Web traffic and campus trends: a multi-institution analysis

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Ordinarily, it is difficult to generalize operational research conducted at one library to the environment of another. Different survey instruments, user populations, and sampling techniques make direct comparisons difficult. Despite these clear differences, libraries continue to use this literature to plan new services. There is a clear need to establish a baseline for comparison. The use of web server log statistical reports and other web analytics may offer this baseline. Such reports are now available to most libraries, and offer a rich, and consistent, look at library user behavior. This data tells a rich story about library use, and offers a valid point of comparison between institutions. The Orbis-Cascade Alliance Research Interest Group presents these initial results as our first collaborative effort. We argue that differences between institutional environments that have long been assumed are now clearly visible in simple metrics such as most-viewed pages, point-of-entry, and peak hours. These points of comparison are analyzed for three types of Alliance libraries: a public and a private four year residential campus, and an urban commuter university. The analysis offers empirical evidence of the commonalities and differences between these member libraries.

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  • “Visitors” = unique IP -addresses
  • Visits = one IP address can have multiple visits
  • Page views = may include images, but just counts as one hit.
  • Hits include counting every single element (images, PDFs, other) as separate hits. Generally researchers disregard this measure as too noisy.
  • LauraWhat was on web server is a sub-set of what people are actually seeing on the web interface.
  • Carnegie Classifications:UO is listed as a Doctoral/Research Universities-Extensive universityOHSU is listed as a Medical Schools and Medical CentersPortland State is listed as an Doctoral/Research Universities-Intensive university
  • It’s hard to see the pattern because of the scale difference between the institutions.
  • This doesn’t tell us anything, right?Yes it does – UO has a lot more hits on its site – how their servers are structured, etc.We can’t compare our institutions to each other, but if regraph can aggregate together days of week and see we all follow the same pattern.
  • Normalized Implications for server maintenance and administration – don’t take down the systems at peak times.
  • Normalized
  • Sorted the top 150 – 180 pages from each website.
  • PSU: June 2008; UO / OHSU: Jan 2008.Larger UO homepage traffic MIGHT reflect more public workstations OR the sheer number of links on the homepage.PSU redesign was intended to be flat: ‘all within 3 clicks’: this might explain why 3/4rs of the pageviews are to the homepage or first ‘top level’ page.OHSU has a long tail. Also why OHSU home page is the smallest of all 3 b/c more of your most viewed pages are showing up in that graph. When people are not going to home page are they going to catalog, JSTOR, etc? We just don’t record that traffic.
  • 160-200 most trafficked pages. UO/OHSU Jan 08PSU: Jun 08.
  • OHSU just missing dataWhat does this data mean? Everyone going to class in middle of quarter – they are going to class.
  • One way we know the data is pretty good is that the Monday (blue line) with the diamond in week 9 is Memorial Day. Oddly people at PSU are cramming on Memorial Day, but people at OHSU and UO take a holiday weekend.
  • Web traffic and campus trends: a multi-institution analysis

    1. 1. Web traffic and campus trends: a multi-institution analysis<br />Jon Jablonski, University of Oregon LibrariesRobin Paynter, Portland State University LibraryLaura Zeigen, Oregon Health & Science Univ. Library<br />
    2. 2. Why we did this project<br />Orbis Cascade Alliance Research Interest Group <br />Were there differences in web use attributable to institution type?<br />We knew we all had some transaction log data<br />What the literature shows/did not show<br />
    3. 3. Web Log Analysis Methodologies<br />Conceptual Framework definition*<br />“These studies usually introduce<br />a set of concepts related to an<br />existing (or future systems), or<br />to a set of objects, or to behavior<br />aspects of participants. Concepts<br />are then used to construct <br />conceptual frameworks, which<br />provide the plan, purpose and <br />direction for the study. Depending <br />on the goals, data, and technology<br />the conceptual frameworks offer<br />a choice of methodologies: <br />surveys, data analysis, literature<br />review or many others.” <br />Discourse Analysis<br />Historical Method<br />Conceptual Framework /<br />Inquiry<br />Ethnography<br />Content Analysis<br />Case Study<br />Phenomenology / <br />Ethnomethodology<br />*Jansen, B. J., Spink, A., & Taksai, I. (2009). Handbook of research on web log analysis. <br />Hershey, PA: Information Science Reference. p 507<br />
    4. 4. Conceptual Framework/Inquiry<br />Transaction Log Analysis<br />Search Log Analysis<br />Complementary Methods<br />Key Performance Indicators<br />Definitions*<br />Transaction log analysis: <br /> “analysis of Web system logs”<br />Blog analysis: <br /> “analysis of Web blogs”<br />Search log analysis: <br /> “analysis of search engine logs”<br />* Jansen, Spink & Taksai, p. 508<br />
    5. 5. Transaction Log Analysis <br />■ Hit *<br />■ Unique visitors<br />■ New/Return visitors<br />■ Page views<br />■ Page views per visitor<br />■ Visit duration<br />■ IP address<br />■ Visitor location<br />■ Visitor language<br />■ Referring pages/sites (URLs)<br />■ Keywords<br />■ Browser type<br />■ Operating system version<br />■ Screen resolution<br />■ Java or Flash-enabled<br />■ Connection speed<br />■ Errors<br />■ Visitor paths/navigation<br />■ Bounce rate<br />Behavioral vs. intentional<br />Server vs. client side<br />Cache vs. cache busting<br />Proxy servers | Fixed/Dynamic IPs<br />Flash cookies vs. cookies<br />Page tagging<br />Web 2.0 (blogs, RSS, social networking)<br />Analysis packages (AWStats vs. <br /> Php my visits)<br />Public computers with default library <br /> homepage<br />Website links to other servers<br />Campus portals, other access venues<br />Server not reporting data<br />* Napier, H., Judd, P., Rivers, O., & Adams, A. (2003). E-business technologies (pp. 372-380). <br />Boston, MA: Thomas Course Technology.<br />
    6. 6. Key Performance Indicators<br />What are key metrics for academic library websites? <br />Same as commercial websites?<br />High page views?<br />Visit duration? (Shorter or longer better?)<br />Is benchmarking possible, useful and/or desirable?<br />Return visitors or unique visitors? <br />(or looking for seasonal changes, i.e. fall more new<br />and spring more returning visitors?<br />Trends in data? (e.g., fewer error messages=improved user experience)<br />Different KPIs for administrators and designers*<br />*Cohen, L. B. A Two-Tiered Model for Analyzing Library Website Usage Statistics, Part 2: Log File Analysis. <br />Portal v. 3 no. 3 (July 2003) p. 517-26 <br />
    7. 7. OHSU Unique Visitors<br />
    8. 8. OHSU Number of Visits<br />
    9. 9. OHSU Page Views<br />
    10. 10. OHSU Hits<br />
    11. 11. What we looked at<br />Only basic web traffic<br />No OPAC<br />No digital collections<br />No institutional repositories<br />No databases or other electronic resources<br />
    12. 12. 42 (or 47) links overall on homepage.<br />
    13. 13. 6 to non-www library servers.<br />
    14. 14. 2 to non-library pages<br />
    15. 15. Leaving 39 links that get counted.*<br />*not counting Facebook, Twitter and H1N1.<br />
    16. 16. Leaving 31 links that get counted<br />
    17. 17. 18 links counted<br /><ul><li> Shaded areas link to </li></ul>catalog or other pages where<br />links go to resources not on<br />the OHSU web server.<br />A-Z journals and databases go <br />to another web server page. <br />all links from those pages are<br />routed through the catalog or<br />our EZProxy server<br />
    18. 18. Portland State Doctoral/Research Universities-Intensive university<br />Until recently been primarily a teaching universityStudent body largely composed of later life adult students, who are employed (full or part-time) and have families. ~39% attend part-time. Graduate student population largelyin professional schools (social work, education, urban planning, etc).<br />University of Oregon<br />Doctoral/Research Universities-Extensive university<br />Student body largely composed of young adults going to school full time.<br />Many work part time while going to school.<br />OHSU<br />Medical Schools and Medical Centers<br />Largely graduate, professional programs of later life adult students, many of whom have families, some of whom are employed part or full time while they are going to school full time.<br />
    19. 19. Comparison across institutions<br />We looked at basic pieces of the web logs to see if comparing across institutions was a valuable exercise.<br />What did we find that was different and what did we find that was the same?<br />Just looking at page views per month shows us differences in our institutional calendars.<br />
    20. 20. Traffic follows the academic terms……except for OHSU, which doesn’t have a strong term system. …and PSU doesn’t appear to take spring break.<br />Page views per month<br />
    21. 21. Re-graphing fixes scale problem.<br />*But PSU still doesn’t seem to have a spring break.<br />
    22. 22. But what about the scale difference?<br />Unique visitors<br />Pageviews<br />
    23. 23. Can these results be true?<br />Our study analyzed web server transaction logs, and the Nicholas et al. study analyzed journal database <br />usage across four institution types….still the resemblance in page view data by institution type is interesting<br />Figure 1 graph: Nicholas, D., Huntington, P., & Jamali, H. R. (2007). Diversity in the information seeking behaviour <br />of the virtual scholar: Institutional comparisons. Journal of Academic Librarianship, 33, 629-638. (p.632)<br />
    24. 24. Visits per day of week<br />Spring term, 2008<br />
    25. 25. Visits per day of week: aggregate<br />Spring term, 2008<br />Normalized<br />
    26. 26. Visits per day of week: average<br />Spring term, 2008<br />Normalized<br />
    27. 27. Compare types of pages<br />Top level pages<br />The homepage and other basic pages that are mostly gateways to pages with actual content and information. These are generally accessible directly from the homepage and include things like a site index, an ‘about the library’ page, and a getting started guide.<br /> Library &apos;how to&apos; pages<br />Pages whose primary purpose is to inform users how to use the library. These include instructions for accessing collections and services. <br /> Administrative/operations pages<br />Pages that describe in detail how the library is structured as an organization. These pages include staff directories, lists of subject specialists, phone lists, and maps of the library. <br /> Tools<br />URLs associated with home-made databases, web forms, other applications.<br /> Department homepages<br />Unit homepages that reflect the organization of the library. Top level pages for branches and divisions. <br />Content<br />Individual research guides, documents, lists of resources, digitized materials (although most of these are elsewhere.<br />
    28. 28. Comparision: Most viewed pages<br />PSU<br /> 79% of total traffic<br />UO<br /> 57%<br />OHSU<br /> 14%<br />Homepage<br />Other top-level pages<br />Content<br />Department homepages<br />Tools<br />Administrative/operations<br />‘How to’ pages<br />
    29. 29. Top viewed?<br />Homepage as percent of traffic:<br />PSU: 38%<br />UO: 32%<br />OHSU: 4%<br /># of pages viewed:<br />PSU: 2,009<br />UO: 10,059<br />OHSU: &gt;2000<br />
    30. 30. Visits per day: by week of term.<br />
    31. 31. Visits per day: by week of term.<br />
    32. 32. Now what?<br />Implications for design<br />Where should we put our web efforts?<br />Services: what are our hidden gems?<br />Analysis of all of the library’s web traffic (OPAC, resources on other servers)<br />Possible benchmarking?<br />
    33. 33. Challenges in process/points to ponder<br />Using different software can make comparisons challenging.<br />Get your data in raw text format (or .csv or at least .htm) for ease in importing into spreadsheets.<br />Web log analysis is inherently fuzzy around the edges – proceed with caution!<br />
    34. 34. Resources<br />Handout/bibliography/slides<br />www.ohsu.edu/library/staff/zeigenl<br />http://nwlibresearch.pbwiki.com<br />SlideShare<br />Orbis-Cascade Alliance Research Interest Group<br />http://www.orbiscascade.org/index/research-interest-group<br />http://nwlibresearch.pbwiki.com<br />Next in-person meeting in June or July 2010<br />
    35. 35. Contact information<br />Jon Jablonski, UC-Santa Barbara jonjabbers@gmail.com<br />Robin Paynter, Portland State Universitypaynter@pdx.edu / 503-725-4501<br />Laura Zeigen, Oregon Health & Sci. Universityzeigenl@ohsu.edu / 503-494-0505<br />

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