Analyzing clickstreams using subsessions

Andersen, Jesper; Giversen, A.; Jensen, Allan H.; Larsen, Rune S.; Pedersen, Torben Bach; Skyt, Janne

doi:10.1145/355068.355312

Cited by 35 publications

(29 citation statements)

References 9 publications

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“…Using data warehousing techniques, Andersen et al 26 modeled clickstream data by introducing an additional fact table to the star model proposed by Ref. 27.…”

Section: Methods Of Analysismentioning

confidence: 99%

Subsessions: A granular approach to click path analysis

Menasalvas

Millán

Peña

et al. 2004

Int. J. Intell. Syst.

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The fiercely competitive web-based electronic commerce (e-commerce) environment has made necessary the application of intelligent methods to gather and analyze information collected from consumer web sessions. Knowledge about user behavior and session goals can be discovered from the information gathered about user activities, as tracked by web clicks. Most current approaches to customer behavior analysis study the user session by examining each web page access. However, the abstraction of subsessions provides a more granular view of user activity. Here, we propose a method of increasing the granularity of the user session analysis by isolating useful subsessions within sessions. Each subsession represents a high-level user activity such as performing a purchase or searching for a particular type of information. Given a set of previously identified subsessions, we can determine at which point the user begins a preidentified subsession by tracking user clicks. With this information we can (1) optimize the user experience by precaching pages or (2) provide an adaptive user experience by presenting pages according to our estimation of the user's ultimate goal. To identify subsessions, we present an algorithm to compute frequent click paths from which subsessions then can be isolated. The algorithm functions by scanning all user sessions and extracting all frequent subpaths by using a distance function to determining subpath similarity. Each frequent subpath represents a subsession. An analysis of the pages represented by the subsession provides additional information about semantically related activities commonly performed by users.

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“…Using data warehousing techniques, Andersen et al 26 modeled clickstream data by introducing an additional fact table to the star model proposed by Ref. 27.…”

Section: Methods Of Analysismentioning

confidence: 99%

Subsessions: A granular approach to click path analysis

Menasalvas

Millán

Peña

et al. 2004

Int. J. Intell. Syst.

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“…The data recorded includes the time and date of visit, the IP address, details as to the history of entry to the site, whether the visit resulted in a purchase, and if so the amount of revenue. Analysis of such data enhances understanding and prediction of website visitor behaviour Andersen et al [2000], usually with the aim of maximising customer sales and revenue. Session data forms a subset of the record and explains a customer's browsing behaviour during a single visit and is the most commonly analysed aspect of e-commerce data.…”

Section: Application: Analysis Of Clickstream Datamentioning

confidence: 99%

Robust and scale-free effect sizes for non-Normal two-sample comparisons, with applications in e-commerce

Wooff

Jamalzadeh²

2013

Journal of Applied Statistics

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. (2013) 'Robust and scale-free eect sizes for non-normal two-sample comparisons, with applications in e-commerce.', Journal of applied statistics., 40 (11). pp. 2495-2515. Further information on publisher's website: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThe effect size (ES) has been mainly introduced and investigated for changes in location under an assumption of Normality for the underlying population. However, there are many circumstances where populations are non-Normal, or depend on scale and shape and not just a location parameter. Our motivating application from e-commerce requires an ES which is appropriate for long-tailed distributions. We review some common ES measures. We then introduce two novel alternative ES for two-sample comparisons, one scale-free and one on the original scale of measurement, and analyse some theoretical properties. We examine these ES for two-sample comparison studies under an assumption of Normality and investigate what happens when both location and scale parameters differ. We explore ES for phenomena for non-Normal situations, using the Weibull family for illustration. Finally, for an application, we assess differences in customer behaviour when browsing E-commerce websites.

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“…Finally, an adaptive site (one that is capable of being customized to each individual) benefits from (or even "requires") knowledge of prior web site traversal behavior (Perkowitz & Etzioni, 1997). Often, just knowing how users leave the web site system is important information (Andersen et al, 2000).…”

Section: Comparing Scores For Online and Paper Evaluationsmentioning

confidence: 99%

“…Web-logs can be missing records due to client-side caching, server-side caching, intermediate network caching or errors. Two of the most difficult problems are: distinguishing what is a unique, single transaction "session" (Andersen et al, 2000), and uniquely identifying users through one or more sessions (Spiliopoulou, 2000). These are not intended to invade privacy in any way, but rather to link clicks together in an accurate and anonymous manner.…”

Section: Comparing Scores For Online and Paper Evaluationsmentioning

confidence: 99%

See 1 more Smart Citation

Web Log Analysis: A Study of Instructor Evaluations Done Online

Klassen¹,

Smith²

2004

JITE:Research

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Executive SummaryThis paper focuses on developing a relatively simple method for analyzing web-logs. It also explores the challenges and benefits of web-log analysis. The study of student behavior on this site provides insights into website design and the effectiveness of this site in particular. Another benefit realized from the paper is the ease with which these concepts can be discussed with students. The purpose and context of the website used are easily understood by instructors and students, providing the basis for some rich discussions regarding: web-logs in general, web-log analysis and its' challenges, the use of data for decision-making purposes, and other aspects.Web-logs record every page that users access during their visit to the site, thus providing a picture of their behavior based on a number of things, including the order in which they hit pages (i.e., the "traversal path"), the length of time spent on each page, whether they go to pages in error, and whether they return more than once to a page. This is a vast data resource which many companies have difficulty using in an efficient and effective way. Using the on-line instructor evaluation site provided a finite database to study; in this case raw data of approximately 35,000 records represented 3,368 student-class evaluations done over a two-week period (a small web-log). Opensource software was used to calculate a number of statistics, and it was found that students do access the evaluation site from home, taking advantage of the 24-hour availability. However, many students fill out evaluations on weekdays between 9 am and 3 pm, suggesting that a fair proportion do the evaluations in between classes when they are on campus.The main portion of this work involved identifying and analyzing the traversal paths of the students through the site. There were a huge number of unique paths, highlighting the difficulty in isolating common user behaviors. It was found that students are able to complete the entire process (signing in, filling out one or more evaluations, etc.) in just over 8 minutes (on average). Students spend on average 82 seconds answering 12 multiple choice questions and 110 seconds filling out qualitative comments, suggesting that they spend a sufficient amount of time to adequately evaluate the course. In fact, students provide more qualitative comments on the online evaluations than they do on the in-class paper evaluations. Further path analysis helped to show why there are so many unique paths; for instance, if a user hits "reload", the page will register again in the web-log, creating a different path than a user who only loads the page once even though these twoMaterial published as part of this journal, either on-line or in print, is copyrighted by the publisher of the Journal of Information Technology Education. Permission to make digital or paper copy of part or all of these works for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage AND ...

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Analyzing clickstreams using subsessions

Cited by 35 publications

References 9 publications

Subsessions: A granular approach to click path analysis

Subsessions: A granular approach to click path analysis

Robust and scale-free effect sizes for non-Normal two-sample comparisons, with applications in e-commerce

Web Log Analysis: A Study of Instructor Evaluations Done Online

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