Analytics Talk » Tips posts – Analytics Talk

Save Your Ass With Google Analytics Data Alerts

Justin Cutroni — Wed, 04 Jan 2012 15:44:43 +0000

Let’s face it, we’re all a bit “lazy.” I admit it, I don’t check my website data every day. And when I’m on vacation it can be a bigger challenge.

But that’s OK, I use data alerts to stay on top of my data even if I can’t check analytics every day. Are you using alerts?

If you’ve never used Google Analytics alerts they’re fairly simple to set up. You can find them in the Profile Settings.

There are five things that you need to do to set up an alert:

1. Choose a profile to apply the alert to.
2. Select how often you want to monitor the data (daily, weekly or monthly).
3. Identify the segment of traffic you want to monitor (i.e. which group of visitors, like visitors from a particular campaign or geographic region).
4. Choose what metric you want to monitor (Revenue, Visits, Time on Site, etc.).
5. Set a threshold for the alert meaning how much does the metric need to change to activate this alert (ie increase, decrease, etc.).

I divide my alerts into two groups: those that insure I have good data and those that measure the performance of the business. This post deals with the former: alerts that will keep your data in tip-top shape.

Alert #1: 10% Daily Traffic Drop

This first alert is simply there to make sure that I’m collecting data. I’ve noticed, for my site, that my traffic usually never drops more than 10% for a given day when measured week-over-week. So if I see a drop of more than 10% I know that something happened and I need to investigate more.

A 10% traffic drop alert.

Alert #2: Flatline! i.e. No Data

This is a generic alert that identifies when a metric goes to zero. I can’t tell you how many times a client tries to analyze data at the end of a month only to find a data issue. This commonly happens because not every metric is analyzed every day, some metrics are reported monthly.

But I don’t want to wait until the end of the month to find a problem.

So for almost every important metric I will create a daily alert to insure that I’m getting data. Here’s an example alert that monitors an event.

A Google Analytics Alert that monitors an Event

Alert #3: Daily Error Pages

Another metric I like to monitor is error pages, more specifically 404 errors. I want to know if any of my content is generating an error for my readers. So I created a little alert that will alert me when the number of 404 pages is greater than 0.

Monitor 404 pages, and other website errors, with an alert.

Obviously you can change this alert to monitor almost any type of website error, it depends on which errors you are tracking with Google Analytics. The key is that you need to be able to identify your error pages in a Google Analytics report.

As you can see above, my error page has a unique URL (404.html) and a query parameter that identifies the page that was missing. You may need to use a virtual pageview or an event to track your error pages. Once you do you can create an alert.

Alert #4: Non-Domain Traffic aka The Poser Alert

The hostnames report in Google Analytics is a handy little report. It shows the hostname that is in the location bar of the visitor’s browser. On numerous occasions people have ~~ripped off~~ borrowed my content without my permission. They just copied my source code and posted it on their own domain.

I know, classy.

This alert will automatically let me know if my account is getting data from any domain other than my own.

The "Poser" Alert will let you know if someone is poaching your content.

In some cases this alert actually detects something good! I regularly see the hostname translate.googleusercontent.com, which is used when popele use Google Translate to translate the content on my site.

A note from your data therapist

All of these alerts can help. But you’ll still have inaccuracies in your data. The idea is to keep them to a minimum. If you try to eliminate all errors 100% of the time you won’t do anything else. It’a almost impossible. Do the best you can.

If you do have some data issues, and you will, you’ll have to spend some time resolving them when you do your analysis.

Save Your Ass With Google Analytics Data Alerts is a post from: Analytics Talk by Justin Cutroni

Mastering Google Analytics Custom Variables

Justin Cutroni — Wed, 18 May 2011 12:39:43 +0000

I’ve got a stack of posts that I want to write, and realized that the all deal with Custom Variables. So, to make sure that we’re all on the same page when it comes to custom vars, here’s my guide to Mastering Google Analytics Custom Variables.

For those of you that have not used custom variables, CVs are a way for you to insert custom data into Google Analytics. What kid of custom data? Almost any kind of data. As long as the data is available to you in the source code of a web page you can pass it to Google Analytics via a custom variable.

There are 4 parts to a custom variable:

1. The name of the variable
2. The values for each variable
3. The index or slot of the variable and the scope of the variable
4. The scope of the variable

Name & Value

Custom variables are name-value pairs of data. The name is also called the “key”. To create a custom variable you define the name of a custom variable, like Favorite Baseball Team, and then assign it different values, Red Sox, Braves, Giants, etc.

Google Analytics will show you a list of all the custom variable names in a list and then let you drill down into the list and see all of the values.

Here’s an example. I use a custom var named “Year” to track the publication year for all my posts on this blog. I can view which year had the most popular content by finding “Year” in the Visitors > Demographic > Custom Variables report.

Then I can click on “Year” to a get a list of all the values:

Custom variables can also be used in custom reports and advanced segments. The name of the custom variable becomes the “Key” dimension and the value of the custom variable becomes the “Custom Variable Value” dimension.

Index or Slot

The index is a way to organize your custom variables. Index is also referred to as “slot”. At a basic level, you get 5 custom variables. But this can actually be misleading. Think of the index as a parking lot with 5 parking space. You can put or “park” a custom variable in each space.

You can technically have more than 5 custom variables, but we need to discuss the next concept, scope, and how it impacts the index.

Scope

The key to mastering custom variables is understanding the scope. The scope of a custom variables determine how long the custom variable will persist.

If we use the parking lot analogy, the scope determines how long a car will stay parked in the custom variable parking lot.

There are three different scopes for Google Analytics Custom Variables:

Page Level: A page level custom variable will only persist for as the visitor is viewing the page where the custom variable has been set. Once the visitor moves to a new page the custom variable will expire.

Visit Level: A visit level custom variable will persist for the visitor’s entire visit. Once the visit ends the custom variable will expire.

Visitor Level: A visitor level custom variable will persist for 24 months as a cookie on the visitor’s machine until the visitor deleted her cookies.

Where scope really gets complicated is when we use it with the index. Remember, you have 5 slots that can hold a custom variable. Once you place a custom var in a slot that SLOT BECOMES FILLED and you can not place another custom var into the slot.

Scope and index work in tandem to determine how many custom variables you have available at any given time.

Let’s start with a visitor level custom variable.

When you place a visitor level custom var in a slot that slot remains filled until you delete the custom var. Remember a visitor level var is stored as a cookie so you have to actually delete the cookie. So, if you set a visitor scoped custom variable in slot 1 you can NEVER use slot one again. Unless you want to erase the custom var you set in slot 1.

Every time the visitor comes back to your website slot 1 will be filled because there is a cookie (named __utmv) on the visitor machine.

PARKING ANALOGY: There is a car in parking space 1. The car has no tires… and no engine… it’s not going anywhere soon… unless it gets towed.

When you create a visit level custom variable and place it in a slot, that slot remains full for the entire visit. Once the visit ends that slot opens up and you can place another variable in that slot.

Unlike the visitor scope custom variable there is no cookie. So when the visitor comes back to your site that slot is open.

PARKING ANALOGY: There is a car in a parking space 1, but just for the day. The spot will be vacated at the end of the day and someone else can park there.

When you create a page level custom variable and place it in a slot that slot remains filled until the visitor moves to another page. Once the visitor moves to another page the slot opens up and you can place another variable in the slot.

PARKING ANALOGY: There is a car in a parking space 1, but someone is just running into a store. The engine is idling and the spot will open up in 5 minutes.

Let’s look at a couple of examples:

Example 1: A visitor on a news site logs in. I set 5 visitor scoped custom variables. This means that whenever the visitor comes back to the website ALL five slots are filled. I can never use any other custom variables unless I erase or over-write one of the existing variables.

Example 2: A visitor is on a blog. Each blog post is categorized 3 ways using a page level custom variable. on every page I set the same 3 custom variables. When a visitor logs in I set a visitor custom variable to identify them as a member. So now I have 3 page level variables and one visitor level variable. The slot that contains the visitor level variable will always remain full, so I need to make sure that I always place my page level variables in the same slots.

Example 3: A visitor logs into a news site. I set 5 page level custom variables when they log in to record various pieces of information about their membership. One the next page I fire of 5 different custom variables to record information about their past browsing history. In this senario I have actually set 10 custom variables. How? Because they are all page level custom variables. The slots associated with a page level custom var open up when the visitor moves to a new page.

Hopefully this explains how you can technically have more than 5 custom variables.

The Code

Like everything else in Google Analytics, you implement Custom Variables using JavaScript. All of the topics we discussed above, index, name, value and scope, are all part of the JavaScript.

Here’s the standard Async code snippet:

_gaq.push('_setCustomVar',index,'name','value', scope);

The index is a number, from 1 through 5.

The scope is also a number. A value of 1 indicates a visitor scoped custom variable, a value of 2 indicates a visit level custom variable, and a value of 3 indicates a page level custom variable.

The name and the value are just plain text. Whatever you enter into the code above will be pulled into Google Analytics.

Where do you put this code? That’s the hard part. You place this code when you want to create a custom variable. You may want to categorize all of the pages on your site using a page level custom variable. In this case you might place the custom var code in the standard GA code snippet, like this:

Or, if you’re going to identify someone as a ‘member’ of your site, you might place the code on a page after the visitor logs in. Like this:

But you can also attach the code to visitor action, like onClick events.

You can also create custom variables for mobile devices using the iOS SDK and the Android SDK. How cool is that!

And some of the super nerds I work with at Cardinal Path have been able to hack custom variables and set them using the Google Analytics Mobile Tracking code for low-end mobile devices.

Regardless of the platform that you’re tracking, you can probably use a custom variable.

Super Nerd Stuff

Custom variables are not without their limitations and oddities. Here are a few things you need to be aware of when implementing:

1. What happens if you place a custom variable in a slot that is already filled? The last value placed in the slot is applied to the visit.

2. Custom variables are sent to Google using the same invisible image request. In the old days (ie two months ago) the size of this request was limited to 2048 characters by the browser. Because this limitation can really restrict data collection, Google decided to limit the size of your custom variable. This means that the name – value combination must be less than 64 characters. I know, this sucks.

BUT, Google has since changed how it collects data. The size of the image request is now 8000 + characters. So, in theory, Google could kill the 64 character CV limit. But they have not…

~~3. Custom Variable processing lags the standard data processing by about 24 hours. However, if you add a custom variable to a custom report it will be visible. I know, completely weird.~~ This is really only for very, very big sites, where data processing can lag in general.

4. Page level custom variables are more-or-less the same as an event. When you create an Advanced Segment using a page-level custom variable, GA will return the number of visits that included that custom var. So, if you don’t have enough slots available you may want to consider using an event.

5. When you set a custom variable the data is sent immediately back to Google Analytics via an image request. This means that if you set a custom variable, on the last page of a visit and AFTER the pageview is recorded, that custom variable will NOT be collected by Google Analytics. If you can, put your custom variable code BEFORE _trackPageview().

That’s ALL I have to say about custom variables. Hopefully this gives you a good baseline understanding of how they work and how to implement them.

If you’re using custom variables leave a comment. I’d love to hear how you’re using them.

Mastering Google Analytics Custom Variables is a post from: Analytics Talk by Justin Cutroni

Tracking Zero Result Searches in Google Analytics

Justin Cutroni — Wed, 09 Sep 2009 02:53:57 +0000

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.

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.

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!?

Tracking Zero Result Searches in Google Analytics is a post from: Analytics Talk by Justin Cutroni

Segmentation Options in Google Analytics

Justin Cutroni — Wed, 22 Apr 2009 03:58:38 +0000

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.

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!

Segmentation Options in Google Analytics is a post from: Analytics Talk by Justin Cutroni

Count Me Out: GA.JS Version

Justin Cutroni — Thu, 16 Apr 2009 19:28:35 +0000

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.

Count Me Out: GA.JS Version is a post from: Analytics Talk by Justin Cutroni

UPDATED: Integrating Google Analytics with a CRM

Justin Cutroni — Wed, 18 Mar 2009 16:53:47 +0000

A while back I wrote a post about how you can integrate Google Analytics with a CRM system. That posts referenced the old urchin.js code and is in need of an update. Plus a lot of people have been asking me about different integration options and I thought it would be a good time to revisit the topic.

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!

UPDATED: Integrating Google Analytics with a CRM is a post from: Analytics Talk by Justin Cutroni

Adding Business Data to Google Analytics Data

Justin Cutroni — Wed, 29 Oct 2008 04:15:33 +0000

I know the past week has been full of Google Analytics news, but I’m excited to tell you about something one of our team members created: Google Analytics Notes.

For a long time we’ve wanted to add business data to GA to help keep track of marketing activities, industry news and GA configuration changes. These things are critical to know when analyzing data as they add more context and help us understand what’s affecting website performance.

We tried using Google Spreadsheets to store business info but it never worked. People did not take the time to open up a spreadsheet and add information. We figured that adding some type of ‘note’ functionality to GA would be the easiest way to change this behavior. That’s how GA Notes was born.

GA Notes is a Firefox extension that lets you add business data to a profile. Notes appear in a concealable table at the top of every report.

Any GA user who views a profile, and has the Firefox extension, will see the notes entered for the profile. You can add notes, edit notes and delete notes. Notes can also be exported in XML format for archival purposes.

Installation

Installing GA Notes is easy. Just download the following file to your computer:

https://ga-notes.appspot.com/ganotes.xpi

Once downloaded double click on the file. Firefox should open and ask if you want to install the extension. Click install and that’s it. You’re ready to start adding notes to your GA data.

Usage

The extension adds a ‘Show Notes’ button in the GA menu bar. Click on the button to view notes for this profile or to add a note or edit/delete an existing note. It’s not that complicated. :) We wanted to keep this easy and flexible.

How it Works

For those of you that are interested, GA Notes runs on Google’s App Engine. No data is stored on your machine or our servers. It’s stored on Google’s servers. The Firefox extension provides the interface to enter and display data. But all of the processing and data storage happens on App Engine. All data sent to App Engine is encrypted prior to transmission.

In a perfect world we would have added notes to the data-over-time graph at the top of each report. However, we can’t get inside that part of GA using a Firefox extensions (or Greasemonkey script). We thought this was a good compromise. If anyone out there knows how to dig into Flash let us know! :)

Road Map

This is obviously a beta version of the software. We have a number of features that we’re working on and hope to have done soon. These include:

Sorting and searching notes by date
Excel friendly export
An admin flag for notes to separate admin changes to your GA account
Some type of alert to show you how many new notes have been added since your last login
A more graphical visualization of note

If you have any suggestions or ideas please let us know!

Credit

I don’t have the smarts to build these types of things, I just know enough to be dangerous. Chris, a new member of our team, built GA Notes from the ground up. Thanks Chris for all the hard work.

Adding Business Data to Google Analytics Data is a post from: Analytics Talk by Justin Cutroni

Tying clicks & content to conversion in GA

Justin Cutroni — Mon, 21 Jul 2008 03:59:08 +0000

Many site owners spend a lot of time creating content that is supposed to drive conversions. But what’s the best way to measure the performance of this content with Google Analytics? How can we measure a specific piece of content, be it a page or a piece of creative, and it effect on conversions?

Google Analytics has a metric called $Index to help measure the “value” of site pages. But the problem with $Index is that it is an average, and averages can be skewed very easily. $Index is about the performance of a page, not the content on a page. Also, many people want to know how many times a piece of content directly led to a conversion. We just can’t get that with $Index.

We could view this type of analysis as a navigation analysis. Google Analytics has the All Navigation report and the Initial Navigation report, but these reports track things that happen in under 3 clicks and not everyone converts in 3 clicks.

Rather than tackle this problem using navigational analysis, let’s consider it a content challenge. What we want to do is see if a specific piece of content ultimately lead to conversion.

Given this approach we could use the Site Overlay report, which is supposed to show the performance of each link on a page. But, in my experience, the Site Overlay report is buggy at best (and I’m being nice).

We need is a way to link a piece of content, i.e. a pageview, to a conversion. There’s a very simple way to do this using the Funnel Visualization report.

The Concept

Each funnel has a ‘required step’ setting. When enabled, this setting requires that the visitor views the first step in the funnel prior to conversion. If the visitor does not see the first step then the Funnel Visualization report will not count a conversion. The conversion will still be recorded in all other reports, but not the Funnel Visualization report.

What few people know is that it does not matter when the ‘required step’ is viewed, as long as it is viewed prior to the conversion.

We’re going to use this setting to associate a conversion with the content we want to evaluate.

Example

Let’s say I want to track how many people view the About Me page on this blog before subscribing to my RSS feed. I can create a goal and funnel that links the About Me pageview to the RSS subscription goal.

Step 1: Set up the goal

The first step is to create the goal. Just set up the goal like any other normal goal. Identify the goal URL, give the goal a name and a value (if you so desire).

Step 2: Identify the “required step”

Now let’s turn to the funnel. Remember, step 1 in the funnel, the ‘required step’, is really the piece of content (i.e. the pageview) we want to evaluate in terms of conversions. Simply add the page URL to the Step 1 URL field, give the step a name, and check the “required step” checkbox.

That’s it! There’s nothing else to do. The funnel visualization report, for this specific funnel, will only show a conversion if the visitor views the About Me page at some point prior to conversion. GA doesn’t care when the visitor sees the page, as long as they see the page prior to conversion.

The Data

Here’s a sample Funnel report. We can see that 4 conversions occurred after viewing the about me page. Remember, it does not matter when the About Me page was viewed, as long as it was viewed prior to conversion.

Now, if we compare the number of conversions in the Funnel Visualization report, to the overall number of conversions for this goal, we notice there is a difference.

The difference is the number of visits that did not include the About Me page prior to subscribing to the RSS feed. There were 8 total RSS Subscription conversions, but only 4 of those conversions viewed the About Me page prior to converting. Now we know how effective the About Me page was at driving RSS subscriptions.

Taking it Further

What about associating a set of pages with a conversion activity? No problem, just use a regular expression to define your required step. Here’s the same example, but I’ve tweaked to to track visits that include the About Me page or the All Posts page.

And remember, the pageview you specify for your required step does not need to be a real “pageview.” It can be a virtual pageview, generated with the pageTracker._trackPageview() method. In fact, that’s what I’m doing on the blog. I generate a virtual pageview every time someone clicks on the RSS icon.

This technique is very useful if you want to measure how well a specific piece of content on a page is performing. Generate a pageview when a visitor clicks on the content and use it as step 1 in the funnel.

Think this is a good idea? Got one that’s better? Leave a comment!

Tying clicks & content to conversion in GA is a post from: Analytics Talk by Justin Cutroni

2008 Google Analytics Resolutions

Justin Cutroni — Tue, 08 Jan 2008 15:13:04 +0000

Happy new year! I can’t believe 2007 is over. Continuing with a tradition I began last year, I give you my 2008 GA resolutions.

Before I get into the list, I want to thank everyone who reads and contributes to Analytics Talk. 2007 was an incredible year for me, and I really owe a lot to you guys. Thank you for reading, posting questions and helping me learn so much.

1. I will migrate to the new GA.JS tracking code.

Google announced a new version of the tracking code, ga.js, in October 2007 and launched the new code in December. After some minor launch problems things seem to be running smoothly. While you don’t need to migrate to ga.js, you should start to think about it because Google will no longer add features to urchin.js. In my opinion, you should tackle this problem sooner rather than later.

2. I will contemplate Event Tracking and how I can use it.

The reason Google introduced a new version of the tracking code was to enable a powerful new feature called Event Tracking. While most folks might think of event tracking as a ‘web 2.0′ tracking tool geared towards video players and Ajax, it’s really a flexible framework for data collection. I was skeptical at first, but now I’m a convert. All of us can take advantage of this new feature.

I’ll be writing more about Event Tracking and its uses when Google pushes the feature to everyone. In the mean time, check out this series of posts to learn more:

Event Tracking Pt. 1: Overview & Data Model
Event Tracking Pt. 2: Implementation
Event Tracking Pt. 3: Reporting & Analysis

3. I will get creative with profiles.

This is something I’ve been talking about for a while. Profiles are so much more than website data. They’re a collection of data and business rules. Last year, as part of my 2007 resolutions, I mentioned setting up test profiles as a way to insure your configuration settings are correct.

For 2008 I suggest setting up profiles for all major marketing campaigns and mediums. Why? So you can segment reports that normally can not be segmented. Check out Segmenting Visitor Loyalty Reports in GA for more information.

4. I will try some type of ‘advanced’ Google Analytics configuration.

Most of us have a fairly basic implementation of GA. We don’t need to do much more than add the tracking code to our site, set up goals, and configure on site search reporting.

Why not try something new this year? How about using an ‘advanced’ feature like custom segmentation, event tracking or even e-commerce tracking? All of these features can help you learn more about your visitors and what they do. That’s why we use these features and try these hacks: to gain insight and knowledge.

5. I will keep track of website changes and Google Analytics changes.

This is something that I wrote about a long time ago, but it’s still really important. It’s a good idea to keep track of your GA configuration changes so you can better understand the data. Any modifications, like a change to a goal, funnel or filter, should be recorded.

It’s also a good idea to keep track of website changes and online marketing changes. Knowing what’s going to happen with your online business helps drive analysis and you’ll be able to deliver data that will make people happy.

You don’t need anything elaborate, a simple Google Spreadsheet, like this one, will work just fine.

There you have it, a few ideas to spice up your 2008 Google Analytics plans. Got a better idea or think that I missed something? Leave a comment below. And happy new year!

2008 Google Analytics Resolutions is a post from: Analytics Talk by Justin Cutroni

Make GA Data Quality Suck Less!

Justin Cutroni — Tue, 06 Nov 2007 14:44:03 +0000

We all know that data quality sucks! But there are a few, vital steps that you can take to insure that your Google Analytics data is as accurate as possible. Remember, accurate data makes for happy, and accurate, analysts.

Here are three simple tips that can help make your data more accurate.

1. Eliminate Duplicate Data

Many sites that I work on have duplicate data. The usual cause is mixed case URLs. Google Analytics is case sensitive, it captures the data exactly as it appears in the location bar of the browser. So if a URL is of mixed case in the browser, it will be captured and displayed in mixed case within GA.

It’s very easy to have two URLs, that have the same functional meaning, appear as two line items in GA because they have a different case. Here’s an example:

/worldseries/index.php?year=2007&keyword=lowell
/Worldseries/index.php?year=2007&keyword=Lowell

Both URLs are probably the same, they just appear different because of the case. We want to force both URLs to have the same case and thus make them appear as a single line item in GA. This can be done with a Lowercase or Uppercase filter. I like the lowercase filter, but you could easily use the uppercase filter. It’s a personal preference.

The filter below will force the Request URI to lowercase:

I recommend adding a case changing filter to any data element (i.e. filter field) that could be mixed case. This includes:

Request URI
Campaign Name
Campaign Term
Campaign Medium
Campaign Source

Another cause of duplicate data is multiple URLs that display the same content but have a different file extension. Here’s an example:

/champions/redsox.php
/champions/redsox.htm

These URLs may appear different (because of the file extension), but the web server might interpret them as the same file. Please note that not every web server behaves this way. It all depends on your web server. Check with your IT guru if your site has URLs with multiple file extensions.

You should merge duplicates URLs, that have different file extensions, into a single line item. I find the best way to do this is with an advanced filter.

Some may think that a search and replace filter is the best way to remove these duplicates. But you would need to create a search and replace filter for each set of URLs that needs to be merged. An advanced filter, because it uses a regular expression, will change every URL that ends in ‘.htm’ to a ‘.php’ extension.

2. Remove Irrelevant Information

Extra information in the URL can cause big problems in Google Analytics. The reason is that GA will capture all of the data in a URL, which includes the query string parameters. Query string parameters that don’t have a functional meaning should be removed from the URL.

An easy way eliminate these parameters is to collect data for a week and then analyze the top content report. Any query string parameter that does not provide insight into what the visitor sees or does should be eliminated.

To remove a query string parameter from GA simply add it to the ‘Exclude URL Query Parameters’ field in the profile settings:

Enter multiple parameters as a comma separated list.

Be aware that once you remove a query string parameter from GA it is completely eliminated from the system. So any goals, funnels or other filters that use that parameter will no longer work.

Also remember that you should remove any query string parameters that contain personally identifiable information. It is against the GA terms of service to collect PII.

3. Identify Your Segment

I could have easily named this tip ‘exclude internal data’ but I wanted to change the way we all think about profiles and the data that’s in them. I believe we should think of profile data in terms of the segment we want to analyze, not who we want to exclude. I know these statements are very close in meaning, but there is a slight difference. Segmentation is so important to analysis. I believe that every time we create a profile we should consider what segment of data it will contain.

I can think if a few segments of data that I would like to analyze:

CPC traffic
New visitors
Return visitors
European visitors
Traffic from a specific marketing campaign
Non-employee traffic
Traffic generated by my call center

All of the above segments can be created as different profiles using include filters. Each will provide some insight into that segment. Don’t get me wrong, you’ll probably want to exclude internal traffic from 99% of your profiles. But try to think in broader terms, focus on the segment that you want to analyze.

Creating a profile based on a particular segment of traffic is pretty easy. The first thing you want to do is identify what segment of traffic you want to include in your profile. Then create a filter based on the filter filed that represents that segment.

Let’s say I want to see all traffic generated from visitors performing some type of external search on my name. I could apply the following include filter to a profile:

This filter can easily be modified to include a specific marketing campaign (using the Campaign Name field), a specific country (using the Visitor Country field) or any other segment of data so long as it is represented by one of the filter fields. Please note that this will work even if you’re using AdWords auto tagging on, even though you haven’t done any heavy lifting to define the Campaign Term.

By the way, you will want to exclude internal traffic from many profiles. My favorite way to remove internal traffic from a profile is with an ‘Exclude all traffic from an IP address’. Make sure you use anchors at the beginning and end of the regular expression.

Another good way to exclude internal traffic, especially if you don’t have a static IP address, is to use a little hack called Count Me Out. This hack uses the GA custom segment cookie to identify users.

So remember, yes, you need to exclude internal traffic, but try to take a broad view and think about segmentation when you filter your profiles.

Make GA Data Quality Suck Less! is a post from: Analytics Talk by Justin Cutroni