Recently I started reading a new book on social media, and I quickly became concerned with one part of the book that provided a laundry list of social media “metrics”. Social media is certainly a hot topic, and many companies are interested in how to best measure this new marketing channel. Unfortunately, too many of the “metrics” in this list were not even metrics but instead reports (e.g., Buzz (?) by social channel, Sentiment by volume of posts, Method of content delivery, etc.). I’ve seen this problem not just in this book but across our industry — blog posts, articles, whitepapers, presentations, marketing materials — and yes, even at my own company.
It’s concerning that as we open up a flood gate of new metrics — both meaningful and useless — many people still don’t have a firm understanding of the differences between metrics, KPIs, dimensions, and reports in the existing web analytics world.
Too often these data terms are being carelessly interchanged or misused. You may be thinking: “It’s harmless if my marketing director wants to refer to all our metrics as KPIs. At least he’s excited about the data. It’s okay if our agency inadvertently refers to reports as metrics. No big deal. Brent, you’re putting the ‘anal’ in analytics.”
In 2000, I was understanding (and I was in the process of figuring it out for myself!).
In 2005, I became concerned.
In 2010, I’m just plain upset.
How can we truly take a data-driven mindset to the next level within our various organizations and the field of web analytics when we don’t use data terminology properly? Sloppy logic waters down the impact that analytics data can have on our organizations. It creates a weak foundation for building the data-driven evolution we’re trying to foster. We can’t have industry experts, analysts, consultants, business managers, and executives perpetuating this problem as we enter emerging analytics frontiers such as social media, mobile, and apps.
Enough is enough — it’s time to tighten the screws. Are you with me?
What is a metric? The dictionary defines it as “a standard for measuring or evaluating something; basis for assessment.” Avinash Kaushik states “a metric is a number.” To be more specific, metrics are expressed in numerical values. 4,563 is not a metric, but the value of a metric (e.g., 4,563 could be the number of leads in a month). The Web Analytics Association (WAA) further clarifies that there are two types of metrics: counts (e.g., 125,909 visits) and ratios (e.g., 2.1% conversion rate).
What is a dimension? WAA defines it as “a component or category of data. Metrics (counts and ratios) are measured across dimensions.” Dimensions can be a variety of attributes such as search engine, country, page, referring domain, date/time, keyword, etc. Dimensions are expressed as textual values. For example, California, Utah, Virginia, and New York would be textual values of the US States dimension.
What is a report? In terms of web analytics tools, a report is a collection of data or values for a specific set of dimensions and metrics. Every report has at least one dimension and one metric. The data can be presented in graphical or tabular format and most likely a combination of both formats. A report is really where everything comes together.
You may be wondering about trended metric reports such as a Page Views or Visits report, and whether or not these reports have a dimension. In these cases, time itself is the data dimension (e.g., June 1, June 2, etc.).
Let’s look at a scenario where you had 5,500 submitted leads in June. 5,500 is the value of a metric (Submitted Leads) that you’re capturing with a custom event. In many cases, metrics by themselves may not be that insightful.
The data frequently becomes more interesting when we add dimensions. Data dimensions break out or allocate metrics across different textual values or categories. For example, if we added the dimension of “US State” (captured in an eVar) we’d be able to see how many leads we had per state. It may be more informative and useful knowing almost half of your submitted leads are coming from Virginia than knowing the total number of leads in a month.
It’s important to understand and be clear on these basic aspects of web analytics data, especially when people are frequently confusing the terms. A little more rigor around what we define as metrics will ensure that not only our web analytics focus is sound, but we’re also better able to navigate the still relatively uncharted waters of social analytics. In my next post, I’ll discuss some specific ways in which we can raise our standards for metrics.