter in terms of a single value and if a page view time exceeds the cut-off value the session is deemed to have ended. This approach assumes that page view time is a single distribution and that the cut-off value is one point on that distribution. The authors however argue that page time distribution is composed of a number of quite separate view time distributions because of the marked differences in view times between pages (abstract, contents page, full text). This implies that a number of timeout delimiters should be applied. Employing data from a study of the OhioLINK digital journal library, the authors demonstrate how the setting of a time out delimiter impacts on the estimate of page view time and the number of estimated session. Furthermore, they also show how a number of timeout delimiters might apply and they argue that this gives a better and more robust estimate of the number of sessions, session time and page view time compared to an application of a single timeout delimiter.

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Detail Information

Series Title

Information Processing & Management

Call Number

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Publisher

s.l. : Elsevier., 2008

Collation

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Language

English

ISBN/ISSN

0306-4573

Classification

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Content Type

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Media Type

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Carrier Type

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Edition

Vol. 44, No. 1, Page 358-372

Subject(s)

Web log analysis
Session identification
Sessionization
User behaviour

Specific Detail Info

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Statement of Responsibility

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