The TechWyse team has been writing on this blog for quite some time. We have recognized that one some of the most popular articles we write are the ones that have a month to month following. Currently DJ writes a regular monthly column on Search Engine Market Share.
So following on this lead, I bring you the first in a monthly series — JD’s Google Analytics Tip of The Month!
This is the 1st in my series of monthly tips to help Google Analytics users leverage this powerful tool. For this 1st post I think its important to address the most very basic step in ensuring your Analytics data. This months tip is…
How To Ensure Data Accuracy…
Google Analytics is not a difficult tool to setup. It takes a modicum of intelligence, a belly full of curiosity and a dab of technical nous and hey presto your GA profile is recording data. If, however, you don’t make the following configuration tweaks and checks then you could be recording practically useless information that will not pattern correctly as you start to analyze and tune your content and campaigns. Here is my list of checks to carry out in maximizing data quality.
Step 1 – Verify your code is on EVERY page.
There are a number of GA code verification tools that will automatically crawl your site and report whether they find the GA code present on a page. Use them! The most popular are www.sitescanga.com and our own tool www.gpablo.com. Both will allow you to check pages and export to a spreadsheet.
The next version of GPablo, due for release imminently will allow creation of accounts, storing of sites and reports for easy rescanning. It also will provide pagerank information for those tracking SEM performance. If you would like to be included in our beta test on the new version comment below and we’ll set you up!
Both tools will provide reporting on which pages the code is missing from and you should ensure that every page has been tagged to prevent the visits being reported as having left the site and coming back as a referrer.
Step 2 – Filter out ALL non-relevant traffic.
Site managers and owners love to visit their own site. Most have it bookmarked on their desktops, some set it as a homepage. Also, your developers will be hitting the site regularly to perform updates, and your conversion experts of course, will be regularly visiting to assess analytics results and check usability! 😉
It is vitally important to filter out this non-relevant traffic, otherwise you will be reporting on data that operates under a different pattern, and rarely converts. Examples of data skewed by internal visitors include, a larger amount of “Direct Traffic”, a higher “Bounce Rate” due to staff opening the site and not interacting with more pages than their entrance page, “Ecommerce Transactions” that are tests. By filtering out this traffic you achieve a better picture of the behaviour of true visitors looking for your services.
We have a couple of articles to assist in how to setup IP filters and advanced IP filter setup. Be careful to filter the entire range of IP addresses that your ISP might be providing to you because they will regularly change it on you! If you are unable to filter a narrow range of IP addresses available through you, and someone like Bell gives you a large range then you can exclude visitors by having internal staff initially visit a hidden page.
Brian Clifton has an elegant solution in his Advanced Web Metrics book, a bible for anyone interested in Google Analytics. He uses a user defined variable to segment the internal traffic out. This user defined variable is planted, again, by a hidden page visit.
Step 3 – Ensure that any cross domain traffic is accurately reported
When you jump from one domain to another then Google Analytics will report this visitor as referring traffic, effectively losing the previous visitor path that has brought the visitor to the site. This is very important for tracking conversion. If your cross domain traffic has converted, then the credit for this conversion will go to the referring domain. By modifying the GA code (as described in the GA support area) then you will properly attribute the credit to the latest method the visitor found your site.
Here’s an example… www.mysite.us forwards all traffic to www.mysite.com, which has a shopping cart. You have setup ecommerce tracking on the cart so that transactions can be reported on. A search visitor that finds www.mysite.com through a google search for “BABY TOYS” and then checks out in this visit. Alanytics will attribute the transaction credit to the google/organic source and also attribute the purchase to the “BABY TOYS” keyword.
If the visitor finds the www.mysite.us via the same search, is then forwarded to the www.mysite.com and performs the same transaction, then the credit for the purchase goes to www.mysite.us/referrer with no keyword recorded. As an analyst this is data that cannot be lost!
Step 4 – Setup GOALS in your profiles
We have written many times about 1) websites being tools for conversion, 2) measuring this conversion and 3) understanding the effectiveness of your marketing effort. Measuring goals allows you to report on this, but from an analysis perspective goals provide a deeper benefit.
When you establish a goal in an analytics profile GA will then link the content and traffic source reporting. The benefit is being able to understand how individual sources/campaigns/search keyword are contributing to goal conversion.
Don’t forget to add a value for the goal to ensure that you receive some value data for comparison!
Check out the following screen capture to see an example of the type of data you can capture by having a goal established in your profiles:
Step 5 – Tag inbound links to aggregate source information
You have the ability to tag inbound links with source, medium campaign (and additional) fields. The purpose of this is to identify and collate traffic sources. Examples of these types of sources include, newsletters, directories and referrers where you have control over the URL, and other PPC campaigns including Adwords.
The benefit is allowing more accurate reporting on source information. For example: some PPC traffic will come in as organic if it is not tagged, referring links have no information on whether they were part of an ad campaign and newsletters (generally one of the most effective sources of converting traffic) will list the source information as individual email referrers. This is valuable data being wasted!
Take the time to tag it properly. Use a predefined naming convention and you will have much more effective data to determine the value of your sources!
Step 6 – If your site has a shopping cart you MUST setup ecommerce tracking!
By setting up tracking of purchases, Google Analytics provides amazing data into your revenue generation. You MUST leverage this information. It will tell you how much revenue your sources, keywords, campaigns and even give you insight into page value using Google’s $index metric.
Here is Google’s step by step on setting up analytics ecommerce tracking.
With ecommerce enabled, your traffic source reports will have another tab, intuitively named “ecommerce”. Check out the info on this baby! The slide below is an example of the revenue information that you can report on for your sources. Note the per visit value metric that is a small step away from projecting changes in budget allocation. More on this in a future post… 😉
Analyzing how advertising campaign adjustments affect revenue is fascinating and one of my favourite pastimes! The standard GA ecommerce reporting provides a great amount of information. You can however, pull various metrics together in custom reports that will show a whole host of influencing factors. A future blog post will be sharing some of these with you.
That’s it for now…
There are of course many more techniques for refining the collection of data and for representing the data in specific ways within your analytics profiles. I will post on some of these tips over time. Feel free to comment or contact our team if you need more information. For now, these steps should get most GA users on the right path to accurate data driven decisions.