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Web traffic and campus trends: a multi-institution analysis

Jon Jablonski, University of Oregon Libraries�Robin Paynter, Portland State University Library�Laura Zeigen, Oregon Health & Science Univ. Library

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Why we did this project

  • Orbis Cascade Alliance Research Interest Group
  • Were there differences in web use attributable to institution type?
  • We knew we all had some transaction log data
  • What the literature shows/did not show

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Web Log Analysis Methodologies

Conceptual Framework /

Inquiry

Phenomenology /

Ethnomethodology

Content Analysis

Ethnography

Historical Method

Discourse Analysis

Case Study

*Jansen, B. J., Spink, A., & Taksai, I. (2009). Handbook of research on web log analysis.

Hershey, PA: Information Science Reference. p 507

Conceptual Framework definition*

“These studies usually introduce

a set of concepts related to an

existing (or future systems), or

to a set of objects, or to behavior

aspects of participants. Concepts

are then used to construct

conceptual frameworks, which

provide the plan, purpose and

direction for the study. Depending

on the goals, data, and technology

the conceptual frameworks offer

a choice of methodologies:

surveys, data analysis, literature

review or many others.”

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Conceptual Framework/Inquiry

Transaction Log Analysis

Search Log Analysis

Complementary Methods

Key Performance Indicators

Definitions*

Transaction log analysis:

“analysis of Web system logs”

Blog analysis:

“analysis of Web blogs”

Search log analysis:

“analysis of search engine logs”

* Jansen, Spink & Taksai, p. 508

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Transaction Log Analysis

Behavioral vs. intentional

Server vs. client side

Cache vs. cache busting

Proxy servers | Fixed/Dynamic IPs

Flash cookies vs. cookies

Page tagging

Web 2.0 (blogs, RSS, social networking)

Analysis packages (AWStats vs.

Php my visits)

Public computers with default library

homepage

Website links to other servers

Campus portals, other access venues

Server not reporting data

Hit *

Unique visitors

New/Return visitors

Page views

Page views per visitor

Visit duration

IP address

■ Visitor location

■ Visitor language

■ Referring pages/sites (URLs)

■ Keywords

■ Browser type

■ Operating system version

■ Screen resolution

■ Java or Flash-enabled

■ Connection speed

■ Errors

■ Visitor paths/navigation

■ Bounce rate

* Napier, H., Judd, P., Rivers, O., & Adams, A. (2003). E-business technologies (pp. 372-380).

Boston, MA: Thomas Course Technology.

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Key Performance Indicators

Return visitors or unique visitors?

(or looking for seasonal changes, i.e. fall more new

and spring more returning visitors?

High page views?

What are key metrics for academic library websites?

Same as commercial websites?

Visit duration? (Shorter or longer better?)

Is benchmarking possible, useful and/or desirable?

Trends in data? (e.g., fewer error messages=improved user experience)

*Cohen, L. B. A Two-Tiered Model for Analyzing Library Website Usage Statistics, Part 2: Log File Analysis.

Portal v. 3 no. 3 (July 2003) p. 517-26

Different KPIs for administrators and designers*

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OHSU Unique Visitors

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OHSU Number of Visits

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OHSU Page Views

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OHSU Hits

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What we looked at

  • Only basic web traffic
  • No OPAC
  • No digital collections
  • No institutional repositories
  • No databases or other electronic resources

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42 (or 47) links overall on homepage.

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6 to non-www library servers.�

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2 to non-library pages

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Leaving 39 links that get counted.*

*not counting Facebook, Twitter and H1N1.

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Leaving 31 links that get counted

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18 links counted

  • Shaded areas link to

catalog or other pages where

links go to resources not on

the OHSU web server.

A-Z journals and databases go

to another web server page.

all links from those pages are

routed through the catalog or

our EZProxy server

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OHSU

Medical Schools and Medical Centers

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.

Portland State �Doctoral/Research Universities-Intensive university

Until recently been primarily a teaching university��Student 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 largely�in professional schools (social work, education, urban planning, etc).

University of Oregon

Doctoral/Research Universities-Extensive university

Student body largely composed of young adults going to school full time.

Many work part time while going to school.

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Comparison across institutions

  • We looked at basic pieces of the web logs to see if comparing across institutions was a valuable exercise.
  • What did we find that was different and what did we find that was the same?
  • Just looking at page views per month shows us differences in our institutional calendars.

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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.

Page views per month

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Re-graphing fixes scale problem.

*But PSU still doesn’t seem to have a spring break.

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But what about the scale difference?

 

UO

PDX

OHSU

Apr-08

104,352

10,110

9,682

May-08

105,337

12,466

8,584

Jun-08

110,773

18,350

11,755

Month

UO (2008)

PSU (2009)

OHSU (2008)

Jan

1,626,357

264,529

572,178

Feb

1,890,241

299,180

604,895

Mar

1,697,888

324,171

607,958

Apr

1,913,613

345,586

862,900

May

1,817,830

324,230

883,564

Jun

1,402,282

246,388

783,779

Unique visitors

Pageviews

UO

PSU

OHSU

undergrads

16,681

21,674

grads

3,695

6,298

20,376

27,972

~11,000 FTE

faculty

1,714

1,477

degrees

5,177

4,966

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Can these results be true?

Figure 1 graph: Nicholas, D., Huntington, P., & Jamali, H. R. (2007). Diversity in the information seeking behaviour

of the virtual scholar: Institutional comparisons. Journal of Academic Librarianship, 33, 629-638. (p.632)

Our study analyzed web server transaction logs, and the Nicholas et al. study analyzed journal database

usage across four institution types….still the resemblance in page view data by institution type is interesting

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Visits per day of week

Spring term, 2008

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Visits per day of week: aggregate

Spring term, 2008

Normalized

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Visits per day of week: average

Spring term, 2008

Normalized

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Compare types of pages

  • Top level pages
    • 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.
  •  Library 'how to' pages
    • Pages whose primary purpose is to inform users how to use the library. These include instructions for accessing collections and services.
  •  Administrative/operations pages
    • 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.
  •  Tools
    • URLs associated with home-made databases, web forms, other applications.
  •  Department homepages
    • Unit homepages that reflect the organization of the library. Top level pages for branches and divisions. 
  • Content
    • Individual research guides, documents, lists of resources, digitized materials (although most of these are elsewhere.

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Comparision: Most viewed pages

Homepage

Other top-level pages

Content

Department homepages

Tools

Administrative/operations

‘How to’ pages

OHSU

14%

PSU

79% of total traffic

UO

57%

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Top viewed?

  • Homepage as percent of traffic:
    • PSU: 38%
    • UO: 32%
    • OHSU: 4%
  • # of pages viewed:�
    • PSU: 2,009
    • UO: 10,059
    • OHSU: >2000

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Visits per day: by week of term.

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Visits per day: by week of term.

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Now what?

  • Implications for design
  • Where should we put our web efforts?
  • Services: what are our hidden gems?
  • Analysis of all of the library’s web traffic (OPAC, resources on other servers)
  • Possible benchmarking?

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Challenges in process/points to ponder

  • Using different software can make comparisons challenging.
  • Get your data in raw text format (or .csv or at least .htm) for ease in importing into spreadsheets.
  • Web log analysis is inherently fuzzy around the edges – proceed with caution!

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Resources

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Contact information

  • Jon Jablonski, UC-Santa Barbara jonjabbers@gmail.com

  • Robin Paynter, Portland State University�paynter@pdx.edu / 503-725-4501

  • Laura Zeigen, Oregon Health & Sci. University�zeigenl@ohsu.edu / 503-494-0505