“How come the %Exit metric doesn’t say 100%? Everyone has to leave a website at some point, right?”
Measuring how visitors interact with your website – and how they consume your website – is the critical way in which you can refine and improve your website to provide an increased level of visitor satisfaction, and a higher profit margin.
Entrances, bounces, and exits are three foundational, bedrock metrics that Google Analytics uses in many different ways to help you measure your website’s effectiveness. As it turns out, there seems to be slight to moderate confusion about what these metrics represent, and what metrics like bounce rate and %exit really mean.
Let’s start by defining the aforementioned three foundational metrics:
Entrances – This is the number of entries by visitors into the pages of your website.
Bounces – This is the number of single-page visits by visitors of your website.
Exits – This is the number of exits from your website.
Pretty clear so far, right? Good. Now, it starts to get slightly trickier. How about we define another metric:
Bounce Rate – This is the percentage of single-page visits to your website. Bounce Rate is calculated by dividing bounces into entrances.
So, when you have a 60% bounce rate, 60% of your entrances left your website on the same page they entered from. In other words, they did not view another page. Also, when you have a 60% bounce rate, Avinash Kaushik’s head explodes. 🙂
Still with me? Great! Now let’s define the last of our metrics:
%Exit – This is the percentage of site exits from your website. %Exit is calculated by dividing exits into page views.
A-ha! You probably already knew or had an inclination toward what the first part of the definition would say, but then reading the second part, you start to see why the %Exit metric isn’t ever equal to 100%.
Now let’s take the final step.
Does everyone have to enter your website to view it? Yes. Does everyone have to leave your site at some point? Yes, they do. So, while the %Exit metric isn’t going to be 100%, the number of total exits should equal the number of total entrances.
A few images will help you understand everything you’ve been reading so far. I created a custom report in the new Google Analytics platform showing entrances, exits, page views, %exit, entrances / page views, bounces and bounce rate metrics. I also have the page as the dimension for my custom report. This first image shows the scorecard, which tallies up the totals for all metrics for the dimension that I have chosen:
As I expected, the number of total exits equals the number of total entrances (everyone has to come in to some page and everyone has to leave at some point). But the %Exit metric reads 58.92%, not 100% as you would initially think. So, I threw in the page views metric to give you some clarity as to how Google Analytics is calculating %Exit. 13,467 exits divided into 22,857 page views, all multiplied by 100% will give you 58.918%, which is rounded up to 58.92%.
However, viewing the very first line item of my custom report shows that the number exits does not equal the number of entrances for any given page, because not everyone leaves your website on the same page that they entered it from:
Here, the %Exit metric makes a little bit more sense, as it is not tallying up page views for all viewed pages on the site – only the /index.php page. 43.91% of all page views on the /index.php page resulted in an exit from the website from this particular page.
I hope that you were able to obtain some clarification and some deeper understanding of how some of the common metrics that you see in Google Analytics are tabulated and used by Google. Remember to always know what data you’re looking at – that is, get to be familiar with the way metrics are computed in Google Analytics (or, your web analytics platform of choice), as it will help you glean those insights that we all strive for!
Getting lost in the shuffle of recent great announcements in the analytics community is how the new Google Analytics platform is bringing to the forefront the very hierarchy that Google Analytics is governed by.
This post is neither intended as a criticism or a “fan-boy” propaganda about Google Analytics. It is intended to make you aware of how the new platform differs from the old / current platform from an organizational standpoint, and to give you some food for thought as you enjoy the new version of Google Analytics.
1. The Web Property
The web property is the hierarchical level of organization between an account and a profile in Google Analytics. It has always been a part of Google Analytics, old and new, but the new platform has been constructed in a way that has increased the significance of the web property and how you access your data.
In the old / current platform, the web property was represented by the light gray bar that separated profiles using different domains. Most accounts do not have profiles using different domains, but we see a good amount of accounts that do. This is what it looks like in the old / current platform:
In the old / current platform, one could not edit what appeared as the web property, but you could see that the Google Analytics account number (the “UA” number) was slightly different for each web property, and that the profiles were organized into each web property specifically.
When your account’s administrator created a profile using a new domain, they were essentially creating a new web property and a profile within that new web property. When they created a profile using an existing domain, they were simply creating a new profile within an existing web property.
In the old / current platform, that minor yet important differential was not as clear as it is today in the new platform. When you log-in today to the new version of Google Analytics, you have two different ways to search for profiles within your account. The first is the traditional approach, similar to the current platform where profiles are listed in a table underneath the respective web properties. That is accessible via the home button on the top-left of the new version:
The second way places the web property into focus by forcing you to click on a web property to access the profiles within them. This is accessible via the “star-like” icon on the top-right of the new platform:
2. URL structure
The URL structure from the old / current platform has changed in the new platform, and if you’re quick on the pick-up, you will notice a few valuable pieces of information in both URLs. When we say “URL”, we mean the URL that appears in your browser’s address bar when viewing the listing of your Google Analytics profiles.
Old URL example:
New URL example:
In the old URL example, the “scid” parameter at the end is your account’s “UA” number. In the new URL example, that long string of numbers and letters at the end of the URL is your account number, the web property ID number, and the profile ID number:
Account Number: a12345678
Web Property ID number: w987658
Profile ID number: p012987012
This data is actually quite critical for anyone using the Google Analytics API to call up and retrieve data directly from Google’s servers.
3. The Value of a Profile
With the way in which the new Google Analytics platform allows you to easily search, filter, and segment your web analytics data, the value of a filtered profile changes. This doesn’t mean that profiles aren’t valuable to have any longer – they most definitely serve extremely important purposes, which we have blogged about in the past.
For example, switching between profiles in the old Google Analytics platform meant starting from the dashboard while looking at the last 30 days of data (essentially, starting from scratch), while in the new platform, your date-range, report location, and viewing options make the smooth transition between profiles as you would expect them to.
However, advanced segments, in-report searches, and dimensions have become easier to apply and easier to manage in the new platform. With a few clicks of your mouse, you can bring up the specific data within a single profile, without even having to toggle between profiles. For example, you no longer have to be forced to use the “All Visits” advanced segment in conjunction with other advanced segments – you can apply up to four advanced segments at any one time where none of them are the previously very sticky “All Visits” segment.
And, from personal experience up to this point, I have found myself switching between profiles less, and interacting with dimensions and segments more.
Hopefully, you learned something new today about how Google Analytics is organized and how it works. Education, via our MoreVisibility blogs is why we’re here!
Website experimentation is the most under-rated, under-valued, under-used way to improve your conversions or your sales online. With a small amount of effort or cost, you can multiply your leads and add to your bottom line, while at the same time improving your customer’s level of satisfaction with your brand.
So why is it that most web sites are not actively running an experiment? Check the source code of every web site you visit today – can you find any tags for Google Website Optimizer or SiteCatalyst Test & Target? You may find them once a day, if you’re lucky.
Is the lack of actively running website experiments on the web due to technical challenges? Is it attributed to a lack of education to the masses? Are there simply not enough options available to choose from to run experiments?
The latter of those questions is something I can help you with today, because there has been a lot of buzz in the analytics community about Optimizely – and for good reason. Optimizely is an A/B website experiment tool headed by two ex-Googlers (but not a Google product), and is giving the established website experiment players in the marketplace a run for their money.
Other Optimizely features that you may want to ponder (full feature list on this page of Optimizely’s website):
– Automatic Goal Tracking: No need to configure specific conversion points
– Multiple Goals, Simultaneously: Track more than one conversion for an experiment
– Multi-Browser Testing: Screen-shots of your experiment on the major browser platforms, pre-launch
And, while those features are great, the greatest thing about Optimizely’s service is that you can preview what your experiment will look like, even before you open an account or install tags! In fact, you can mock-up an experiment for any website on the internet! This is why Optimizely will become your next guilt pleasure (online). Which leads me to sharing with you something I’ve wanted to do for a long, long time.
I always wished that Google would insert an “Analytics” link on their top navigation menu on their homepage. This would obviously increase the visibility of the Google Analytics product, while providing an even faster way to access a Google Analytics account. So, I made it happen in my fantasy world through Optimizely’s experiment preview. First, at the bottom of Optimizely’s homepage, I entered in the website’s URL:
Then, I began to work my quick magic. I simply hovered over the row of links on the top-left of Google’s homepage and started editing the text:
Within less than one minute, I finally re-created what I’ve always wanted to see – even if it was in this temporary, intangible capacity:
Is that cool, or what?
(Full disclosure: you should know that Optimizely is not a free service. You can use it for experiments on your web site from as low as $19 / month. However, Optimizely does offer a free 30-day trial).