A few days ago I came across an article written by Thomas Baekdal, "Measuring Results: Don't be Fooled By Math," that really got me thinking. And not just the usual "Hmm, this is interesting" type of thinking, but the "I'm five levels deep in the Matrix and I just bent a spoon with my mind" type of thinking. My mind was flooded with flashbacks of reports that still haunt my dreams; metrics so meaningless that I'm still not sure why the requests were made.
This caused me to analyze and ask questions about most of the activities I do on a daily basis as an analyst. After thinking about the differences between B2B, B2C, and e-commerce clients, I decided on the below three steps that will help uncover amazing insights for clients, regardless of the industry or who they are selling to.
Visualize the data. Visualize your data beyond the spreadsheets and tables you are used to seeing in your analytics tools. Some of the most interesting and valuable insights can quickly get lost in a spreadsheet. Instead, use your complete set of data to create bar charts or line graphs, or whatever type of chart best represents what is actually occurring in the data. Once you find something interesting, dig in deeper, create another chart, and repeat.
Segment the data. Segmentation is one of the most powerful features of your analytics tool, but to leverage the full power of segmentation you need to go beyond the typical segments, such as marketing channel. Segmenting traffic by channel isn't good enough, you need to look at the specific sets that comprise each channel. For example, if you were to only segment based on your paid search traffic you would likely be lumping a large group of unrelated visitors together. Visitors have different motives for coming to your site; some come for product information or research, some come for support, and some come to purchase. Make your segments reflect the different motives for someone visiting your site. Instead of just "paid search traffic," break your segments out into "paid search current customers," "paid search non-customers," "paid search product support," and so on for whatever fits your site and business.
Choose the right metrics. Current analytics platforms and tools are much more powerful and convenient than going through server logs and pulling together information about your site's visitors. However, with this power and convenience often comes laziness. It's very easy to just log in to Google Analytics or Adobe SiteCatalyst, pull a quick key performance indicator (KPI), and then go about your day. But what do the metrics even mean? How are they calculated? What defines a visit? What's the difference between a visitor and a unique visitor?
Let's take a second and look at a metric that Thomas talks about in his article, average order value (AOV). At first glance, AOV is a pretty solid metric. It's easy to understand, and easy to calculate. But does it always tell what is happening? Let's compare it to another common metric that practically every American has heard of, median household income (MHI). Fluctuations in household income that you hear about on the news use MHI. But why median and not average? That's because the Warren Buffetts and Bill Gates' of the world will significantly skew the average household income. For example, during the 2010 census there were roughly 120,000 households in the U.S. In the same year, the MHI was $49,445, while the average household income was $67,530. That's a difference of almost 37 percent, even with a population of 120,000 households! Just looking at the average presents a much different picture than the median, which has been accepted as a more accurate representation of the "average" American household. Take a deeper look into the metrics you use that are averages (pages/visit, average time on site, conversion rate, etc.), and try to identify if you have some Warren Buffetts that may be hiding what is actually going on.
So those are my main steps to uncovering insights that may be missed by just looking at the default metrics presented in your analytics tool. The process will take you a bit longer, but the reward is definitely worth the effort. The outcome should be sounder and better informed insights, produced by deeper data analysis, which will help increase sales, form submissions, or whatever your organization's KPI is.
Robert Miller is a Senior Analyst at Search Discovery. He is actively involved in industry organizations, such as the Analysis Exchange and the Digital Analytics Association.
With the Analysis Exchange, he helps non-profits capitalize on their website data, and educates aspiring digital analysts about the foundation of digital analytics, from the implementation of a digital analytics tool to performing analysis and making data-driven recommendations for organizations.