You can now create up to 20 goals per profile in Google Analytics. I can literally hear the applause at eMetrics :)

In addition to expanding the number of goals Google has expanded the types of goals to include ‘threshold’ goals for pageviews per visit and time on site.

I think we all know the importance of tracking goals, so I’m not going to get too deep into why you should use goals. If you’re not using goals you should start NOW!

Let’s talk about this new feature.

Goal Sets

Goals are now organized into four sets. Each set of goals can contain up to five different goals.

Sets have been introduced as a way to accommodate all the new data in GA. In the report tabs, rather than one goal tab there can be up to four goal tabs in a GA reports.

When creating a goal you can place it in any set as long as there is room. Once you place a goal in a set it’s best to NOT MOVE IT. Google Analytics sees this as a NEW goal and does not move the previously captured conversions to the new goal.

TIP: I like to organize goals by business function i.e. put goals that are related together. For example, if you’re a content site, you might create goals for spending a certain amount of time on site (1 minute, 2 minutes, etc.). I would group these goals in a set all related to time.

Goal Types

In the old days a goal was a pageview that represented the completion of some high value process, like a thank you page. Now goals can be based on actions that have nothing to do with viewing a page. Conversions can be based on how much time a visitor spends on the site or how many pages the visitor views.

Time Based Goals

Time based conversions are triggered after a visitor has spent a certain amount of time on the site. To configure a time based goal enter the hours, minutes and seconds that a visitor must spend on the site before a conversion is counted. Once the visitor reaches that amount of time on the site then a conversion is triggered.

What’s interesting here is that you can create a time based goal if a visit does NOT reach a certain amount of time. If you choose ‘Less Than’ Google Analytics will trigger a goal if a visit does NOT reach a certain length.

Why on earth would you measure this? I like to think of ‘Less Than’ goals as ‘Failure’ metrics. We often define success metrics, like Conversion Rate, but rarely define metrics to measure our failures!

Using failure based metrics really packs a punch when you’re talking to co workers or clients. For example, when you configure a failure goal you can easily measure and say, “Did you know that 97% of our traffic does not spend at least 2 minutes on our site? We suck!”

Abandonment rate is another well know failure metrics.

Time based goals can also be very useful if you’re trying to MINIMIZE the amount of time people spend on your site. For example, if you have a support section on your site you may want to understand what percentage of traffic spends a certain amount of time on your site. Long term you can try to reduce the number of visits that are too long.

How about setting up a goal set for various time intervals and then try to move visitors from one “goal” bucket to the next. 10 minutes, to 7 minutes, to 5 mintues… You guys are bright, you get the idea :)

Remember, time based goals can be affected by creating virtual pageviews and events. Both of these activities send data to Google Analytics and can change how visit length is calculated.

Pageview Based Goals

Another new goal type is pageviews per visit. Like time on site goals this this type of conversion is triggered when a visit exceeds a certain number of pages. I can literally hear all the advertisers clapping out there!

Pageviews goals are set up in the same manner as time based conversions. Just specify a condition (greater than or less than) and the number of pageviews in a visit.

Like time goals, pageview goals can also be affected by virtual pageviews. If you’re creating a lot of data using _trackPageview() you need to understand that this can change your overall goal calculation.

URL Destination Goals

The old standby! ‘Traditional’ goals are now called URL Destination Goals. You can still use a regular expression, head match or exact match to identify a page that represents a goal. This functionality has not changed (you can learn more about goals in this old post.)

Now that we have 20 goals we can easily measure all of those micro conversions (RSS subscription, email signup, reaching product page, downloading white paper… etc, etc, etc).

And yes, you can still use a virtual pageview as a URL Destination goal.

Funnels

Google did spend some time tweaking the interface. The old interface always showed 10 steps in the funnel. Now you can choose the number of fields the funnel form displays. You’re still limited to 10 steps in total. This isn’t such a big deal.

But think about the bigger picture. Do we really need funnels if we have so many goals? With 20 goals we can use a goal to represent each stage in a process, rather than a funnel step? So do we still need funnels?

Yes. Funnels provide a nice visualization of critical processes, so I think they are still relevant. Plus, you need to configure a funnel if you want to measure Abandonment rate, a very nice failure metric that can make people squirm :)

Odds and Ends

A few random thoughts re: new goals:

If you’ve been creating lots of profiles for goals you may want to consider consolidating all goals to a single profile. The benefit is you can have all your conversions in one interface. No more messing with multiple browser tabs and adjusting the date range.

If you need to control the access to certain goals, you may need to create a profile for certain goals and then give only the people who need access to those goals access to the profile.

A visitor can only convert at each goal once per visit. This is the way it’s always been.

And finally, creating new goals will not modify your historical data, only future data. So all those new goals you’re going to create this week will only track from the day your create them onward.

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.

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.

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

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.

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

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:

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.

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!

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.

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?”

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.

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:

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:

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.

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:

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!?

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.

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.

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.

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!

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]

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.

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.

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.

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.

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.

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!

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.epikone.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.

So here it is, an update. I’m not going to re-do the entire post, just a few key points.

Why Do This?

This entire hack configuration is based on one simple idea: to build a more robust view of customers/prospects/leads/whatever-you-call-them. The more information we know about people that we sell to the more we can adjust our marketing. This technique let’s us combine where the visitor came from (referral, campaign, direct, search engine) and how often they’ve been to the site with CRM data.

How It Works

We’re going to extract data from the GA tracking cookies and add it to a lead generation form. The GA data will be stored in hidden form elements that the visitor can not see. When the visitor submits the form the GA will be connected to the other information that the visitor entered into the form (usually name, contact information, etc.).

Here’s the data that we’re going to pull from the GA cookies:

* Current referral information (i.e. where the visitor came from)
* Custom segment value (if it exists)
* How many times the visitor has been to the site

You’re probably wondering why we’re not extracting the data via the GA API. More on that in a minute.

The Code

Here it is, the updated code:

The new code differs from the original code in three ways:

First, the inclusion of ga.js. No explanation needed there.

The second change is the addition of the _uGC() function. This function was in the original urchin.js, and because I don’t include that file I need to include the code in the script.

The function is used to parse pieces of data and return certain parts. For example, I use to to parse the cookies (which are just a string) and return the value of the campaign cookie (__utmz), the custom segment cookie (_utmv) and the visitor identification cookie (__utma).

Finally, I added some new functionality to include this visitor’s number of visits. This data is pulled from the __utma cookie (which identifies the visitor). The last integer in __utma is actually a visit counter. I think it is interesting to understand behavior based on frequency so I added this nugget of data.

If you want to see the code in action try this:

In my previous post I included a reference section that documents the format of the campaign cookie and the _uGC() function. Check it out if you’re looking for more technical information.

An Alternate Approach

Remember, all the data we’re pulling is in first party cookies. This means that you could extract the info at the server level rather than the browser level (first party cookies are available to server side code).

You don’t need to do this in JavaScript, as I did. You could use ColdFusion, PHP, .NET or some other server side language.

I’m just trying to provide some inspiration here :)

What About the Google Analytics API?

I’m sure there are a lot of people out there wondering about the GA API and why I don’t just use it to pull data out from GA. The reason is the data in GA is all anonymous, aggregated data. There’s no way to reach into GA and say, “tell me where John Doe came from.” That’s why we pull data from the cookie, because we can combine it with a visitor’s info when they supply it.

Sure, it is possible to pull out GA data and place it in a CRM (like traffic from an email campaign, bounce rate from paid search, etc.) but it’s tough to identify individuals in that data.

Is it impossible? No, not from a technical standpoint. You would need to place a unique identifier in GA to link the data.

But we’re getting into a risky area here.

Remember, it is against the GA terms of service to capture data in GA that identifies an individual. This could be an email address or database ID number. Not my rule, Google’s, and they will crack down.

I’d be interested to hear people’s thoughts on this. Feel free to comment!

One challenge that many people face when trying to use custom reports for analysis is determining what data relationships they should create in order to do segmentation.

An easy way to get started is to consider what you have control over. I like to segment by advertising parameters that I can change, like time of day. Why? I can make immediate improvements based on the data in these reports. Let’s take a look.

Google Analytics Custom Reports

You’ve all probably seen the custom reports interface. You can drag metrics and dimensions to create your own report. The columns are different metrics (counts, ratios, etc) and the rows are dimensions, which are the attributes of our visitors and the visits they create.

We can also create a drill down functionality by nesting multiple dimensions in the interface.

Creating the Report

Getting back to our example, let’s segment marketing campaigns by the time of day. Remember, I can run my ads at different times during the day using almost all online advertising tools. Nothing new here, this is called day parting.

Here are the settings for the custom report. Nothing crazy, just a column for visits and a column for conversion rate (in this case a conversion is a sale).

The Data

So this custom report will show all our campaigns and if I click on a campaign we’ll see data for the selected campaign at all hours of the day:

Here we can see the traffic and conversion rate for our chosen campaign at different hours of the day. 6.25% conversion rate at 16:00, not bad! [Website conversion rate is 2.1%]

Now check out this data:

I don’t know about you, but I don’t like paying for traffic that does not convert. Here we can see that traffic sucks from 02:00 - 06:00. We may want to curb our spending at these hours and move the spend to a different part of the day.

[I know, some of the traffic may be 'upper funnel' traffic that is just starting the buying process. But we could easily modify this report to drill down to the individual keywords driving to make that determination.]

I could also create a different visualization that starts with the medium/source dimension, then shows the hour of day, and finally campaign name. This report would be useful if we wanted to look ‘across’ all paid traffic, regardless of campaign, but still retain the ability to drill down to the campaign level.

So here we have some really actionable info. If you’re looking to save some money you may want to see how well your time-based advertising performing at different times of the day.