MLA 2010 Improving Library Web Sites with Web Analytics


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Presented at the Michigan Library Association Annual Conference, November 11, 2010.

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  • Selected examples of what you can learn from Google Analytics – and how to use that information to change or improve your web site.
  • What does web analytics mean: analysis of data from your web site

    Quantitative data is most often collected by using one or both of two main types of tools.

    Server logfile analyzers: analyze the data that is automatically collected by a web server.

    Advantages include: these files record every transaction in a standard format, can analyze data using a variety of programs, includes visits by search engine spiders
    Disadvantages include: include visits by search engine spiders, don’t count uses of cached pages, require access to the web server logs. In our case, our site is located on the university web server and we don’t have permission to use this data.

    Page tagging methods such as Google Analytics require scripts to be added to pages on a web site – usually using JavaScript & cookies.
    Advantages: only need access to the web pages, not the server logs. Counts page views in the browser, excludes spider crawling.
    Disadvantages: less reliable in recording all transactions – script might not load, user might have cookies and/or JavaScript turned off. Better for observing trends than collecting exact data. Additional set up required to track events other than a page view. (Example: downloads of a pdf document)
  • Look at an entire academic year to spot trends over time – not too surprising, data confirms what we might have guessed.

    Could be used to identify differences in web site use & in-person use of library building.
  • Is site use changing?
    Select “Compare to Past” when setting date ranges.
    When comparing dates in a graph, match days of the week, particularly for short time periods.

    GA won’t give you cumulative totals for day of the week, you could get this by exporting data to Excel and then manipulating.
  • Segment Example – Visits by Browsers/OS
    Looking at visits, broken into segments by particular browser/operating system combinations

    This screen is showing pie chart with percentages – I can select an alternate view. Since I’m comparing 2 date ranges I’m going to select the comparison view.

  • Comparing the beginning of the 2010 fall semester to the beginning of the fall semester in 2009, it’s easier to see the differences.

    Relevance? If our library promotes or creates browser extensions, like Zotero for citation management, or LibX for searching, you should definitely be tracking browsers, browser versions.

    Note mobile browsers on the list
  • Mobile Use - Tracked in Google Analytics Jan 2010 (rolled out as early as Oct 2009 for some accounts)

    Visitors – operating systems & browsers segments (as on previous slide)
    - Mobile devices, Mobile carriers (example on next slide)

    Not all are included – must support JavaScript and cookies (iPhone, iPod, iPad, Android, some others)

    Mobile tracking code
    We haven’t used yet. (Don’t yet have a mobile site for our main web site.)

  • Use of Mobile Devices, December 2009 – October 2010, graphed by week

    Use of mobile devices has been increasing over the last year – overall web site use is about the same in January and September, so the difference for mobile is striking.

    Use from mobile devices results in significantly lower pages per visit and average time on site – are they more efficient users? Is the site not as useful for them? Are they using the site differently?

    Why useful? Questions to ask for further investigation into making site more useful for mobile users, demonstrating the need for a mobile site, mobile versions of catalog and other online resources.

  • Same data – sorted by average time on site. We saw on previous slide the average for all mobile devices was fairly low, there are some significant differences.

    Add to this data: look more closely at what these users are do, also try to get some qualitative data.
  • In 2009, a task force in our library examined the information and services provided to what are called our Extended Programs students (distance and online learning students.) Prior to the launch of our redesigned web site in August 2009, this task force had rewritten web site information about services for these students. They wanted to know how this information was being used, and also wanted to identify other trends in web site use by this group of students.

    There isn’t any way to definitely indentify those particular visitors to the web site using Google Analytics.

    We decided it would be useful to at least identify on-campus and off-campus use.

    Service Providers – useful if your institutional network is identifiable
    Advanced segments – taking the available segments, and making it more flexible
    I.P. Filters
  • Visitors > Network Properties > Service Providers

    In our case, traffic on the Eastern Michigan University network is identified as a specific service provider.

    This dimension can be viewed when looking at many other reports (such as on specific content)
  • Example – We wanted to see if off-campus students are using the web page page that explains services for them.

    - Go to Content
    select Service Provider dimension
    filter to that particular page URL

    Found that the use wasn’t that high. Also, about 55% of the use was internal to EMU.

    [Sept 1 – Sept 30 2009, looking at a particular month in order to be able to compare same part of the academic year.]

    Changes to the web site:
    More direct link from the library home page
    Better linking from other sites (Extended Programs web site, improved link from library “course” in course management system)
  • For the same time period, increase in overall page views: from 186 to 375.
    Significant decrease in the % of page views that are from within EMU: from 56% to 28%

  • Another way to look at this data is through a feature Google Analytics calls Advanced Segments

    Find under “My Customizations” on left menu or at the top of most report screens. Select criteria for each segment, in this case, Network Provider contains EMU, Network Provider does not contain EMU.

  • EMU/Not EMU – Service Provider = Eastern Michigan University
  • Once Advanced Segments is applied – visible in all graphs and data displays

    Trend of off-campus use (green) higher than on-campus use Friday - Sunday
  • Here the EMU/Non-EMU Advanced Segments are helping us see annual trends in on-and off-campus use of the library site.

    Spring break – very significant decrease in on-campus use, hardly any in off-campus use.
  • Hour information is only available in selected categories – here’s one example

    First 7 full weeks of the semester

    4pm – off-campus use equals, from 6pm-2am it’s higher

    Average for all days of the week – see different results on weekdays & weekends.

    Why is all of this information about patterns of EMU/Non-EMU use relevant?

    Looking at this data and additional data, during evenings and weekends, remote/online research assistance is potentially more important than in-library research assistance. This may influence decisions about our participation in 24/7 research chat, and directing students to that chat from our website and our online resources. Keep chat widget in a prominent position, consider adding to more web locations.
  • Another way to get information about users by location is through the use of filters.

    Google Analytics doesn’t allow browsing of data by I.P. address.

    But you can use filters on a profile to select data based on I.P. ranges.

    Filters must be in place before data is collected – off-campus example, decided we wanted the data for past dates, and didn’t have filters in place.

    Therefore – create a duplicate profile to collect data both with and without the filter.
  • Example – Google Analytics profiles for our library research guides.

    The second profile has exclude filters to exclude times the pages are viewed when editing information on the guide.
  • Identify sites that currently drive traffic, sites that you would like to bring more traffic to your site

    That information can motivate developing relationships with stakeholders on these sites

  • Referring Sites (not direct traffic or search engine traffic)

    Surprised how much came from

    Disappointed by how little came from – course management system used by online classes, and some hybrid in-person classes. It includes a default “library” course, but that resulted in very little traffic

    Main lesson – though the task force looking at services for online/distance students revised this library course, and in our opinion improved the content and the links to the library, it’s more important to direct efforts to getting library links in the individual courses.

    Additional evidence from Google Analytics data on Research Guides. Some of the guides are for particular courses, and GA data shows that when the course page on the course management system has a direct link to the guide, that can drive a lot of traffic to that guide. For example, a course guide for a social work class that I know is linked, and 50% of the traffic to that particular guide is from the CMS so far this semester.
  • Different domains/URLs that are essentially the same site

    Can do similar filtering for pages when looking at content, etc.
  • Page views: that’s what is tracked each time the Google Analytics javascript code is loaded.

    Anything that doesn’t contain that code (a pdf document, for example) isn’t tracked.

    See Paul Betty article (references) for example of tracking use of a tutorial.
  • Virtual page views – how we originally set up this tracking. Include a call to javascript in the link (onclick) and include a label for the content. Find reports in Content: Filter for that content label

    Event tracking – Include a similar call to javascript, with parameters: (category, action, label-optional, value-optional). Event Tracking is a separate category under Content.

    Examples of items we’re tracking:

    Outgoing: Selected outgoing links, including links to library videos hosted on another site
    Downloads: Links to pdf/word help documents

    Google Analytics is tracking through these particular links. So in the example of a pdf guide to a library database – the links on the web site include the code that records the use in Google Analytics. But if a faculty member or librarian sends a student a direct link to the pdf – that use won’t be tracked.

  • Clicks on the tabs don’t load a new page, so they aren’t counted as pageviews.

    Recently added event tracking to the tabs.
  • Very small use of Research Guides tab!

    For more information: also examine other links to Research Guides.

    Future investigation: try different label? [website optimizer]
  • Ideally, thoroughly examine your needs and do exhaustive set up at the beginning.

    What are your needs – what kind of data do you want to use?
    Site content and structure – do you want to analyze sites on multiple domains? Separately or together?
    What do you want to track other than page views. Events, actions, marketing campaigns?

    Our approach was in a hurry – learning as you go. We created a Google Analytics profile, added the code to the site, and then looked at what happened and revised as needed.
  • Annotations let you track changes you’ve made in Google Analytics, adds indicators to timeline. Can also use to track related changes to your website that you think may influence certain data.
  • Goal – track specific outcomes: URL destination, time on site, pages/visit

    Go to Settings to set up goals (not in reports)

    Goals are only applied from the date you create the goal, as visible in this goal, which shows that 26% of site visits have included a visit to a Databases page.
  • Goal with a funnel – include the step or steps that lead to that goal.
  • Email options are available for almost any report.

    Yourself or others
    One-time or scheduled (daily, weekly, monthly, quarterly)
    Variety of formats

    Very useful for keeping appropriate people informed about website use.
    More efficient than going in and recreating regularly used reports
  • Could be used to test different labels for home page search tabs.

    But that could create problems with helping users on our site, particularly remote users.

  • MLA 2010 Improving Library Web Sites with Web Analytics

    1. 1. Improving Library Web Sites with Web Analytics 0Web analytics 0What you can learn from Google Analytics 0Patterns of site use - visits 0Visitor characteristics, mobile use 0Visitor segments 0Sources of site traffic 0Actions and special content 0Tips for implementing Google Analytics
    2. 2. Web Analytics 0Analysis of data from your web site 0Quantitative data is usually collected using: 0Server logfiles: AWStats, Webalizer 0Page tagging: Google Analytics, Webtrends, Yahoo! Web Analytics
    3. 3. Patterns of site use over time 0Visits, visitors, visitor characteristics 0Google Analytics features 0Comparing date ranges 0Segments
    4. 4. Visitors Overview
    5. 5. Visits throughout a year
    6. 6. Look for changes in site use
    7. 7. Comparison View
    8. 8. Mobile Use 0Visitors – Mobile devices & carriers 0Mobile device must support JavaScript and cookies 0Mobile tracking code 0separate code 0works for all mobile devices 0doesn’t track all of the same data
    9. 9. Mobile Devices Dec 2009 - Oct 2010
    10. 10. Mobile Devices – Sort by Time on Site Sorted by average time on site
    11. 11. Learning more about visitors 0Example: Off-campus vs. on-campus students 0Methods 0Dimension (Service Provider) 0Advanced Segments 0Filters (I.P.)
    12. 12. Pageviews of library service information for distance/online learners (Sept 2009)
    13. 13. After Web Site Revisions (Sept 2010 compared to Sept 2009)
    14. 14. Advanced Segments 0Compare segments side by side 0Can be used on existing data 0Once created, can be applied to any report or profile
    15. 15. Advanced Segments - Using
    16. 16. On/Off Campus – Weekly Trends
    17. 17. On/Off Campus – Annual Trends
    18. 18. On/Off Campus - Time of Day 0Visitors > Visitor Trending > Visits 0Graph by Hour
    19. 19. Filters 0Google Analytics doesn’t allow browsing of data by I.P. address (privacy). 0Filters can be used to select data based on I.P. ranges. 0Filters must be in place before data is collected. 0Create a duplicate profile in order to collect data both with and without the filter.
    20. 20. Example of Filter Use Excludes administrative use of research guides
    21. 21. Sources of Site Traffic 0Identify sites that do or don’t drive traffic 0Develop relationships
    22. 22. Referring Sites
    23. 23. Filter to combine different sources
    24. 24. Actions & Special Content 0Google Analytics automatically tracks page views 0Other actions that can be useful to track 0Outbound links 0Downloads of documents 0Use of videos, Flash tutorials 0Any other events 0Marketing campaign tracking
    25. 25. Tracking web site actions 0Virtual page views 0Add javascript code to link or within Flash content 0Increases page views 0Event tracking 0Add a similar code 0Tracked separately from page views 0Google Analytics data will not include actions that don’t follow these links
    26. 26. Action: clicks on home page tabs
    27. 27. Event Tracking Results
    28. 28. Implementing Google Analytics 0What are your needs? 0What do you want to analyze? 0Site content & structure 0Actions other than page views 0Create profile 0Review, revise
    29. 29. More Google Analytics Features 0Annotations 0Goals 0Email reports 0Website Optimizer (related service)
    30. 30. Annotations Expand to view or add annotations
    31. 31. Goals
    32. 32. Email Reports Options: one-time or scheduled; pdf, csv, xml, etc.
    33. 33. Google Website Optimizer 0Test two different versions of a page or multiple variables on a page 0
    34. 34. Questions / Discussion Sara Memmott Emerging Technologies Librarian Eastern Michigan University
    35. 35. References - 1 • Betty, P. (2009). Assessing homegrown library collections: Using Google Analytics to track use of screencasts and flash-based learning objects. Journal of Electronic Resources Librarianship, 21(1), 75-92. doi:10.1080/19411260902858631 • Breeding, M. (2008). An analytical approach to assessing the effectiveness of web-based resources. Computers in Libraries, 28(1), 20-22. • Cutroni, J. (2010). Google Analytics. Sebastopol, CA: O’Reilly Media. • Fang, W. (2007). Using Google Analytics for improving library website content and design: A case study. Library Philosophy and Practice, Retrieved from • Gillis, J. (2008). A deeper look at advanced segmentation: Filtering on the fly. Retrieved December 11, 2009, from segmentation.html • Google. How do I create a filter? Retrieved October 9, 2010, from • Google. How do I exclude traffic from a range of IP addresses? Retrieved October 9, 2010, from
    36. 36. References - 2 • Google. How do I manually track clicks on outbound links? Retrieved October 9, 2010, from • Google. How do I track files (such as PDF, AVI, or WMV) that are downloaded from my site? Retrieved October 9, 2010, from • Google. What mobile reporting is available through Google Analytics? Retrieved October 9, 2010 from • Google Code. Event tracking overview. Retrieved November 7, 2010 from • Google Code. Google Analytics for mobile. Retrieved November 6, 2010 from • Khoo, M., Pagano, J., Washington, A. L., Recker, M., Palmer, B., & Donahue, R. A. (2008). Using web metrics to analyze digital libraries. Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries, Pittsburgh, PA, USA. 375-384. doi:10.1145/1378889.1378956 • Kilzer, R. D. (2008). Using Google Analytics in the proprietary OPAC [PDF document]. Retrieved from • Ledford, J., & Teixeira, J., & Tyler, M. E. (2009). Google Analytics (3rd ed.). Indianpolis, IN: Wiley.