Week 12 – Awesome Web Analytics

November 12, 2011

Kaushik, Avinash. “The Awesome World of Clickstream Analysis: Metrics.” In Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity, 38-73: Sybex, 2009.

 

Avinash Kaushik outlines the ins and outs of metrics and key performance indicators in web analytics.  He defines a metric as a quantitative measurement of statistics describing events or trends on a website while a key performance indicator is a metric that helps you understand how you are doing against your objectives (37). 

Kaushik explains that the visitor experience of someone coming to your website and spending some time browsing around before leaving is commonly called a session, visit, visitor, or some other label (38).  The emphasis on this metric is the time aspect.  Similarly, computing Unique Visitors is when the web analytics tool tries to approximate the number of people who come to your website.  This gets confusing because the tool often counts visitors more than once, creating faulty data.  Kaushik asserts that because of this faulty duplication, there are only two visitor metrics worth assessing in web analytics:  Visits and Absolute Unique Visitors (43). 

Time on Page and Time on Site is designed to measure the time that visitors spend on an individual page and the time spent on the site during a visit or session (44).  However, the web analytics tool is unable to calculate how long the visitors spent on the last page on your site because the second time stamp is missing.  Therefore, the challenge is to know when the exit from the last page happened. 

Kaushik’s favorite web metric is the Bounce Rate which measures the percentage of sessions on your website with only one page view – meaning that  person came to the web page and left without giving the website eve one click.  He prefers Bounce Rate to Exit Rate because Exit Rate simply records how many people left your website from a certain page.  The problem with this is that everyone who enters a website eventually has to leave – “their exit from a page is no indication of the greatness, or lack thereof, of that particular page!” (54). 

The Conversion Rate metric receives the most attention because is measures what comes out of the websites.  Expressed as a percentage, the Conversion Rate is defined as “Outcomes divided by Unique Visitors” (55).  Kaushik believes that most customer behavior is pan-session (or, across multiple sessions) which means that most customers in real-world purchasing will come to the website, check elsewhere, allow time to pass, and then return to the website to complete the purchase (56). 

Engagement as a metric is difficult to measure because it is impossible to derive the kind of visitor Engagement (positive/negative) from degree of Engagement (58).   Indeed, it would be far more beneficial to use other forms of measurement (such as surveys or response cards) to measure the degree to which a visitor was engaged. 

Because of all the options with web metrics, it is important to use ones which fit the four attributes of effective metrics:  uncomplex, relevant, timely, and instantly useful. Additionally, taking time to customize the analytics reporting interface saves time and energy because it will result in “a single clear view [to] help understand performance better and take action” (68).

Web analytics is something that I’ve never really understood until now.  I can definitely see the unsurpassed value of not only using web analytics but also having the insight to know which metrics would be most beneficial for the needs and goals of the company or organization.  Again, the whole idea of keeping the bigger goal in mind comes into play with understanding and interpreting such data.  Without truly understanding what all the numbers mean, faulty reports could result (such as reliance on Daily, Weekly, or Monthly Unique Visitors rather than Absolute Unique Visitors).  Additionally, misinterpreting web analytics could result in fixing something that’s not actually broken.  For example, if someone relied too heavily on the Exit Rate metric, they may think that a particular web page was ineffective because of the high percentage of visitors who left from that page.  However, closer analysis could reveal that visitors left from that page because it was the last page in a series of online check-out steps for making a purchase.  Using common sense as well as giving serious attention to what the metrics are actually measuring is essential for truly delivering accurate results.  What are the implications of failing to correctly interpret web analytic results?


Week 3 – St. Amant’s Prototype Theory

September 11, 2011

Amant, Kirk St. “A Prototype Theory Approach to International Web Site Analysis and Design.” Technical Communication Quarterly 14, no. 1 (2005): 73-91.

The importance of effective communication with a global audience is becoming more relevant to the technical writing community as the international spread of online access continues to grow.  Kirk St. Amant addresses the subtle differences in how international audiences vary in their response to website design.  He emphasizes implementing prototype theory into the research phase of website design in order to create a product that is both usable and credible to a specific cultural audience.  The process includes identifying websites from a specific culture and then implementing both macro-level and micro-level analysis in order to note patterns in websites “designed by individuals from a particular culture for individuals from that same culture” (82).

I find Amant’s emphasis on understanding and appealing to an international audience as essential to my writing as a technical communicator.  The statistics revealed in the essay indicate astounding growth in developing nations in the realm of online access; therefore, to be competitive in this field, I need to not only have an understanding of this change but also have the skills to effectively design websites for an international audience.  It is key for me, as someone new to the field of technical writing, to begin addressing this issues immediately “while most of the world is still getting online” in order to “anticipate and address or avoid potential differences before they lead to intercultural communication problems” (75).  Beginning to implement aspects of prototype theory will enhance my work’s international appeal by helping me establish good practices from the beginning rather than attempting to repair damage later.  Amant’s essay also reminded me of the need to allow for substantial time dedicated to thorough research when I am beginning a project of this nature.

In addition to recognizing the importance of differences between cultures regarding visual displays, icons, advertising, and color, I also foresee the importance of addressing cultural differences in how to write.  During a multicultural education class I took during my undergraduate studies, I discovered that certain cultures prefer information to be presented in a straightforward manner whereas other cultures expect to apply a greater level of inference to text they read.  These issues of how language functions within a culture will also come into play with an international audience.

At the end of the essay, Amant comments that using prototype theory is “only a first and basic step in addressing a larger issue of cultural expectations.”  What are some of these “larger issues” of cultural expectations that come into play with an international audience?