There was much excitement when Google Analytics unveiled its Events metric. This meant web analytics could store several levels of information on a specific action, and associate that information with a unique web visit and visitor. Before that, if you wanted to — let’s say — record a download, you’d need to create a Virtual Page View.
The answer is simple: If you consider sharing to be a goal of your site, you may want to set it as a Google Analytics (GA) Goal. Events, for all of their power, can’t be set as Goals.
Another action that Events are commonly used for is downloading white papers. Events seem perfect for this because you can set and capture a number of variables, such as title. In other words, you can set the Event Label as the title of the paper. But if you want to measure this as a Goal in GA, you’re out of luck.
Events don’t even “talk” to Goals. [This is no longer true – changes to GA allows any event to be used as a Goal – JL] Let’s say you want to generate a report showing how many people who downloaded a white paper remained on the site for three or more minutes. The time on site can be set as a GA Goal, but you can’t easily generate a report showing the percentage of those who downloaded that remained on the site for that time period.
You can do all of this with GA Virtual Pageviews.
My rule of thumb is this: If you need to identify more than one variable with an event (such as identifying various Actions and Labels), and you do not need to correlate these with GA Goals, used Events. For all else, stick with Virtual Pageviews.
How To Track Content Interest Index In GA Using AddThis
Today on Jason Fall’sSocial Media Explorer, I discuss my new favorite data visualization technique — one that I’m starting to move into production with web analytics reports I create for clients. Its official name is the Tree Map, but as I mention in that post, I prefer to call it The Brownie Chart.
Note: Yes, I know. That web address resolves to a real encyclopedia site. The reason I didn’t just make up a domain name is you never know when one will go live with a site. I didn’t want to have someone inform me, two months from now, that my blog is now pointing to a porn or gambling site! Unless Encyclopedia Britannica takes a surprisingly sleazy turn, I think I’m safe.
Here is another example of how the tree map / brownie chart can make web analytics reporting easier to understand:
So what is this mysterious metric? In a nutshell bounce rate measures the percentage of people who come to your website and leave “instantly.”
They’re the one-page visitors. Yes, they might be finding what they were looking for — but more often than not, these people just didn’t dig the neighborhood.
Avinash has refined his description over time. In his recent, truly outstanding book on measuring web traffic, Web Analytics 2.0, he characterizes bounce rates this way: “I came, I saw, I puked.”
Bounces can be reviewed for all traffic to a site, or only for certain important segments — traffic from search engines is a good example. Reporting of bounce rates can also be broken down by page.
The brownie chart becomes particularly handy for this per-page bounce rate reporting. It helps those responsible assess the severity of a site’s problem pages.
You see, you can’t easily be sure that a page with a high bounce rate really is a problem page. Think of it: If nearly everyone ups and leaves when they arrive at a particular page, but that page gets relatively little traffic, there’s no huge emergency. Content management resources are usually scarce, so it’s better to keep looking, for other pages that attract more page views that happen to have comparatively high bounce rates. It’s those more popular pages that require immediate first aid!
To illustrate, take a look at Everything Brownies’ bounce rates on this brownie chart. The graphic shows all major pages of this fictitious site, and shows the pages as more red if they have the highest bounce rates relative to the others. You should know that size represents the relative numbers of page views. The bigger the “brownie piece,” the more views that page gets.
What does this chart tell us? Quite a bit.
The Holiday Brownie Baking Kit, which I placed my mouse over in this screen capture, has an excellent (i.e., low) bounce rate. It also has a ton of page views.
That means this page is doing quite well in keeping visitors from leaving immediately. Well done! On the other hand, Deluxe Baking Pan is not nearly as successful. Its relative bounce rate is quite high, and because it has the most page views of the entire site, it’s clear this page is majorly dropping the ball!
There are plenty more insights, but you get the picture.
As I mentioned on Jason’s blog, what I like about this charting format is non-math types (such as myself!) can understand these statistics immediately, and know exactly what needs to be investigated further — and in what order of priority. As my friend Bob likes to say, “That’s good stuff!”
I hope you find the potential of this charting technique as exciting at I do.
A Round of Applause for BeGraphic and Sparklines for Excel
This example of a fake report for EB.com, as well as the one on Social Media Explorer, was produced using an “Add-on” for Excel called BeGraphic. The Add-on consists of a whole suite of graphic tools — all based on Excel data and rendered within that application. The particular functions I used were part of Sparklines for Excel within the BeGraphic suite. I urge you to support the folks behind these amazing visualization tools.
That’s why this post includes a little more information on exactly where the CII comes in handy and how it appears to be unique in doing some important work.
In a web visit, Attention is measured by Page Views. Each view is evidence of a page commanding user attention. It’s an opportunity to draw the prospect deeper into the sales process. By the way, a “sale” in this instance is defined as any commitment including but in no means limited to the following:
Setting an appointment
Requesting more information
Filling out a request for quote (RFQ)
Subscribing to an email newsletter
Interest is the “missing link,” in terms of what can be quantified within a web visit. Some attempts at measuring this are commonly called engagement metrics. They’re limited in when they can be used. An example of an engagement metric is counting how many people view an embedded video, or tracking how long they stay and watch. Other metrics, such as bounce rates, are better at gauging interest at a session level, not a page level. Content Interest Index (CII) is an attempt to measure interest by tracking a number of user behaviors.
Desire, defined in this instance, is activity that demonstrates strong buying interest and a high probability to convert. It is the water circling the drain, or — to use a more web-specific metaphor — descent into a conversion funnel. In both online conversions and face-to-face selling, this stage is the answering of objections and a clarification of the real costs and benefits of the commitment. Conversion funnels are fairly easy to configure to track these behaviors.
Action is the fourth and final stage of a sale or conversion, and is as easy to quantify using conventional web metrics as the desire stage. It is the actual transaction. In most analytics systems, conversions are measured when a confirmation page loads, such as a “Thank you for requesting an appointment,” or “… for inquiring,” etc.
Using Content Interest Index to improve content
The primary use of CII is as a content management coaching tool. This is an extremely new metric, but promises to provide insights into raising the “interest quotient” of pages surrounding conversion funnels. In attracting more interest — especially via social media sharing — pages optimized through CII appear to be better at actually feeding visitors into important conversion funnels.
By finally allowing digital marketers to measure every link in the chain to conversion, content interest index has the potential of increasing the number of people entering conversion funnels, and thereby improving the conversion success of the entire web site.
The first marketing class I ever had in college taught me the AIDA model of advertising. It’s still used today, lo these many years later. The AIDA model goes like this: Once you attract Attention, you must generate Interest, create Desire, and enable your market to take Action. Do all of those things and you’re golden.
Back then the only way you could actually measure any of these (except for the last A, which was making a sale) was by employing expensive and time-consuming research. The web changed all that. It allows us to measure each of these steps — except for that pesky thing called Interest.
Until now, the interest that people exhibit in a site’s content has been impossible to accurately measure through current techniques. What has changed? I and my team have come up with a workable solution — a simple way to plug the hole in that famous AIDA acronym.
We’ve found a way to reliably measure web visitor interest.
That’s a bold statement, but here’s why I make that assertion. After analyzing a critical mass of data from the site of one of our largest clients, I can say with confidence that we have a metric that is the rarest of creatures. It is a measurement that arms content managers with real, actionable feedback about the changes they’ve made to web content over which they have responsibility.The metric is called CII: Content Interest Index. I’ve been wrong more than once in my career. Sometimes I’ve been wrong in spectacular ways. But I firmly believe from what I’ve witnessed that this is a unique and valuable tool for managers of many types of sites.
The CII is most valuable for those who manage large amounts of product and service information, delivered over a site with a content management system (CMS) handling a ton of impressions. Volume is really key. The biggest constraint of this metric is it requires high levels of traffic to the pages being measured.
The graphic above is a sample from an analysis for a client — who will remain anonymous — who manages that critical mass of traffic I was referring to, and has provided two year’s worth of data to analyze. In the graphic, the CII illustrates to a single content manager how five product pages are doing, this year compared to last. Specifically, it shows that for two of the pages, CII has dropped in real numbers, while in all five cases page views have risen.
These CII comparisons help a content manager optimize pages using real feedback provided directly by the user (through the measurement of two behaviors, as described below). In the hands of the right content manager, CIIs for the pages managed should progressively climb, as a positive feedback loop continues to reward well-targeted content. It’s the equivalent of a public speaker talking to real audiences, and getting real applause (and real yawns), as opposed to merely guessing at what people want to hear by speaking to an empty room.
The CII counts instances of a page’s “Printer Friendly Format” or “Email a Colleague” icon receiving a click. This observes what are arguably the two most common ways that visitors save or share information – either through printing or emailing content. To factor out a page’s level of overall readership, the sum of clicks is divided by page views.
Page views still come into the equation of analyzing CII, and they are excellent measures of the first A of a site’s AIDA. In other words, the number of people who view a page can be a proxy for the amount of attention you have to play with for that page.
As the white paper explains, the D of AIDA can also be measured using existing web metrics, because desire is exhibited as prospects circle your product or service more closely — and more often — investigating things like delivery options, pricing variables and means of payment. And, as with the “real” world, the online marketplace has always measured the A — as in action, well. This is a transaction.
That leaves interest as something without a good yardstick. Until now.
Check out my white paper and feel free to adopt the system on your own site.
I’d like to extend a special thank you to all of my marketing and technology friends and colleagues who have helped by commenting on drafts of this white paper. Your help has been invaluable.
Marketing Technology Musings and Tips by Jeff Larche