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Navigation Behavior Analysis and User Profiling Based on Automatically Collected Website Data

Aalto University School of Science. Navigation Behavior Analysis and User Profiling Based on Automatically Collected Website Data. Author Mathias Nyman. Supervisor Docent Kalevi Kilkki Instructor M. Sc. Emma Nordbäck. 12th February 2013. Contents. Background Research Questions Theory

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Navigation Behavior Analysis and User Profiling Based on Automatically Collected Website Data

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  1. Aalto University School of Science Navigation Behavior Analysis and User Profiling Based on Automatically Collected Website Data Author Mathias Nyman Supervisor Docent Kalevi Kilkki Instructor M. Sc. Emma Nordbäck 12th February 2013

  2. Contents • Background • Research Questions • Theory • Navigation Styles • User Needs • Seeking-behaviors • Methods • Results • Implications • Evaluation of the Study • Future Research

  3. Background

  4. Background User-centered design (UCD) incorporate user tests with real users involved Time consuming way of gathering and analyzing data and claim loads of resources Traditional UCD practices can thus be rather expensive

  5. Background User log data has, on the other hand, enabled data analysis without any interference with real users As web analytics tools has been developed rapidly and a new way of analyzing website usage has emerged User log data collected by software has enabled data mining Data Mining = Data processing using sophisticated data search capabilities and statistical algorithms to discover patterns and correlations in large preexisting databases; a way to discover new meaning in data (WordNet, 2012).

  6. Background Data mining has great potential to be used in supporting website design Data mining is rather inexpensive in comparison to traditional user-tests What the possibilities of user log data are, and how far it can be interpreted, however, remains to be seen.

  7. Background This study will make a contribution to this question, by exploring how automatically collected user data can be used for studying navigation behavior and user profiling.

  8. Research Questions

  9. Research Questions 1 To what part of the website do the majority of homepage visitors navigate? 2 Based on web analytics data, what type of navigation style(s) does the homepage visitors have? 3 Based on web analytics data, what type of information need(s) does the homepage visitors have? 4 Based on web analytics data, what type of seeking-behavior(s) does the homepage visitors have?

  10. Theory Navigation styles Information needs Seeking-behaviors

  11. Theory - Navigation Styles Flimsy navigation Time spent in processing content instead of understanding the hyperstructure that showed where the relevant information was Content focus Users’ goal was to find those pages that ought to be read and then read them Laborious navigation Trial and error strategy - followed links just to see if they are useful or not. Revisits numerous, but a new link was always followed on return Juvina and Oostendorp 2006 Divergent navigation Users not eager to revisit pages, rather explore new directions

  12. Theory – Information Needs The perfect catch • Users search for a specific information • E.g. the population of Rome or the Latin name of an animal • Lobster trapping • The users doesn’t really know what they are looking for • Not committed to search for more than just a few items • Not looking for a perfect catch (because the user wouldn’t recognize the perfect catch even if it’s caught) • If users stumble upon any useful information, it’s good enough • ”I’ve seen you before, Moby Dick” • Users find information they would not like to lose track of • Users save the information e.g. using bookmarks or downloading files Morville and Rosenfeld 2006 Indiscriminate driftnetting • Users want to find out everything about a certain topic

  13. Theory – Seeking-Behaviors Type • Integration • Combining searching, browsing and asking in same finding session • Iteration • Iteratively find your way to the goal • Information needs might change on the way, as the user is exposed to surrounding information and realizes something Morville and Rosenfeld 2006

  14. Theory – Seeking-Behaviors Category Morville and Rosenfeld 2006 • Two-step • Step 1: User is confronted with multiple website subsections and needs to figure out which section contains desired information • Step 2: users choose a suitable candidate from the subsections, and begins looking for more specific information • Berry picking • Start with a search query • From the found information pick small information bits (berries) • Formulate new search query from the newly learnt information • Iterate • Pearl growing • Users start with one or few good documents that are exactly what they need, and they strive to get “more like this one”

  15. Methods

  16. Methods - Metrics Entry pages report Pages report Adobe SiteCatalyst Page summary report Full path report

  17. Methods – Metrics into Measures • Step 1 – Define • Define webpages site depth • Step 2 – Get metrics • Site journey metrics • Top 20 most used • Path length 2 – 10 Step 3 – Draw pattern charts • Step 4 – Analyze • Map patterns to • User needs • Search behavior • Navigation styles • Step 5 – Analyze (optional) • If patterns could not be mapped to navigation behavior, go into site journey details

  18. Results Short Statistical Introduction

  19. Short Statistical Introduction Data collected during 1-30.9.2012 Total accessed pages 3 147 Global IT Company Website Unique visitors 382 147 Total amount of visitors 526 083

  20. Most Popular Entry Points Home 135 856 (25.8 %) Product 1 overview 37 951 (7.2 %) Product Family 1 27 849 (5.3 %) All products – 26 466 (5.0%) Product Family 1 Promo – 25 697 (4.9%) Troubleshooting – 12 026 (2.3%) Support – 11 400 (2.2%) Product 3 – 10 914 (2.1%) Product 5 – 9 603 (1.8%) Product 2 – 9 524 (1.8%) . . .

  21. Homepage Statistics Total amount of visitors 135 856 % - of all visitors 25.8 % Site journey length Average 4.67 Site journey length Median 3

  22. Homepage Statistics

  23. Results Navigation Styles

  24. Results – Navigation Styles Content focus 77.03 % Laborious 15.56 % Divergent 7.14 % Flimsy 0.78 %

  25. Results – Navigation Styles Content focus 77.03 % Laborious 15.56 %

  26. Results – Navigation Styles Divergent 7.14 % Flimsy 0.78 %

  27. Results Information Needs

  28. Results – Information Needs The perfect catch 64.22 % Lobster trapping 32.38 % Indiscriminate driftnetting 1.70 % I’ve seen you before, Moby Dick N/A

  29. Results – Information Needs The perfect catch 64.22 % Lobster trapping 32.38 % Indiscriminate driftnetting 1.70 %

  30. Results Seeking-Behaviors

  31. Results – Seeking-Behaviors Type Iteration 92.59 % Integration 3.21 %

  32. Results – Seeking-Behaviors Type Iteration 92.59 % Integration 3.21 %

  33. Results – Seeking-Behaviors Category Two-step 58.09 % Pearl growing 32.84 % Berry picking 3.21 %

  34. Results – Seeking-Behaviors Two-step 58.09 % Pearl growing 32.84 % Berry picking 3.21 %

  35. Implications Personas (portfolio) Breadcrumb (or something equal) important mean for navigation Some homepage content based on SiteCatalyst data Link suggestion logic based on SiteCatalystdata Aid long paths to become shorter -> more efficent

  36. Evaluation of the Study

  37. Evaluation of the Study Overall, a successful way of capturing the basics of the users’ information needs, seeking-behaviors and navigation styles More time needed for planning, however, planning needed only once Less resources needed for execution Study proved the potential of data mining

  38. Evaluation of the Study Lacks in collected data No pattern specific page visit times Little data can be viewed in fine graines format No back button tracking etc... The result does not involve the underlying reasons to user behavior.  Still need of UCD, e.g. qualitative interviews

  39. Future Research Cultural differences on website usage Investigate in what extent surface links, such as banners, are utilized Is there connections between navigation styles, information needs and seeking-behaviors Which type of navigation style, information need, and seeking-behavior is most effective in which type of information architecture

  40. Thank You! Questions?

  41. Appendix

  42. Site Journeys Length Statistic 1 page 32 308 (6.14 %) Combinations: 1 2 pages 20 549 (3.91 %) Combinations: 215 3 pages 19 337 (3.86 %) Combinations: 1 356 4 pages 15 271 (2.90 %) Combinations: 4 012 5 pages 10 804 (2.05 %) Combinations: 6 348 6 pages 8 002 (1.52 %) Combinations: 6 655 7 pages 6 253 (1.19 %) Combinations: 5 909 8 pages 4 669 (0.89 %) Combinations: 4 588 9 pages 3 630 (0.69 %) Combinations: 3 616

  43. Site Journey Length 1 – 3 1 page 32 308 (6.14 %) Combinations: 1 2 pages 20 549 (3.91 %) Combinations: 215 3 pages 19 337 (3.86 %) Combinations: 1 356 % - hompage visitors 23.79 % % - hompage visitors 15.13 % % - hompage visitors 14.24 % Total 53.17 %

  44. References Morville, P., & Rosenfeld, L. (2006). Information Architecture for the world wide web: designing large-scale web sites. O'Reilly Media, Incorporated. Juvina, I., & van Oostendorp, H. (2006). Individual differences and behavioral metrics involved in modeling web navigation. Universal Access in the Information Society, 4(3), 258-269. Data-mining. WordNet Search. Retrieved on 21 January 2013 from http://wordnetweb.princeton.edu/perl/webwn?s=data%20mining

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