I have chosen Google Analytics as my object of study for ENGL 894 Theories of Networks. More specifically, I have chosen the Google Analytics account I manage on behalf of the University of Richmond School of Professional and Continuing Studies. Although this account is a sub-account on the larger University of Richmond Google Analytics roll-up account, I will limit my study to my School’s subdomain’s account.

Google Analytics is a free web activity data collector, aggregator, and reporter. It’s among the most popular web metrics products because it is free, but it is certainly not the only one—other products exist that do a more thorough job of collecting all traffic across multiple web platforms, including advertising and direct email from multiple providers. Google Analytics provides data on all web traffic, but it segments that traffic in reports largely to the benefit of its related products, like Google Adwords and the Google Display Network.

Google Analytics offers web administrators and marketers a window into the activity of users on their website(s). It collects and aggregates data about all web activity on a given domain—in my case, the subdomain SPCS on the richmond.edu domain (spcs.richmond.edu). Any time a person visits any page in the subdomain, Google Analytics collects aggregate data on the visit, including but not limited to user operating system, browser type and version, platform (including desktop, mobile, and tablet), referral source, internal previous and next pages, time spent on the page, time spent within the domain, exit pages, and much more.

Google collects two types of data: metrics (“quantitative measurements of users, sessions and actions”) and dimensions (“characteristics of users, their sessions and actions”) (Google Analytics Academy, 2013). By combining specific metrics and dimensions in a report, web administrators and marketers can answer specific questions about visitor behavior, like which web pages generate more traffic than others and which pages result in visitors remaining on, rather than exiting, the website. As a result, Google Analytics provides key quantitative data to support specific goals, including increased time on site and increased traffic to a specific page. Adding e-commerce and online advertising data collection in Google Analytics offers a complete picture of the effectiveness of online communication efforts across multiple digital channels and platforms.

Google Analytics offers several interesting uses in English studies. Since web sites, especially in higher education, are written as communication, English studies should be able to use Google Analytics to measure whether written communications are effective. Professional communication pedagogy should address specific, measurable ways to determine whether communication is successful; by tracking aggregate visitor behavior on a specific communication goal, like a call to action, writers can hone messages to communicate more effectively.

Google Analytics is a tool for analysis. It collects metadata about web visits as a means to understanding the way visitors navigate a set of web pages. Its analytical tools and methodology are ripe for analysis and critique. Google obfuscates its search algorithm; Google Analytics offers a window into the results of searches, which helps administrators and analysts reverse engineer Google’s search algorithm. As search becomes the default way people engage with the web, Google’s social and economic clout offer intriguing opportunities to open and close markets, to serve the underserved or to underserve a specific population. Obfuscating the source of this power invites social critique, a favored method for English studies.

Google Analytics is a window into the remarkably detailed visitor data Google collects on each visit to a given web site. Such aggregate data provides Google a powerful tool to offer online advertisers that seek to target specific demographics. Scholars in English studies have opportunities to consider the potential social impact of collecting and sharing such data—to analyze and critique collection methodologies, social implications, marketing efforts, and communication channels. The data are quantitative; English studies provides an opportunity to examine, qualify, and put a human face on the data results.

If we consider a single web domain (or, in this case, subdomain) a node in a global virtual network, Google Analytics is the shadow “metanetwork” informing the node. Whether we consider the node the subdomain itself, a folder in a subdomain, or an individual page within a folder, Google Analytics is the shadow network providing metadata at the intersection of the human and the electronic, of the virtual node and the visitor node. Its ability to function as a network while informing about other networks is exciting and complex, ideal fodder for theorizing networks.

Reference

Google Analytics Academy. (2013, October). Key metrics and dimensions defined [Video transcript]. Digital Analytics Fundamentals. Retrieved from https://analyticsacademy.withgoogle.com/assets/pdf/DigitalAnalyticsFundamentals-Lesson3.2KeymetricsanddimensionsdefinedText.pdf

[Behavior flow visualization courtesy University of Richmond SPCS Google Analytics account]