Okay, I knew I couldn’t go three posts without talking about Google Analytics in some way, shape, or form. So, I’m not going to fight it – let’s talk about the four main “views” available in regular reports in Google Analytics. The names of the views aren’t official, but they’re what I like to call them.
Toward the right-hand side of most every report in Google Analytics, you’ll see the Views toggle menu – you can click on any one of the four icons to instantly change the view:
View #1: The Table View
The Table View is the default view for most reports in Google Analytics – so, if you’ve been in GA at all, you should be very familiar with this view. Within all of the reports, Google Analytics emphasizes the importance of displaying several metrics together, so that you aren’t making any snap decisions or quick judgments based upon one individual metric. And, of course, each view comes equipped with Site Usage, Goal Conversion, and when applicable, Ecommerce tabs for your analysis pleasure. In the Table View, you can click on any one of the column headings to sort the table to your liking.
View #2: The Pie Chart View
The Pie Chart is a standard in any business report, and provides a pretty viewing alternative to any data that you need to present. With the Pie Chart view, you can now change the metric that you’re viewing with the drop down on the left-hand side of the report, and change the metric’s contribution to total metric with the drop-down menu on the right-hand side. Play with this for a bit the next time you’re in Google Analytics.
View #3: The Bar Graph View
I love Bar Graphs. Specifically, I love vertical Bar Graphs. This report view gives me all I need in that department. It’s an easy-to-use and easy-to-understand view, with the ability to change the individual performance metric on the right-hand side. Anyone in the world can understand that the bigger the bar, the more / higher / worse the line item is.
View #4: The Comparison to Site Average View
Finally, the most interesting report view in Google Analytics. This shows how each individual line item is performing in comparison to the average of everything on your website combined. This report is great for easily picking out the winners (and the losers) in the group. You will be able to tell which items are bringing your site averages down, and which ones are your rock stars. (Red = Bad, Green = Good…pretty simple I would say). Again, change the metric that you’re using as the comparison with the drop-down menu on the right.
It’s very important that you become comfortable at looking at similar data differently. This will enhance your analysis skills greatly over time, and you’ll be pleasantly surprised how different views can show you different things – from a similar set of data.
One of the things that I always find that people have difficulties with when starting off on their Web Analytics journey is when they discover what’s known as “The Hotel Problem”. Wikipedia’s has a good explanation about it, and also notes its origins.
Unique Visitors for each day in a period of time DO NOT add up to the same total as Unique Visitors for that entire period of time.
Let’s say you kept a daily track of all of the Unique Visitors to your website for every day of this week. Here’s what that would look like:
Sunday: 5 Unique Visitors
Monday: 10 Unique Visitors
Tuesday: 5 Unique Visitors
Wednesday: 10 Unique Visitors
Thursday: 5 Unique Visitors
Friday: 10 Unique Visitors
Saturday: 15 Unique Visitors
So, that’s 5+10+5+10+5+10+15 = 60 Unique Visitors (Don’t be afraid to double-check that math, lol :))
But then let’s say that you go back in your Web Analytics program and look at the Unique Visitors for that same week that you kept track of (So, instead of looking at Unique Visitors only by that single day, you’re now looking at Unique Visitors by that whole entire week). Chances are extremely high that you will see something like this:
Unique Visitors: 37
– “Which number is correct?”
– “My Web Analytics system must be broken…”
Most every Web Analytics program tabulates statistics based upon your date-range. Even though you added up 60 Unique Visitors to your website manually, your Web Analytics system won’t, when showing you the Unique Visitors for that entire week combined. The reason? Because during that week, you probably received repeat unique visitors to your website on separate days, and your Web Analytics program won’t show you those “repeat unique visitors” when looking at that week combined.
The best possible explanation available is thinking about it as a Hotel, hence the term “The Hotel Problem”. Let’s say that I’m a guest at your Hotel. I stay on Monday (1 Unique Visitor on Monday), I stay on Wednesday (1 Unique Visitor on Wednesday), and I also stay on Friday (1 Unique Visitor on Friday). If you add them up manually, that’s 3 Unique Visitors total…but there can only be one of me, so looking at that week as a whole, you’d only see 1 Unique Visitor total.
So, which one is “right”? They both are. But you’ll need to know why you’re looking at your Unique Visitors in the first place, and then go from there.
Again, I recommend checking out the Wikipedia Article on it, as it has a nice table that visually explains it the best.
Greetings, and welcome to the new MoreVisibility Analytics and Site Intelligence Blog! My name is Joe Teixeira, and I’m the Manager of Web Intelligence here at MoreVisibility. I’ll be doing most of the posting here, but from time to time, some of my co-workers and colleagues will join in on the fun.
I was thinking of what my first blog post would be about, and I’ve decided to talk about one of the most common items that is brought up in Web Analytics discussions, especially for newcomers to Web Analytics. Most analytics packages, like Google Analytics, show a metric named “Bounce Rate”, and also show a metric named “Exit %”, or “Exit Percentage”. At first glance, these metrics may look very similar, and you may even interpret them to mean the same thing. However, they are two COMPLETELY separate metrics, calculated two entirely different ways.
First, lets define a “Bounce”. A “Bounce” is a single-page visit to your website. For example, John lands on your homepage, www.xyz.com, and leaves your site without visiting any other pages on your website – that’s a “Bounce”. The “Bounce Rate” is calculated by taking the total number of Bounces (to your website or a set of pages, depending on what you’re looking at), and dividing it by the total number of Visits (to your website or a set of pages, depending on what you’re looking at).
The Exit Percentage is calculated by taking the total number of Exits, and dividing them by the total number of Pageviews (Not Visits – Pageviews). The Exit Percentage doesn’t care whether or not any of the Pageviews were from visitors who viewed 1 page, or viewed 1,000 pages – it simply does the math, and prints it in your Web Analytics interface.
Usually after explaining this difference, the follow-up questions that I usually get are “So, which one do I look at? / Which one do I use?”
I have a pretty simple rule: “Never make any analysis based off of one metric or one statistic”. So, you should never think of “looking” or “using” one individual metric to make any kind of decisions – you should always look at the complete picture of your website’s data, and then go from there. However, every rule has an exception, and this one is no different. If there was any metric in Web Analytics that you could make a very strong argument for using by itself, without the support of any other metrics, it would be the Bounce Rate. Think about what the Bounce Rate is calculating – it’s calculating the percentage of visits to your website who viewed one page on your website, and then left. If the visitors to your website were engaged and reacted positively to your website when they first landed on it, wouldn’t you think they would at least visit another page on your site, instead of leaving it entirely? Especially if you are looking for people to interact and visit the other pages on your site?
With Exit Percentage, all you can say is “This Percentage of Exits happened from this page / this set of pages”, without separating visits that Bounced from visits that did not Bounce. It’s impossible to draw any conclusions or formulate any hypotheses from this, as you can from the Bounce Rate metric. Also, keep in mind that, at some point in time, a visitor to your website ultimately has to leave your website (unless they are some sort of android that can stay awake and on a website infinitely).
So, I would recommend for you to pay close attention to your Bounce Rate – especially if you have a website featuring multiple pages (not counting blogs or single informational pages). Of course, the lower the Bounce Rate, the better, and the more engaged visitors are with your website. I can’t give you any kind of solid figure or benchmark on what your Bounce Rate should look like, but chances are that if 2 out of every 3 visits to your website are Bouncing, you may have a big problem that requires your immediate attention. If 1 out of three (or less) are Bouncing, chances are probably pretty good that you’re doing something right.