ILS 534 Reaction Paper #2 Jennifer B. Gardner Southern CT State University
November 13, 2012
Introduction
This paper discusses the article by Wei Fang, "Using Google Analytics for Improving Library Website Content and Design: A Case Study" published in Library Philosophy and Practice in 2007. Fang makes the key point that websites are an increasingly essential component of
library service. And things are changing rapidly. The balance between the physical facility and digital resources is shifting and providing access through the web is growing in importance. But just having a website is not sufficient. The library’s website must be user-centered and effective. Web analytics can help. Defined by the Digital Analytics Association as,
“the collection, analysis and reporting ofWeb site usage by visitors and customers of a web site,” web analytics is a utility that helps libraries understand user experience and guides decision-making (McFadden, 2010). Fang’s article shows that although in-depth web analytics were previously cost prohibitive for many libraries, Google Analytics, specifically, has provided the opportunity to delve more deeply into website effectiveness and better deliver on the library’s mission.
Summary and Reactions
Fang profiles the Rutgers-Newark Law Library (RNLL), the largest law library in New Jersey. The RNLL website has more than 2200 visitors per day and serves as an excellent research subject. Fang’s article begins with a simple history of Google Analytics, which was started when Google purchased Urchin Software. Google then made the software available online in 2006. Somewhat surprisingly, Google Analytics is available for free to anyone with a Google account.
To use the tool, code is inserted into each html page so that it can be tracked. This can be achieved by simple copy and paste which took RNLL about 20 minutes. However, Fang doesn’t dwell on the fact that the process can also be more difficult if the site has numerous static pages or doesn’t use templates. It is also notable that while the code is very basic, a possible issue is that the
code could cause a page to “hang” when loading if Google has a server problem, java is incompatible or there is an internet connection issue (Turner, 2010).
The article also discusses strengths and weaknesses of other research methods and surmises that web analytics is clearly superior. He notes that print surveys and online surveys both have limitations and basic counters do monitor traffic but they can only detect volume. Also analyzing log files can be “ a nightmare” and does not capture some user information Google can, like browser, OS, and navigation path (Fang, 2007). Fang favors web analytics for its “ objective and multi-faceted” data collection that occurs automatically, minimizing labor and human error.
Having chosen web analytics, Fang then quickly lauds Google Analytic’s superiority vs others. He states that GA is “by far the most sophisticated” but this seems an exaggeration. There are numerous other web analytic tools and it is difficult to say which one is truly the best. A few other well-reviewed technologies I found in the literature include Piwik, an open source option, as well as several other commercial tools like Coremetrics, Adobe’s Omniture and WebTrends.
Google Analytics does have strong functionality. There are over 80 predefined visualized reports that help simplify the data and make it more understandable. These reports provide easy summaries and ensure librarians don’t have to read lines of data and churn through numbers.
Fang outlines some of the key reporting tools employed by RNLL. One of which is Defined Funnel Navigation which shows if the site is easy to navigate, or being navigated as the planners intended. Content by Titles reveals which pages of the website are visited the most. It can help librarians understand what content matters to users. Visitor Segmentation allows you to look at which users are looking at what. It can provide important information about who is using the site and how they are using it. GA can analyze country, region, keyword, browser type and more. Google’s Key Word helps tell you what search words brought users to your site and from where. It is even possible to see the users’ connection speeds and computer configurations. This can be
valuable in deciding how much graphical content to have. Another report is Site Overlay, which shows how many clicks on each link. This has been improved significantly since the Fang article and is now called In-Page Analytics. But an article by Tabatha Farney points out that Google’s tool does not easily discern between the same link repeated in multiple places on a webpage (Farney, 2011).
GA’s Trend Reporting is another function that provides a look at different points in time and compares site performance. In this way you can see changes and growth and Fang used this data to prove improved effectiveness after the redesign (21% more new visitors, returning visitors
increased by 44%) (Fang, 2007). However, regarding the data used to evaluate the RNLL redesign, the time periods used do arouse some concern. RNLL looked at 22 days before and after September 18th, 2006. But perhaps it would be better to use the same month on the following year, given the impact of the academic calendar and time taken for users to familiarize with the new site. A simple before and after could be very misleading in this setting.
In addition to the reports, data from GA can be exported allows for further evaluation. The report data can be exported to text, XML and MS Excel and other statistical programs for even deeper analysis. However, you cannot import additional data into GA. This means you can’t integrate data you may have on server log files and create any kind of combined report or study.
Before embarking on a web analytic venture a library should have a very clear picture of what the goals of the site are, and how they fit into the broader organization goals. But Fang does not offer much discussion on this though Turner and others make the point well, particularly in terms of Key Performance Indicator’s (KPI’s) which are measures of website performance.
“While KPIs can be macro (site-wide), generic, and applicable across many different kinds of websites, it is even more important to closely tie KPIs to organizational goals that are supported by the website, goals that are specific to the website. By tracking and observing these key, site-specific metrics over time, an organization will gain the ability to
determine relative success or failure of effort of the website and, consequently, the website’s effectiveness (Turner, 2010, p.263). “
The only strategic goal specifically mentioned by Fang is the general mission of the library and he fails to fully explore the role the website is meant to play in it, or how KPI’s will measure that.
Despite this lack of focus, the RNLL used the data they obtained from all of their GA reports to make important changes on their site. Using GA evidence they redesigned the site to reflect key learning. Items were added, subtracted and moved in the layout. RNLL added a Most-Viewed items section, Other Links of Interest, and reorganized things. This was to serve patron needs and to promote the library’s own interests – for example they wanted to increase traffic to the pages on their Research Portals so they made them more prominent. They also built in one click access to critical information. But Fang’s team did seem to ignore some of the data as well. Though it was revealed that JURIST headlines were seldom clicked, they still decided to retain them on the main website (with some tweaks) because administrators wanted them included. So there is
professional discretion involved as well.
Overall the RNLL hypotheses were largely supported by the data from GA. Following the changes they made traffic increased where they wanted it to go. But does that truly mean the users were actually “happier” to be on those pages? Or more satisfied? Following a link doesn’t
necessarily ensure satisfaction. Does viewing more than three pages actually mean that you found more valuable information? Or could it just mean you were jumping around? Deriving meaning from GA data can be slippery. And although GA can reveal users paths, it seems that there is some question over whether librarians can truly discern users intentions and thoughts from these reports. Were users following the intended path or just casting about aimlessly, never really finding what they wanted? It is very hard to say.
better or worse. Turner talks about “conversion goals” and this helps define what behavior the library wants (p.268). But Fang doesn’t really do this. Even looking at return visitors doesn’t necessarily indicate “loyalty”, as there is no discussion of where else users would go for the
information. Who is the competition? Did their site visits go up when Fang redesigned? And again, I think the time period is problematic – were there exams in October but not September? This type of contextual data could affect motives, interest, intentions and other behavior.
Fang also neglects the return on investment question. Turner does a better job of drawing comparisons between the non-profit library and e-commerce, though the discussion of Google’s Conversion University and the effort to determine ROI also raise more questions about how similar these types of organizations really are. It seems extremely difficult to value access to one digital resource versus another. Regardless, Fang does not consider the bottom line enough. A Return on Investment assessment is not perfect, but it should be carefully considered in a budget-constrained environment and Fang just glances over it.
Overall, it would be better to use GA and other research tools in combination. Follow-up surveys, though dismissed early on by Fang, could certainly help. An article in Library Technology Reports by Kate Marek comments that web analytics’, “raw data tells only a portion of the story: the “what.” To get at the “why,” and even the “how” you must get into the mindset of the user…you must ask them, using methods such as electronic survey, focus groups and user testing (Marek, 2011, p.6).” Fang’s team seems to have used GA analytics alone to make key decisions.
And there are still more issues not covered in the Fang article, like privacy. As noted by another author, “behind the scenes data collection may make many librarians uncomfortable (Marek, 2011, p8).” Still, most users realize data is being collected all over the web. So how can librarians resolve this conflict? The best way seems to be by guaranteeing anonymity of users and also by using disclosure and offering opt-outs.
A final issue not covered by Fang is security. Google houses all of the data and controls everything so libraries are at their mercy if they decide to change anything. And Google does confess that they share data with 3rd parties. But they assure users it is all anonymous and
aggregated. Still the most conservative may not accept these promises. Also, GA offers no intensive support team to guide librarians, though there are documents and FAQ summaries. It should be remembered that GA provides accurate data, but manipulation and interpretation of it is entirely up to the library.
Conclusions
Fang’s article does a good job of exploring the potential of Google Analytics to help
libraries deliver a better user experience on their websites. Though he fails to discuss KPI’s and the importance of linking metrics to specific goals, Fang shows that his team made some effective decisions about site redesign. And while Google Analytics has some notable issues with functionality and privacy, overall it is a powerful tool for improving library service. The Fang article is a good introduction to how Google Analytics can be employed, but further research would be needed to fully grasp all of the issues inherent with Google and web analytics.
References
Fang, W. (2007). Using Google Analytics for improving library website content and design: a case study. Library Philosophy and Practice, LPP Special Issue on Libraries and Google.
Farney, T. A. (2011). Click Analytics: Visualizing Website Use Data. Information Technology & Libraries, 30(3), 141-148.
Marek, K. (2011). Chapter 1: Web Analytics Overview. Library Technology Reports, 47(5), 5-10. McFadden, C. (2010). Optimizing the online Business channel with web analytics. Christopher - May 2005. Digital Analytics Association. Accessed at http://tinyurl.com/b5mw4fk
Turner, S. (2010). Website statistics 2.0: Using Google Analytics to measure library website effectiveness. Technical Services Quarterly, 27 (3), 261-278.
