Segmenting Unique Visitors in Google Analytics

October 20, 2009 By Justin Cutroni 18 Comments

Google Analytics now has the ability to EASILY segment Unique Visitors. Some of you may be yawning, but I can hear many, many people saying, “That’s fuc*ing AWESOME!”

Google Analytics can now segment Unique Visitors.

Unique visitors is a critical metric especially in the advertising industry. But it’s also a really hard metric to measure because it take a lot of data processing power. Why? Because of the way unique visitors are identified and measured.

Google Analytics defines a unique visitor as a cookie. For all you nerds it’s the __utma cookie.

Every time a visitor visits a site GA checks for the cookie. If the cookie exists then GA knows the visitor has been to the site before. If the cookie does not exist GA sets the cookie and increases the unique visitor count.

The challenge is that every time you want to view a report that contains unique visitors GA has to literally count all of those cookies collected to find how many are unique. That’s why there was only one GA report with Unique Visitors (Visitors > Visitor Trending >Absolute Unique Visitors report).

But Google figured out some way to effectively count all of the cookies in real time. Now the unique visitors metric that can be added to any custom report.

You can easily add Unique Visitors to any Google Analytics Custom Report.

If you need to segment unique visitors you can simple create a custom report and include this metric.

Here’s an example. Let’s say you’re running a branding campaign for an upcoming movie. You want to measure how many actual people visit your website. You can create a custom report with the campaign dimension and the Unique Visitor metric.

There is one technical limitation. Google Analytics will sample data when a date range for the custom report contains more than 200,000 visits.

Still, I’m completely amazed that they figured out how to make this happen.

Filed Under: Analysis Tagged With: ,

Google Analytics Custom Variables Overview

October 20, 2009 By Justin Cutroni 24 Comments

Today Google releases Custom Variables (cv for short) in Google Analytics. This is an evolution of the custom segmentation feature. This post is meant to give you an overview of the feature. We’ll discuss how to use it in a later post.

Like Custom Segmentation, custom variables are a flexible way to add more information to Google Analytics. The big difference is that you can create LOTS of custom variables. How many? In theory you can set an infinite number of custom variables. But GA has some internal limits that keep you to 50,000.

What can we use custom variables for? The possibilities are endless:

  • Segmenting members from non-members
  • Segmenting customers from non-customers
  • Tracking all the campaigns a visitor sees prior to converting
  • Content categorization
  • Segmenting visitors based on landing page
  • Visitor segmentation based on demographic info
  • Customer segmentation based on order history

Google Analytics Custom Variables are like data decorations!

As my friend Phil likes to say, custom variables are decorations that you hang on your data. Almost like holiday decorations hanging on a tree! This is a really good analogy that I’ll continue in this post.

There are four critical attributes of a custom variable that we must understand in order to use them.

Name and Value

The easiest attributes to understand are Name and Value. The Name of a custom variable is literally the name you give to the variable. Each variable can have many, many values. For example, you could define a variable named ‘Baseball Team’ and then add the values:

  • Red Sox
  • Yankees
  • Phillies
  • Giants
  • Angels

This is totally different than the old Custom Segmentation feature. With Custom Segmentation you were limited to one variable (ie one Name) that could contain multiple values. Now you can create multiple variables each of which can have multiple values.

You can view all of your variable names in the new Custom Variables report.

Google Analytics Custom Variables Report

It’s important to note that the name of a variable, plus the value for a variable must be less than 64 characters. Why? The data is sent to Google via a request for an image file. The actual length of the request is limited and Google wants to insure that all of the data makes it to the server.

Scope

Google Analytics custom variables depends on the scope of the variable.

The real power of custom variables comes with something called the Scope. Think of scope as the different ‘levels’ of visitor data. When a visitor visits a website Google Analytics collects data at three levels:

  • Pageview level: This is data associated with each page viewed during a visits. Page level data can change from one page to the next.
  • Visit level: This is data associated with the visitor’s entire visit. This data can change from one visit to the next. But visit level data is applied to every page within a visit. This data only exists for the CURRENT visits.
  • Visitor Level: This data is applied to the visitor and every visit and every pageview that the visitor generates. This data persists across all visits that a person creates. How does it persist? Via a cookie.

This means we can set information, ie custom variables, at the page level, the visit level and the visitor level. If we think of custom variables as decorations “hanging” on our data then we could use the following graphic:

GA Custom Variables "hanging" on your data.

So scope is the same as level. Anyone drooling out there?

The ability to control the scope of a custom variable makes this feature extremely flexible. For example, if you want to group all of the content on your site you can add a page level custom variable to every page that identifies the groups that a page belongs to.

If you want to segment visitors by their purchase history you can add visitor level custom variable. The possibilities are truly endless.

Let’s take a look at some of the reporting so you can get a feel for some of the data.

Here’s the Custom Variables report. You’ll notice it looks a lot like the user defined report. This report contains all of the variables that you defined. If you click on a variable you’ll see all of the VALUES for that variable.

So why has google added a scope if we can’t see it in the reports? I’m just going to let you guys speculate. But it’s obviously a critical part of CVs and we should see that data.

Index

The last attribute that we need to discuss is something called the Index. To be honest, it’s really hard to define the index. Basically the index is a technical attribute that helps GA organize all the custom variables on a page.

It’s only used during the implementation, so we’re not going to dig any further in this post.

Speaking of the implementation, you’ve probably noticed that I haven’t talked much about the implementation. To be honest, we’re still playing with CVs. Obviously this data comes from JavaScript. So you have to do some coding to get this data.

But I’m going to hold off on the implementation talk until later. Implementation involves another concept called the Index which is, to be honest, vague and confusing.

Filed Under: Analysis, Tracking Tagged With: , ,

Introducing Google Analytics v4

October 20, 2009 By Justin Cutroni 17 Comments

Google introduced a new version of Google Analytics today, filled with new features to make analysts and marketers drool.

Included in this new release is:

  • New and Improved Goals (20 goals per profile and new ‘threshold’ goals based on pageviews and time on site)
  • New mobile tracking
  • Table Filtering (a way to quickly filter data in a report)
  • Unique Visitor Segmentation (Unique Visitors is now a metric and can be added to custom reports)
  • Multiple custom variables (an evolution of Custom Segmentation, wicked awesome!)
  • Analytics Intelligence (automatic data analysis. And yes, it is as cool as it sounds!)
  • Analytics Alerts (customizable alerts based on your data rules)

NOTE: Links above are to individual posts.

There are also a couple previously announced features that have FINALLY made it into the product, including:

  • Sharing custom reports and advanced custom segments
  • Pivoting data and segmenting with a secondary dimensions

I must say, this release is very cool. Google has listened to users and included some of the most requested features. 20 goals, automatic alerts, more custom variables… We’ve been hearing/making these requests for years!

One thing I think people will ignore is that many of these features represents dramatic improvements in the GA system. While these features have a definite ‘wow’ factor they provide a solid foundation for future enhancements.

If you’ve been reading this blog for a while you know that I usually blast out as many posts as possible to explain the new features. Today is no different.

Because there are some many people blogging about GA, I’m going to be a bit selective and cover the topics that I don’t think will get much attention or that I just really like :)

So click away! Let’s all explore these cool new features together!

Filed Under: About Google Analytics Tagged With: ,

Tracking Zero Result Searches in Google Analytics

September 8, 2009 By Justin Cutroni 25 Comments

I <3 Google Analytics Site Search reports. There’s amazingly actionable data in those reports. But they’re missing one vital piece of information: searches that don’t produce any results.

Why is this important? Don’t you want to know when visitors search and don’t get any results? Zero result searches can help your identify missing content on your site or a problem with your site search engine.

fenway-scoreboard

Many search solutions will provide this information for you. For example, I use Search Meter for WordPress and it shows me which search queries generate zero results. But I thought it would be interesting to add this data to Google Analytics. That way all my site search information would be in one place.

Unfortunately there is no easy way to add this data to GA. You need to do some programming to collect the data. So this post is really meant for those folks with programming resources AND for those developers that maintain GA plugins. Like my buddy Joost, who has a great GA plugin for WordPress.

If you’re interested in the data and analysis, skip to the bottom of this post.

Conceptual Overview

Our goal with this hack is to modify site search data in two ways. First, we’re going to put all search queries with zero results in a category. This will allow us to use the Search Categories report to easily find all the search terms that yielded zero results.

Second, we’ll modify the actual search terms to indicate that a term yielded zero results. This will make it easy to scan a list of all the search terms and identify which generated no results.

Before we get into the implementation, a big THANK YOU to Charles Miller, one of the lead consultants here. He wrote the JavaScript below. Thanks Charles.

Step 1: Identify No Result Search

The first step is to identify a zero results search page. Most websites have the same search results page regardless of the number of results. You need to identify some something that differentiates a zero results search page from a non-zero results search page.

This must be done programatically and is the hardest part of the implementation.

For example, a zero results search page on this blog has the text “No posts found. Try a different search?”

No Posts Found

I can create code (or more specifically, Charles can create code) to look for the text “No posts found. Try a different search?” If the code finds this text in the page then I can identify that the visitor’s search yielded zero results and than I can send the data sent to GA. Here’s the code that I’m using on this blog:

var content = document.getElementById('content');
if (content.innerHTML.search('No posts found.')) {

The code looks for a section of the page called ‘content’ and then searches that section for the phrase ‘No posts found.’. If ‘no posts found.’ is found (oh, the irony!) then we will modify the data sent to GA.

Important! The way you detect a zero result search page may be different. It’s VERY difficult to create an example that will work for everyone. Take this as a conceptual overview.

Step 2: Tweak GA Tracking Code

Once we know what differentiates a zero results search page we can add some code that tweaks the data. Remember, we want to modify the data in two ways: 1. by placing it in a special search category and 2. by modifying the search term to indicate it did not yield any results.

To create the category all we need to do is add an extra query string parameter to the URL.

To manipulate the search term we need to split apart the page URL and then put it back together with the phrase no-results.

Here’s the complete code.

<script type='text/javascript'>
var pageTracker = _gat._getTracker("UA-XXXXXX-1");
var content = document.getElementById('content');
if (content.innerHTML.search('No posts found.')) {
     // These lines get the search data from the URL and
     //  deconstruct the URL into parts
     var sn = "s";
     var sr = new RegExp(sn+"=[^\&]+"),
      p = document.location.pathname,
      s = document.location.search,
      sm = s.match(sr).toString(),
      srs = sm.split("="),
      // The next line is where we add the category and add
      // the phrase no-results to the search term.
      sre = sm.replace(sr,srs[0]+"=no-results:
 "+srs[1]+"&cat=no-results"),
      sf = s.replace(sr,sre);
      // Send the data to Google as a Pageview
      pageTracker._trackPageview(p+sf);
} else {
      // If this is a regular page on the site, use the standard GA code.
      pageTracker._trackPageview();
}
</script>

The code starts with the section that identifies a zero result search page.

Then we deconstruct the URL to identify the search term. Finally we add the category named ‘no_results’ and the phrase ‘no-results’ to the search term.

If the code does NOT find the term ‘No posts found.’ then a pageview is created as normal.

That’s it for the coding part (thank goodness!)

Step 3: Configure Site Search Settings

The last step is to add the new category parameter to the Site Search settings so GA can identify the no-results search category. This is easy, it’s in the profile setting section of Google Analytics.

How to set a search Category parameter in Google Analytics

I also like to set the ‘Strip Query Parameter’ to YES. This removes the category parameter after site search is done processing and normalizes your pageview data.

That’s it for the configuration! We’re cleared for insight-hunting!

Analyzing The Data

When a visitor performs a search that yields zero results the search term will be placed in a category named ‘no_results’. To find this data navigate to the Content>Site Search>Categories Report:

site-search-categories-google-analytics-2

Immediately you’ll be able to see what percentage of your searches yield zero results. Hopefully it’s very low! Want to see if this impacts conversions or revenue? Click the Goals or Ecommerce tab to check the conversion rate:

Zero Result Searches Impact on Website Outcomes

This is a bad picture, but you get the point.

Next you can click on the no-results line in the data and see exactly which search terms yielded zero results.

Search terms that had no results in Google Analytics

This is super-actionable data. Now you know where you may be missing content or if your site search engine might be broken. You should be asking yourself, “Why are there no results for these terms? Is there missing content or is there a problem with my site search engine?”

You’ll also notice that the search terms now have ‘no-results’ in them. This provides a lot of flexibility for view the search data other ways. Example, let’s use the Search Terms report:

Google Analytics site search queries

Here we can see the search terms ranked by searches. What percent of your top 10, 20 or 50 are no-result searches? How is that impacting your bottom line?

This is just the start. You can use other metrics, like %Search Exists to understand if visitors who receive zero results refine their search or exit.

While this is not the easiest thing to configure, I hope you see the value of the data. More so, I hope that all those folks that maintain plugins add this type of feature to their GA plugins. Joost, you listening!?

Filed Under: Analysis, Tips, Tracking Tagged With: ,

How Google Analytics Tracks ‘Bookmark’ Visits

May 20, 2009 By Justin Cutroni 39 Comments

I was recently inspired by a Tweet to write about how GA tracks visitors that use a bookmark to access a site.

Simply put, Google Analytics will attribute a ‘bookmark’ visit to the information in the Google Analytics campaign cookie.

Many people believe that GA tracks bookmark visits as (direct) traffic. Google Analytics does not track bookmark traffic as (direct) traffic unless (direct) is the value in the cookie. Whatever is stored in the campaign cookie becomes the source of the ‘bookmark’ visit.

The cookie is named __utmz, I’ve talked about a few times, in my series on Campaign Tracking and my post on GA – CRM integration. __utmz always stores where the visitor came from (organic search, campaign referral, etc.)

How about a quick video to walk through an example and save me some typing.

Filed Under: About Google Analytics, Campaign Tracking, Tracking

Segmentation Options in Google Analytics

April 21, 2009 By Justin Cutroni 25 Comments

As web analysts we live and die by segmentation. Without the ability to segment traffic we can not isolate which segments are producing and which need improvement.

Google Analytics offers many different ways to segment data. Each has pros and cons but there is always a way to get the data you need…. well, almost always.

There are 6 different ways to segment data in Google Analytics:

1. Using certain reports
2. Dimension drop down
3. Report filters
4. Advanced segmentation
5. Custom reports
6. Profile filters

Bet you didn’t think there were SIX ways to segment data. :)

Using Certain Reports

Ok, you may think this form of segmentation is lame, but it’s not!

Many reports in Google Analytics are segmented by some default piece of information. There’s nothing for you to do.

For example, the Browsers report segments your data based on the different web browsers that visitors use to access your site. Google Analytics automatically identifies this information when collecting visitor data.

Google Analytics Browsers Report

Other segments that are automatically include in Google Analytics include:

* Visitor type (new and returning)
* Geographic information
* Operating system (and many other “nerd” segments)

Marketing segments are not AUTOMATICALLY segmented. You need to configure Google Analytics to track campaigns (i.e. link tagging) in order to get correct traffic source segmentation.

Pros:
* Easy

Cons:
* You better make sure you’ve got your campaigns tagged correctly ;)

Dimension Drop Down

Many reports have a dimension drop down that allows for segmentation right in the report. This is a handy way to quickly drill down into a piece of data.

For example, let’s say I want to see the most popular landing pages in a particular state. I can navigate to the state in the Visitors > Map Overlay > Regions report, click on the state I’m interested in, and then choose Landing Page from the Dimension drop down.

Google Analytics Dimension Drop Down

You can see in the image above that you can segment based on campaign information, some technical information and some visitor information (visitor type, language).

Overall, this is a good way to go when you’re drilling down and want to segment a single data point by some dimension.

Pros:
* Quick for one-off segmentation

Cons:
* Can trigger sampling
* Limited number of dimensions and no metrics
* A pain if you need to segment a lot of things, like top landing pages for every US state

Report Filters

Bet you don’t think of filtering as segmenting, but it is!

Google Analytics report filter

Any report displaying tabular data has a filter tool at the bottom of the data. This let’s you quickly view data that matches, or does not match, some condition. The condition is the pattern, or regular expression, that you enter into the filter. Using a regular expression you can add lists to the filter.

Here’s an example. Suppose I want to quickly view traffic coming from the Pacific sales region. I can apply the following filter to the Visitors > Map Overlay > Regions report:

California|Oregon|Washington

[The above is a regular expression matching California OR Oregon OR Washington]

A filter Map Overlay report in Google Analytics

Notice that the Scorecard (the top row of data in the table) indicates how our segment, i.e. the data that matches our filter, compares to the overall site? We can now compare the Pacific sales region to the entire site.

And here’s a neat trick, if you add the filtered report to your dashboard the filter will persist in your dashboard widget. I call it a sticky filter.

Pros:
* Quick and relatively easy
* Can be applied to historical data
* Will not trigger sampling

Cons:
* Restricted to one report and the data in that report
* You should know some basic regular expressions
* Can not be shared easily

Advanced Segments

There has been a lot of conversation over the last few months about Advanced segments and rightly so. This analysis tool is really powerful and let’s you slice the data many different ways using different dimensions and metrics. Want to see all visits that generated more than $100, coming from paid search and occurring after 8 AM? No problem with an advanced segment.

An Advanced Segment in Google Analytics

But there are some downsides. First, sampling. Because Advanced segments re-process data in real time there is a sampling algorithm applied to minimize the load on Google’s servers.

You can’t segment more than 200k visits. If sampling is applied you’ll see a confidence interval next to your data.

Sampling accuracy in Google Analytics

Again, the problem is that small segments of data will be really inaccurate when the sampling algorithm is applied. There is no way to disable sampling.

The most common ways to get around sampling are segmenting using profile filters or potentially a report filter. It really depends on the exact situation.

The second issue is that not all reports can be segmented. Due to the segmentation technology certain reports can not be segmented, like the Absolute Unique Visitors report and the Funnel visualization report. Those reports can only be segmented with profile filter (see below).

Pros:
* Can be applied to historical data
* LOTS of flexibility, can segment based on a huge number of dimensions and metrics using different combination of both

Cons:
* Sampling will be applied if trying to segment more than 200,000 visits
* Not all reports can be segmented
* Specific to your username, can not be shared with other users

Custom Reports

Another beta feature that can be used for segmentation is the Custom Reporting tool. This tool is more than just pretty reports. It allows you to create 5 levels of segmentation in a report.

In a previous post I talked about segmenting campaigns by time of day to better understand day parting.

We could take that example one step further by adding geographic region to the report. The result would be a report that has Campaigns data that could be segmented by time of day and then by geographic location.

Multiple levels of segmentation in a GA Custom Report

The problem is that not all dimensions can be used together. The reason is that only certain metrics are related in the Google Analytics data architecture. You can find a complete list of combinations in the GA support docs.

Pros:
* 5 levels of segmentation
* Advanced segments can be applied to a custom report
* Can be shared using automated email feature

Cons:
* Can only segment using dimensions, not metrics
* Limited number of dimension combinations
* Can only drill into one data point at a time

Filtered Profiles

Filtered profiles are the nuclear bomb of segmentation. They are permanent, segment every report in a profile, and can easily be shared.

In case you’re not familiar with filtered profiles, you can include and exclude data from a profile using a filter. Google applies the filter during data processing, thus segmenting the data.

Google Analytics profile flters

Once the data has been processed it can never be changed. This means that you can filter historical data AND if you mess up the configuration of a filter you could have really crappy data.

Another issue with filtered profiles is not all data can be filtered. For example, transactional data is different than pageview data. This can cause some funky information in campaign reports and commerce reports.

If you need to filer pageview data then you’ll need to filter your commerce data with different include or exclude filters. Also note that Event data can not be filtered.

But, on the up side, you can use filters to segment things like Absolute Unique Visitors and the Funnel Visualization report. Neither can be done with an Advanced Segment.

Absolute Unique Visitors in Google Analytics

Pros:
* Segmentation of every report in Google Analytics
* Can control access by assigning users to filtered profiles

Cons:
* Only effective from date of implementation forward
* Limited number of dimensions
* Issues with filtering other types of data, like events and transactions

I hope this inspired you to come implement different segments using different techniques. As I said in the beginning, there are a lot of ways to slice data in Google Analytics. Find the technique to suit your needs and start segmenting!

Filed Under: Analysis, Tips, Web Analytics Tagged With: , , ,

Count Me Out: GA.JS Version

April 16, 2009 By Justin Cutroni 28 Comments

A while back I wrote a post called Count Me Out! that explained how to exclude Google Analytics data based on the custom segment value.

My previous post was based on the old, urchin.js tracking code, and a lot of people have been waiting for an update. It’s taken a while, but here it is.

I will mention that my favorite way to exclude traffic from Google Analytics is using an IP exclude filter. An IP based exclude filter is very accurate unless you having a changing IP. The method below works best if you have a dynamic or changing IP address.

Even if you’re not interested in this post, there is a fun ‘group activity’ below. Please try it!

The old version of this hack method required you to add a new page to your website. That page would set the GA custom segment cookie (named __utmv) on your computer.

This technique works fine, but who wants to add a new page to their site? It can be a pain.

I’ve simplified this technique by removing that page. You can enter the JavaScript directly into your browser.

Step 1: Set Custom Segment Cookie

Go to the site that you are tracking with Google Analytics and view a page.

Copy the code below and paste it into the location bar of your browser and click ‘enter’ on your keyboard.

You should see a message that says, “Custom segment has been set. Time to create a filter.”

Here’s a tip, you can bookmark this JS to make it easy to reset the cookie in the future. I set the cookie every time I fire up my browser.

Step 2: Create Exclude Filter

Next, create an exclude filter in Google Analytics to exclude the user defined segment (i.e. the cookie) you just created:

This filter will exclude anyone with a custom segment cookie with a value of ‘remove-me’.

Remember, cookies are specific to a browser and computer. If you use mulitple browsers or multiple computers you need to set the cookie using all the browsers on all the computers you use.

Having Some Fun With This!

You’ve probbaly figured out that you can set the custom segment cookie on anyone’s website as long as they’re using GA. This means that you can add data to their User Defined report. Let’s try this on my site!

Navigate to www.cutroni.com/blog and place the following code in the location bar of your browser after the page has loaded. Change FOO to whatever you want and press enter on your keyword. That’s it. You’re now in my data.

I’ll post some of the more popular and creative values in Twitter and maybe here at a later date.

Please try to keep it clean. I often review data with my 4 year old son :)

Thoughts on the Old Method

The old version of this technique uses a form to set the custom segment cookie which is pretty handy if you have a lot of people in remote locations that need to be excluded from the data. Just send all your coworkers, contractors, etc. a link to the page and ask them to set the cookie on their computer. It’s a little easier than asking them to paste JS into their browser.

If you’re interested in using this technique here is a new version of the page and the process.

Step 1: Create a new page on your site using the code below.

** Note ** The information below is in an iFrame. If you receive this post via email you may not see the contents.

Step 2: Go to the new page you just added, fill out the form, and click the ‘Create Cookie’ button. Keep track of the value you enter into the form, you need it for step 3.

Step 3: Finally, create an exclude filter in Google Analytics to exclude the value that you entered into the form. Remember, you need to use a regular expression for the filter field. So if you entered ‘remove-me’ in the form, enter ‘remove-me’ as the Filter Field.

That’s it. Sorry for the lame post, but I’m trying to update a lot of the old code and posts on the site.

Filed Under: Tips, Tracking Tagged With: , ,
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