Web Metrics – Post #1: Visit Characterization

One common thought seems to emerge repeatedly in my research of web analytics thus far (particularly concerning metrics) the past couple of weeks. This thought, generally speaking, is that when it comes to web analytics, it is important to understand that data is people and people are data.  This is a valuable perspective to apply to web analytics because in the words of Dr. Liraz Margalit, “human behavior is more complex than metrics”; though might help measure human behavior, but they are not the behavior (Helbling & Wilson, 2017).  Dr. Margalit publishes a column in Psychology today entitled “Behind Online Behavior” wherein she approaches analytics as a “neuromarketer,” using metric data to detect “digital body language.” While Margalit believes analytics should not only consist of what people say, but “how” they say it I’ve selected the two metrics, the “what” is certainly an important starting point. Visit characterization and engagement are perhaps the two metrics that most closely measure “what” is being said. (Note: I’ve also selected these two metrics for my first pots as they are two categories I have confused with each other during my reading/research.)

If visitor characterization (distinguishing attributes of visitors) answers the question “who?”, then visit characterization is the first metric that begins to answer, “what?” by measuring “what the visit consists of” (Web Analytics Association). The “what” that visit characterization answers is different from (albeit a precursor to) the “what” that engagement answers. For visit characterization, “what” seems to be primarily defined by pages—what pages do visitors enter the site on? What pages do they land on from there? What pages, within their visit, do they repeatedly land on?  Do they leave the site in the middle of their sessions? If so, do they return? What outside pages direct them to various entry and landing pages within their visit?  

Nike Golf is an example of a company that successfully used the visit characterization metric to increase traffic to (and through) their website. Nike launched Nike Golf in 1986 hopes of raising profit amidst a financial crisis.  As the brand (and web analytics) evolved, it gained notoriety through endorsement of Tiger Woods and its pro line, but it still struggled to distinguish itself online due to “lack of right keyword strategy and low-level crawlability” (Aitha, 2014). One can see, on the surface, where without Web Analytics this would be the case! Without SEO, a search for “Nike Golf” would likely yield clear visitor paths to several Nike pages, and several golf pages, but not open the door to visitors’ to the entry and/or landing page the company desired. However, through keyword strategy and content optimization, Nike Golf increased their organic search traffic by 169% (Aitha, 2014).

Kaushik (2010) writes that, “visits report the fact that someone came to your website and spent some browsing before leaving.” Nike’s strategy of optimizing content and SEO ensured visits, ideally ones that ultimately result in returns and/or purchases. But returning to Dr. Margalit’s point, the visit itself (and its duration) is simply an occurrence. Looking at the characteristics of the occurrence, particularly referrers and click-through rates, analysts might be able to infer qualitative data about the psychology of the person behind the quantifiable visit as well. The engagement metric seems to be defined by data that offers even more insight to this psychology.

References

Aitha, (2015, April  24^th). Nike Golf Leveraged SEO and Got 169% Total Increase in Organic Search Traffic. Retrieved from: http://www.digitalvidya.com/blog/nike-golf-leveraged-seo-and-got-169-total-increase-in-organic-search-traffic-dmblog-0104/

Helbling, M. & Wilson, T. (2017, June 20). Digital Analytics from a Psychological Perspecitve with Dr. Liraz Margalit. Digital Analytics Power Hour.  Podcast.

Kaushik (2010). Web Analytics 2.0. The Art of Online Accountability & Science of Customer Centricity. Wiley Publishing, Inc.

Web Analytics Association. Web Analytics Defintions. Retrieved from: https://www.digitalanalyticsassociation.org/Files/PDF_standards/WebAnalyticsDefinitions.pdf