Practical Analytics by DigiKnow

Google Analytics: Accounts, Domains, and Profiles (oh my)

Over the past week, I’ve had several clients ask me about the best way to configure their overall Google Analytics setup to cover multiple sites and domains.  I wanted to take a few minutes today to outline the options, what factors determine their use and how to setup the right solution.

Within Google Analytics there are three tiers for grouping your data: Accounts, Domains and Profiles.

Accounts – analytics accounts are the top-level container for all other Google Analytics data.  For the most part, unless you are an agency, like DigiKnow, you will only have access to one account.  Account = Company.

Domains – a domain is generally an individual website and more specifically is a unique Google Analytics tracking code.  For the most part every domain or sub-domain your company has, will be its own domain in Google Analytics. Domain = Website.

Profiles – a profile is an individual view on a domain’s data.  This one is a little more tricky.  For the most part, if you don’t have someone on staff or your agency that is intimately knowledgeable about Google Analytics, you will just have one profile that has all your data.  There are nearly infinite ways to create views on your data and I’ll cover a few of them in a minute.

One of the big challenges when you have multiple websites is whether it is better to setup multiple domains or multiple profiles within a single domain.  Generally, I recommend creating a unique GA domain for each website you have.  The one exception (which I ran into this week) was where it was technically difficult to use a different tracking code on each sub-domain.  My client didn’t have an easy way to put a unique code in each footer, so having a single domain split into profiles for each sub-domain was the better solution.

So how do you use profiles effectively?  There are some best practices, but ultimately it comes down to business needs.  For everyone, I recommend two profiles be created right from the start.  The first is an unfiltered profile that has all the collected data and the second is a profile that has your own traffic removed via IP filtering.  This lets you separate out what you are doing from what “real” customers are doing, but still gives you access to everything should you need it.  From there, the possibilities are nearly endless, but let me give some examples of what we’ve done.

• For a local public institution, we setup profiles with and without visitors on the local wifi and publicly available computers.  This let us see how visitors used the website differently in the building, the workplace or at home.
• For a national CPG company, we setup a profile to treat a section of the site as it’s own website.  This let us see the interactions with the site in a narrow view. Data like page views, bounce rate and referring sites give a greater accuracy than with an advanced segment.
• For a state university, profiles were setup for each of the individual colleges to allow access only to data for their area of the site. 

Profiles and filters can create a near infinite views on your site data.  Hopefully this serves as a good primer for the setup of a set of domains and profiles within Google Analytics.  I’ll continue to expand on this in the future.

Questions from the field

DigiKnow continues to offer our analytics seminars that we started late last year.  Since we just had our latest one yesterday, I figured I’d take a few minutes today and blog about the questions that came from our attendees.

Question 1: We moved from AWStats to Google Analytics and our data doesn’t line up to compare year-over year.  Can you explain this?

Matching data between various analytics systems is always a tricky thing.  Moving from a log based system (AWStats) to a tagging based system (Google Analytics) is even more confusing.  Log based systems track every interaction on the website, including image views, PDF downloads, etc.  Tagging based systems only track page views, unless specific code is in place to track downloads.  If one system is considering PDFs as page views and the other is ignoring them, you will have a discrepancy in your data. 

A major area of discrepancy for this attendee was in their traffic sources.  Although I don’t remember the exact numbers, AWStats was reporting something like 30% search, 30% direct and 40% referrals whereas Google Analytics was reporting 60% search, 20% direct and 20% referrals and 2% other (I realized these don’t add up).  This is significantly different and as it turned out was due to two main factors: First, AWStats was reporting alternative domain names (ex: site is xyz123.com but abc987.com also gets you to the site) as referring sites, dramatically boosting the amount of traffic from referrals (I think it was 15K vs. 5K between systems).  Secondly, Google Analytics was tracking banner and paid search campaigns in the “Other” category, whereas AWStats was putting that either within direct traffic, referral or search.

 

Question 2: On our Ecommerce site, my development team said we can’t use Google Analytics because of the way the pages are structured.  Is that true?

This one is a little less obvious since we weren’t looking at the attendee's website, but given what she was describing, it sounded like they were able to get the data into Google Analytics, but it wasn’t displaying anything useful.  I’m speculating that, because this was Ecommerce, they were including a transaction ID value in the URL string, so that each visitor’s pages were unique.  Visitor #1’s visit to www.xyz123.com/product?id=4564&transid=1 and Visitor #2’s visit to the same page looked like www.xyz123.com/product?id=4564&transid=2.  They are the same page, but to Google Analytics, looked completely different.  The solution I suggested was to apply a Google Analytics filter to the profile that would strip off the Transaction ID (“transid” in my example) and then the two pages would look the same.

Question 3: To install a tagging based analytics packages, to I have to edit every page on the website?

This one is a little easier and the answer is yes.  But what marketers not familiar with HTML and server side scripting don’t know, is how includes work and how easy that can make update every page on the website.  An include is a sub-set of the full page HTML that is stored in a separate file for reference by a page.  A single include is often used as a footer for the entire website, so a single update can change the entire site, making adding a tracking script just a few minutes of work. 

 

If you have any questions that you’d like to see answered on this blog, please email analytics@digiknow.com and we’d be glad to help.  If you are in the Cleveland area and want to attend one of our seminars, please feel free to email about that too.

Back to Basics: Segmentation

Although I've made several posts about segmentation, I thought it would be good to take a step back and talk about what segmentation is and why it is one of the most powerful tools in the analytics tool kit.  Segmentation is the ability to look at just a portion of your traffic, based on some criterion such as geography, site action, time of day, or some other custom variable you set.  The power from segmentation (also known as filtering) is by comparing the patterns of one segment of your users versus the activities of another.  Separating out these groups helps you see how each is performing and try to isolate opportunities.

Let me provide a concrete example.  For a client, we are running a banner and paid search campaign to drive traffic to the site.  The campaign traffic currently accounts for less than 5% of the total site traffic, so there aren’t any dramatic changes in the overall site traffic usage patterns.  Knowing that, I wanted to see if despite being low volume, whether the campaign driving high value.  By creating two segments, one for banners and one for paid search, I could see not only the basics of page views, conversion, etc. (available elsewhere in Google Analytics, in this case) but I could also see what specific pages received the most traffic, the city/state where visitors were coming from, and the browser/OS used to access the site.  With this information, I got an idea of the type of visitors coming in from each of the campaigns and can put more money in the right place next month. 

Segments are powerful.  Segments let you slice your site data down to a single user and see what he/she did.  Scary powerful.  But how often do you really need or want to do that.  If you use Google Analytics, within Advanced Segmentation (their name for this), there are several predefined segments: New/Returning Visitors, Paid/Non-Paid Search, Search/Referral/Direct Traffic, Visits with Conversions, Visits from iPhones, Non-Bounce Visits.  These are all great and if you do nothing beyond using these, you are already ahead of many people. 

But of course, I would love everyone to take this further. Here are some other general segments that I would suggest (with links to Google Analytics examples):

• Completed a specific site goal – if you have goals setup in GA (I hope you do!) then you can use the goal as a segmentation point.  If you aren’t using goals, visitors that reach a specific page can be considered a goal.
  • Example: Complete Goal #1 segment
• Mobile visits – For the purposes of segmentation, I consider anything smaller than 640x480 to be mobile.
  • Example: Mobile segment
• Campaign visits (general or specific campaign) – this can be setup using a campaign name, campaign medium or many other dimensions.
  • Example: All campaign traffic segment; Banner campaign traffic segment
• Visits from or excluding a specific geography – this could be visitors specifically in the US or only international visits; maybe visitors from Ohio vs. visitors from Pennsylvania
  • Example: US visits segment; non-US visits segment
• Visits within a time period – knowing what visitors are doing within or outside of business hours can be powerful, especially if your site has a clear pattern of more weekday traffic than weekend.
  • Example: Visits outside of business hours (all days)
• Internal website visits – it is sometimes helpful to know what your own company is doing on your website.  The easiest way to track this is by Service Provider in Google Analytics if your company shows as its own service provider.  If not, this is tricky, but can be done through custom code.
  • Example: DigiKnow internal traffic segment (this is only useful as a model/pattern)

The easiest way to learn how to use segments it to dive right in.  If you use Google Analytics, the examples above will get you started.  Any time you dig into analytics, the best approach is to have a goal in mind.  If you don’t know what you are trying to find out, you can spend a lot of time exploring, but not learn much and won’t find anything actionable.  Rather than try to explain any more here, I wanted to link you to a great video that explains Google Analytics Advanced Segments.

Related Posts About Segmentation 

• Profile-specific Segmentation

• Managing Google Analytics Segments

• Measuring Mobile

More new Google features: Content Advanced Filter and Intelligence

Google continues to release new features and I got access to more of them this week.  I must say it’s always exciting to log into Google Analytics and find a new feature just waiting for me.  That is exactly what happened twice this week.

On Monday, I had been reviewing some client data for a while when I noticed a new link at the bottom of the page that says “Advanced Filter”.  It made me pause and think… was that there before?  It turns out that it wasn’t and I’ve talked with others that said they found it on their profiles starting last Friday.  At first, I wasn’t sure how useful this new feature was because you can already do a lot just with the current page filter with “containing” or “excluding”.  (If you aren’t aware, you can even put regular expressions in that box to make it more powerful).  So for the last two days, I though, "why did they add that”, then today, I found a perfect case for it.

One frustration with a large list of data, like all the content pages on a site, is that when you sort it, you don’t always get something useful back.  For example, I was looking for the pages with the highest bounce rates.  Before this week, if I sorted by bounce rate, it would put a page at the top with 100% bounce, but it was a page that was only viewed one time.  By using the Advanced Filter, I could limit the display set to pages with 10,000 views or more, THEN sort by bounce rate.  This gave me the “important” pages with the highest bounce rates.  Of course, you always could export this to Excel and filter/manipulate it there, but this is much faster.

Selecting the Criteria	Filter Applied

 

The other major addition that I got this week was Google Analytics Intelligence BETA (of course).  This has been out for a while, but I just got access to it this week.  Actually I have access to it only in 2 of the 3 accounts I regularly use for analytics.  My main account for DigiKnow still doesn’t have access, so my use of it has been been limited.  The video from Google does a great job explaining it, so I’ll put you in their capable hands.

 

 

So what does this mean for analysts like me?  Am I out of a job?  I hope not and I don’t think so.  This is a great tool to help identify major shifts in what is happening on your site.  To me, the more people that are able to see that things ARE changing and ask the all import question “why”, the better off we all will be.  This feature is another feather in Google’s cap and I continue to be amazed that they give all of this to us for free.

I’ll write more about suggestions for Custom Alerts for Intelligence once I’ve played with it a little more.

 

NOTE: I found out that if I give my personal Google user (that has Intelligence access) rights to a Google Analytics Account, then I can see the Intelligence report for the account.  Come on Google, share it with ALL my user accounts.

Google Analytics – Turning an existing source code into a campaign

Measurability is one of the best aspects of today’s digital world.  This is probably most true about online campaigns.  We now have the ability to know which half of our marketing is working.  One of the problems we face is that we have multiple systems tracking campaigns, for some clients, even multiple web analytics packages. 

To get around this challenge, we’ve devised a set of filters to take a generic campaign source code (in this case for HBX) and format it for Google Analytics. 

Google analytics needs three components to make a complete campaign entry: a name, a medium and ad content.  So, our filters take the one piece of data we have, a campaign ad code, and create a properly formed campaign in Google Analytics.

To do this, we need a filter for each campaign field we Google requires:

Campaign Name Filter: Field A: Request URI = (.*)hbx=(.*) Field B: (blank) Output: Campaign Name = Unknown Campaign This filters looks for any URLs that contain the HBX source code variable and writes the word “Unknown Campaign” to the campaign name field.
Campaign Medium Filter: Field A: Request URI = (.*)hbx=(.*) Field B: (blank) Output: Campaign Content = Unknown Medium This filters looks for any URLs that contain the HBX source code variable and writes the word “Unknown Medium” to the campaign medium field.
Campaign Content Filter: Field A: Request URI = (.*)hbx=(.*) Field B: (blank) Output: Campaign Content = $A2 This filters writes the HBX source code to the campaign content field.  The $A2 variable represents the second grouping within Field A, in this case the second (.*) content of the URI.

 

This same process will work for any source variable that you want to put into a campaign.  Give it a try and let me know if this works for you.

 

Update: It should be noted that the Request URI matching is assuming that the source code (my hbx=XXXXX) assumes that the code is always the last variable in the query string.  I’ll write a more robust expression and update this again when that is done.

 

Technorati Tags: Google Analytics,filter,regular expression,campaign,source code,HBX,hitbox

Filtering Incoming Data to Improve Accuracy

The best part of digital marketing is the data.  One of the worst parts of digital marketing is… the data.  Any kind of digital analytics collects more data than can possibly be used and often the data coming in is not in the ideal format.  Fortunately, the web analytics tools from Google, Omniture, Webtrends, etc. have gotten much better in the last few years and they do a lot of the heavy lifting of data clean up for us.  But there will always be website specific tweaks that need to be made.

To parse through the mountain of data that comes through analytics, there are two “tools” that we can use to limit the data collected and/or our view of the data: filters and segments.  Filters are server-side tools that limit the data that is loaded into the analytics tools.  They are typically used to remove test traffic or clean-up extraneous data being collected in URLs.  Generally these are only setup by administrators, but with the ease of the interfaces today, anyone can do it.  Filters usually make permanent changes to the recorded data.  Segments on the other hand, are client-side filters that can be setup in a profile and can be turned and off to compare different groups within your visitor audience.  Typical segments include visitors from search, visits that completed a goal or visits from a campaign.  Segments are very powerful analysis tools and I’ll be talking about them a lot more in the future.

Today, I wanted to briefly talk about filtering and some effective filters I use on a regular basis.  My example code is for Google Analytics, but these filters can work on any analytics solution.  Before we talk about the filters themselves, I should probably back up and talk about profiles.  Since filters are destructive (they change the actual stored data), I always recommend creating multiple profiles for a specific domain.  In Google, this is done with the “+ Add new profile” link on the Settings screen.  At a minimum, I create a ‘Full Data’ profile with no filters and then a ‘Clean’ profile with all my basic filters applied.  So with that in place, these are the filters I apply to my ‘Clean’ profile.

1. Filter yourself out One of the most important things to do in your clean profile is to make sure that you (or your agency/partners) are not skewing the data.  The easiest way to prevent this is to setup an IP filter for your company and anyone you regularly work with that aren’t true customers/visitors. You can start by going to www.whatismyip.com and seeing what IP you are displaying to the world.  It may be as simple as loading that into Google or you might have to talk with your IT group if you find that your filter isn’t working.	This filter covers a large range of IP addresses, but yours may be as simple as the example on the page (63\.212\.171\.123)
2. Removing the Query String This filter is not for everyone, and this is another case where I might setup a extra profile, but if your site has a lot of extra data in the URL after the question mark that is making the same page look like multiple pages in analytics, then you might want to try this one. Field A: Request URL = (.*)\?(.*) Output: Request URI = $A1 This splits the page URL into two parts, the file name and the query string, then overwrites the full URL with just the first part (the file name/path)
3. Clean up Email Provider sub-domains If you do any amount of email marketing, you’ll see that in your Referrers to the site, a lot of different domain names all from the same provider (us.mc317.mail.yahoo.com, us.mg3.mail.yahoo.com, us.mg201.mail.yahoo.com, etc.).  These are all Yahoo mail, but they aren’t nicely combined into one line.  I use this simple filter to strip off the extra sub-domain information for the top email providers. Field A: Campaign Source =  (.*)(mail\.yahoo\.com|webmail\.aol\.com|mail\.live\.com) Output: Campaign Source = $A2 This spits the referring URL into two parts, and drops off the sub-sub-domain that varies for each email provider.

 

There are many other filters that I use, but these are some of the basics.  I’ll post additional recommendations on more advanced filters in future posts.

 

Technorati Tags: Google Analytics,filter,query string,regular expression,email service provider,IP address