Using Offline and Online data to drive Google Analytics Remarketing

The Google Analytics platform has been changing from a web analytics tool to a user-centric digital measurement tool (we’ve been calling it Universal Analytics). This evolution includes a number of changes to the system and completely new features. But what can you do when you put all of these pieces together? I wanted to write […]

Using Offline and Online data to drive Google Analytics Remarketing is a post from: Analytics Talk by Justin Cutroni

The post Using Offline and Online data to drive Google Analytics Remarketing appeared first on Analytics Talk.

The Google Analytics platform has been changing from a web analytics tool to a user-centric digital measurement tool (we’ve been calling it Universal Analytics). This evolution includes a number of changes to the system and completely new features. But what can you do when you put all of these pieces together?

I wanted to write a quick post about how a business could use the entire platform to better market to users on the web based on non-website activities. We’ll explore how to use offline and online data to create remarketing lists in Google Analytics.

Before I start a hat-tip to my buddy Dan Stone – a product manager at Google Analytics who often talks about this type of usage.

Influencing Display Advertising using Email Behavior

Businesses interact with users via many different channels – search, display, social, email, etc. And they’re always looking to better understand how one channel impacts another channel. That’s why we have attribution modeling.

But sometime we want to take direct action, or even automated action, in a channel based on user behavior in a separate channel.

For example, I may want to change my search or display strategy for users on my email list. Perhaps I want them to see different display ads because I have a better relationship with them.

Here’s an example.

With Analytics we can collect data from email marketing tools, send it to Google Analytics and then use that information to change display campaigns.

We can send data from email marketing tools, to Google Analytics, then use the data to drive Remarketing.

We can send data from email marketing tools, to Google Analytics, then use the data to drive Remarketing.

The Implementation

With some of the new features in Google Analytics it is very possible to change a user’s display advertising experience based on behavior in other digital environments.

The first thing we need to do is bind the data in Google Analytics to the data in our own systems. This might be the data in a CRM or some other customer system. We’re going to use an old-school method that I describe in the post integrating Google Analytics with a CRM.

Here’s a summary…

When a user visits your site (or your app) Google Analytics sets a unique, anonymous identifier. This identifier is called the Client ID or cid for short.

What we need to do is extract the client ID value from the Google Analytics cookies and pass it to your CRM system. Once it’s in your systems you should be able to join your internal customer IDs with the GA ID. I should note – this is not some task you finish in an afternoon. You need some nerd help and it could take a while.

You can extract the GA identifier from the tracking cookie and send it to your own system.

You can extract the GA identifier from the tracking cookie and send it to your own system.

Make sure you check out these two posts for more information:
Integrating Google Analytics with a CRM
Understanding Cross Device Measurement and the User-ID

Now that we have the two data sets joined we can do something really cool – we can send user-specific data to Google Analytics from other systems. This means that when we send out an email, or some other user-specific actions happens in our system, we can send that behavioral data to Google Analytics. How?

To send data to GA from other systems we use the measurement protocol. This technology let’s us send data to Google Analytics from any system that can connect to the internet. It defines how to send data to GA. We’ll use the measurement protocol to send data about email activities.

When we send an email to a user we will also send a measurement protocol hit to Google Analytics.

When an email is sent from your system, you can send a hit to Google Analytics using the measurement protocol.

When an email is sent from your system, you can send a hit to Google Analytics using the measurement protocol.

Specifically, we’ll send an event piece of data. The event will indicate that an email was sent to this user and the type of email:

www.google-analytics.com/collect?v=1&tid=UA-XX-Y&cid [UniqueID]&t=event&ec=Email&ea=Send&el=BackToSchool2014

If we want to be really fancy then we can also send a second hit to Google Analytics when the user receives the email and another hit when the user opens the email. For example, if the user opens the email then we can trigger a pixel within the email that sends a hit to Google Analytics.

www.google-analytics.com/collect?v=1&tid=UA-XX-Y&cid=[UniqueID]&t=event&ec=Email&ea=Open&el=BackToSchool2014

I need to stress, you need to write a bunch of code that generates these hits. The implementation will really depend on your systems.

The data in the above hits indicates that this email was part of the BackToSchool2014 campaign (look for the event data ec for Event Category, el for Event Label, ea for Event Actions. If we looked in Google Analytics the data would look something like this:

Offline email actions can be captured with the measurement protocol as events.

Offline email actions can be captured with the measurement protocol as events.

All of these hits include a specific parameter named cid. This is the Client ID for the particular recipient of the email that I discussed earlier. When Google Analytics processes these hits they will be merged with the dame user data from the website – because they have the same cid value.

OK, now we have user data coming from two separate systems and Goole Analytics is merging it together.

Here’s where the fun comes in.

Because all of this data is in one place, we can segment users in Analytics based on behavior, then use that list of users for remarketing.

You can join the Google Analytics ID, called CID, with your own ID. But then you can collect off-site actions in GA and tie them to other GA data.

after all of our work, we’re using the GA data measuring sent emails to create a remarketing list.

For those that have not use Remarketing, this is one of my favorite features in Google Analytics. Remarketing let’s you segment user on your website then send that list of users to Google AdWords (or DoubleClick if you use Analytics Premium) for use as a remarketing list.

The remarketing segment would look like this:

Segmenting users that received and opened an email.

Segmenting users that received and opened an email.

This segment is all users that opened the back-to-school email. I could also add a condition that the user received the email, but that’s not really necessary.

Now we can use this list of users in AdWords. How? I may want to use the same creative for their ads. Or perhaps I offer them the same deal that was in the email.

This technique is not just for email – you can use the measurement protocol to send data from any system. That means behavioral information from other digital experiences can be used to drive remarketing lists.

Hopefully this example gives you some idea of how multiple Google Analytics features can be used together to drive real business results.

Using Offline and Online data to drive Google Analytics Remarketing is a post from: Analytics Talk by Justin Cutroni

The post Using Offline and Online data to drive Google Analytics Remarketing appeared first on Analytics Talk.

Understanding Google Webmaster Tools

As much as you might think Google is making it hard to get traffic, they really aren’t. They have tools like Google Webmaster tools and Google Analytics. The difference between the two are the fact that Google Analytics measures your traffic, and Google Webmaster Tools tells you how Google actually sees your website. This article…

The post Understanding Google Webmaster Tools appeared first on Diamond Website Conversion.

google-webmaster-tools-logo-thumbnailAs much as you might think Google is making it hard to get traffic, they really aren’t. They have tools like Google Webmaster tools and Google Analytics. The difference between the two are the fact that Google Analytics measures your traffic, and Google Webmaster Tools tells you how Google actually sees your website.

This article is written to help you understand Google Webmaster Tools better. In fact, this article is part of a series, so there will be other parts to check out so you can become more familiar with Google Webmaster Tools.

Understanding Google Webmaster Tools

As mentioned before, Google Webmaster Tools is designed for you to see how the search engine (Google) sees your website. Consider it kind of like the doctor promoting healthy search for websites. Some of the results are:

  • Sharing what type of markup data format the search engines are seeing in your site, like Schema.org
  • Suggesting how to improve user experience and performance
  • Allowing you to demote specific areas of your site from Sitelinks
  • Giving a details list of search queries done on your website
  • Giving a list of links to sites linking into your website
  • Listing internal links
  • Showing Index status
  • Giving a list of keywords that are organized by the most significant one first
  • Allowing you to remove URLs from your website
  • Displaying crawl errors, as well as what types of errors
  • Having the ability to block URLs from the search engines
  • Being alerted if there are any security issues

In order to be able to use Google Webmaster tools, you must sign up and submit your website. The process involves putting a verification code somewhere on your website or verifying it through your domain registrar. After you verify the site, you need to submit a sitemap, once that is a valid Sitemaps.org sitemap.

The Sitemap.org valid sitemap allows Google to easily crawl the site. The markup used that search engine crawl is XML. For website owners that use WordPress and have the WordPress SEO by Yoast plugin, finding the link to the sitemap is easy. For other content management systems, there is a somewhat equivalent method to find the sitemap. For static websites (ones not powered with a database and may be solely HTML), building a sitemap may be necessary.

Once the sitemap has been submitting, Google may take a little while to crawl the site. Some site are lucky to be crawled within the week, and others, two weeks. After your site has been crawled, you can view information on what Google is seeing.

search-queries-gwt-screenshotYou probably will want to make sure that there are no crawl errors like a page not found, or any server issues. You will also want to make sure to observe if you have any duplicate meta descriptions and duplicate title tags to improve your search results. You obviously don’t want the same article description for several posts, right? 😉

Another area you might want to check out is the search queries. It’s probably good to check out the first time in order to make sure that the keywords are relevant to what your website is about. If they aren’t, you might need to go back and improve your content.

One last area that you should check is to make sure your site isn’t flagged for spam, duplicate content, or has any security issues. If you’re accepting paid links, you probably should stop. Google has gone to great lengths to discourage website owners from accepting paid links. If you have any alerts, fix the issue. Once done with fixing anything that was flagged, you can reply to Google’s team and they will review to make sure your site is not violating any of their rules.

It’s important to understand that Google Webmaster Tools can be a powerful tool in making sure your website is listed as accurately as possible on the search engine results.

Do you use Google Webmaster Tools?

The post Understanding Google Webmaster Tools appeared first on Diamond Website Conversion.

What Do You Do After You First Apply Google Anayltics to Your Website?

When you get into creating and managing a website, at some point you’re going to hear about Google Analytics, especially being told you need to have it on your website. Regardless if you’re a blogger, a small business owner, or a big corporate business, you do need a tool to measure your site’s progress. Google…

The post What Do You Do After You First Apply Google Anayltics to Your Website? appeared first on Diamond Website Conversion.

google-analytics-thumbnailWhen you get into creating and managing a website, at some point you’re going to hear about Google Analytics, especially being told you need to have it on your website. Regardless if you’re a blogger, a small business owner, or a big corporate business, you do need a tool to measure your site’s progress. Google Analytics just happens to be a good one that is also free to use.

So…

What Do You Do After You First Apply Google Anayltics to Your Website?

The majority of users may look in on their stats once a day or once a week. Google Analytics provides quite a bit of statistics. You can even set campaigns to analyze traffic from your website and some of your social network handles.

It’s quite alright to take a frequent look at your stats, but if you’re just looking at them and wishing your traffic to improve, then you’re missing out on what Google Analytics can do for you. It takes analyzing what’s going on and planning a campaign to drive attention to those areas of your website that you want people to see.

The great thing about most stats programs, including Google Analytics is that they provide exactly what information you need to know about your visitors. You can even find out if you’re targeting the correct audience, and at what times they hit your website.

Once you’ve installed Google Analytics on your website, you should let it do it’s job in collecting information. After about 3 weeks to a month, you should have a nice tentative spread of your website’s traffic.

When installing Google analytics to your website for the first time, some of the most important stats you should look at are:

  • Pageviews
  • Percentage of new visitors
  • Percentage of returning visitors
  • Bounce rate
  • How you are acquiring your visitors (where are they coming from)
  • Keywords

While there are a TON of other stats, your first time through should be to gather this information and start to put together a first campaign.

Your keywords, acquisition, and your bounce rate with each campaign you plan will change in time depending on how you adjust your website conversion plan.

Keywords

Before you even look at your stats, you really should already have a list of keywords that you’ve been wanting to work on for your website. If your analytics in Google are not coinciding with your intended list, then you’ve got homework to do in creating content around those keywords. Don’t worry, some people have websites for a few years before realizing that they’ve been missing out on capitalizing on being more laser focused on their keyword strategy.

Acquisition

If your website is brand new, you might not have too much information on how you’ve been acquiring your visitors. You will have a small idea, and can use those stats to either focus on those places that are sending you traffic, or working on trying to get traffic from new sources. It might take making sure your website is properly listed on search engines, creating social network handles, and sharing your content.

Bounce Rate

Bounce rate gives you a percentage of how many of your website visitors are only viewing one of your pages, and then leaving. Your bounce rate should never be high. In fact, your strategy should be in converting those visitors to fill out your lead forms, buy your product, share and comment on your blog posts, or even subscribe to your newsletter.

If you can put a plan together that gives you a low bounce rate, great acquisition sources, and above all, making a return on investment, you’re on the right path to great website conversion. The great thing is that Google Analytics is free to use… so what are you waiting for? Go forth and find out how your website is performing!

Do you use Google Analytics in your website conversion strategy? Do you still struggle with deciphering those stats and putting a plan together? If not, what advice do you have for newbies just hooking their website’s up to Google Analytics?

The post What Do You Do After You First Apply Google Anayltics to Your Website? appeared first on Diamond Website Conversion.

The Essential Guide To Error Tracking Using Google Analytics

Oft overlooked, error tracking is one of the most valuable ways to use Google Analytics. This essential guide covers how to use Google Analytics to track:

(click a link to jump ahead)

Using:

  • Google Tag Manager
  • Universal Analytics
  • Classic Google Analytics

404 Errors

Tracking 404s is great for:

  • Finding broken links (internal and external)
  • Proactively catching broken functionality
  • Tidying up older site architecture

Use Events to capture the previous page of the visit and the complete location of where the 404 occured. These are available at document.referrer and document.location.href, respectively. These two pieces of information can let you track down exactly where the offending link happens to be; capture these instead of relying on the ‘Page’ or ‘Full Referrer’ dimension in Google Analytics. Those values can easily be altered by filters, campaign parameters, or by other features built into Google Analytics.

FOR GOOGLE TAG MANAGER:

If you’re using Google Tag Manager, checking the title element for the default ‘404’ text is a sturdy test to fire a tracking event. Something like {{title}} equals '404 Page' or {{title}} contains '404 Page', as recommended by Samantha Barnes in her excellent guide to capturing 404 metrics with Google Tag Manager. You can also reference other tags that are on the page – be creative and find a standard value to watch for.

If that’s not possible, you’ll need to get your developers to populate the dataLayer with an event that you can watch for in Tag Manager, like:

var dataLayer = dataLayer||[];
dataLayer.push({'event':'404 Error'});

Then, you add a rule that watches for the 404 Error event, like so:

Then you create a Google Analytics Event tag and populate the Event Action and Event Label with the full URL of the page and the referrer.

As a best practice, I recommend that you try and decouple the mechanism that fires the event into Google Analytics from the actual event. Doing this means you can use a single ‘generic event’ tag to handle the processing of all of your events, and instead just push data into the dataLayer for the event tag to reference. For example:

In the above, each macro simply returns a corresponding dataLayer variable for an event value. It watches for ‘event’:’eventFired’ in the dataLayer. To generate a 404 event, all that needs to happen is the following be pushed into the dataLayer:

var referrer = document.referrer;
if (referrer === '') {
  referrer = 'No Referrer';
}
dataLayer.push({
  'event': 'eventFired',
  'eventCategory': '404 Error',
  'eventAction': document.location.href,
  'eventLabel': referrer,
  'eventValue': 0,
  'eventNonInteraction': true
});

This pattern makes adding future events a snap – all you have to do is push the ‘eventFired’ event into the dataLayer with the corresponding data; no need to create a whole new Google Analytics event tag.

If you’re not using Google Tag Manager, you’ll have to manually fire these events into Google Analytics. You can either include the following code as part of the 404 page template, or add it wrapped in a test into the header of each page on your site, e.g.:

if (document.title === 'Oops! You've found a 404 Page') {
  // Appropriate event tracking code from below goes here
}

FOR UNIVERSAL ANALYTICS:

var referrer = document.referrer;
if (referrer === '') {
  referrer = 'No Referrer';
}
ga('send', 'event', '404 Error', document.location.href, referrer, 0, {'nonInteraction': true});

FOR CLASSIC GOOGLE ANALYTICS:

var referrer = document.referrer;
if (referrer === '') {
  referrer = 'No Referrer';
}
_gaq.push(['_trackEvent','404 Pages', document.location.href, referrer, 0, true]);

JavaScript Errors

JavaScript errors are errors that the browser throws while executing the code that you’ve delivered to it. There are many common causes for JavaScript errors – browser compatibility, coding mistakes, and namespace collisions, to name a few. Tracking JavaScript errors can help you ensure a consistent user experience across browsers, catch bugs that were missed during development, and gauge the impact an error is truly having on users so that a fix can be appropriately prioritized.

FOR GOOGLE TAG MANAGER:

It’s your lucky day. Google Tag Manager offers a JavaScript Error Listener right out of the box. Just select ‘JavaScript Error Listener’ from the ‘Event Listeners’; I recommend you add this to every page.

Then, you’ll need to add another tag that listens for ‘gtm.pageError’ and uses the associated values to populate an Event for Analytics. I recommend reusing the pattern above for events, like this:

dataLayer.push({
  'event':'eventFired',
  'eventCategory': 'JS Errors',
  'eventAction': {{gtm.errorMessage}},
  'eventLabel': URL: {{href}} | File: {{gtm.errorUrl}} | Line: {{gtm.errorLineNumber}},
  'eventValue': 0,
  'eventNonInteraction': true
});

Otherwise, you’ll have to create another tag specifically for turning errors into events, like this:

I make the Event Action the gtm.errorMessage value and the Event Label URL: {{href}} | File: {{gtm.errorUrl}} | Line: {{gtm.errorLineNumber}}. This makes an for an easy-to-read format that can be drilled into for deeper context.

FOR UNIVERSAL ANALYTICS & CLASSIC ANALYTICS:

To track errors in the browser, you’ll need to bind an event listener to the window.onerror event that will generate a hit to send to Google Analytics. Doing this in a 100% backwards-compatible, error-free way can be super tricky, but here’s a copy-and-paste-friendly snippet you could utilize that should work with most browsers and Google Analytics configurations. Feel free to copy and modify to your tastes:

if (typeof window.onerror === 'object' && window.onerror === null) {  // Checks if this seat is taken/exists
  window.onerror = function(message, file, lineNumber) {
    if (typeof ga === 'object') {  // Detects Universal Analytics
      ga('send','event','JS Error', message, 'URL: ' + document.location.href + '| File: ' + file + '| Line: ' + lineNumber, 0, {'nonInteraction': true});
    } else if (typeof _gaq === 'object') {  // Detects Classic Analytics
      _gaq.push(['_trackEvent','JS Error', message, 'URL: ' + document.location.href + '| File: ' + file + '| Line: ' + lineNumber, 0, true]);
    }
  }
}

Server-side Errors

Sending application error data into Google Analytics directly from your server let’s you connect backend performance with front-end behavior. It can also contextualize seemingly unconnected issues across your site.

FOR GOOGLE TAG MANAGER:

Although technically not server-side, you could expose some error information into the dataLayer and then pass it into Google Analytics. On your server, this might look something like:

var dataLayer = dataLayer||[];
<?php
foreach ( $err in $errors ) { ?>
  dataLayer.push({
    'event': 'eventFired',
    'eventCategory': 'Server Error',
    'eventAction': '<?php echo $err->errno . $err->errstr ?>',
    'eventLabel': 'URL: ' + {{href}} + '| File: <?php echo $err->errfile ?> | Line: <?php echo $err->errline ?>';
  });
<?php } ?>

And on the client, you’d see:

<script>
  var dataLayer = dataLayer||[];
  dataLayer.push({
    'event': 'eventFired',
    'eventCategory': 'Server Error',
    'eventAction': 'Fatal error: Undefined class constant "MYSQL_ATTR_USE_BUFFERED_QUERY"'
    'eventLabel': 'URL: http://example.com/test/ | File: /local/www/example.com/includes/database/mysql/database.inc | Line: 43',
    'eventValue': 0,
    'eventNonInteraction': true
  });
</script>

Sidenote:

You’ll notice me using the syntax var dataLayer = dataLayer||[]; and then using dataLayer.push({}) in order to populate the dataLayer in my code; I recommend this as best practice to avoid accidentally overwriting needed information in the dataLayer. Basically, this is read by the browser as ‘The dataLayer is equal to itself, or it’s an empty array’. If the dataLayer doesn’t exists, the next section of the ‘OR’ (||) statement is evaluated, namely ‘dataLayer is an empty array’. In this way, if it exists, it will preserve itself, and if it doesn’t, it will create a new array named dataLayer for us to use.

Not instantiating dataLayer in this way can lead to problems if your code gets messy. For example, if you’re loading the dataLayer at the top of the page, then server errors inside the <content> of the page, this could happen:

<!-- Other HTML -->
<script>
  var dataLayer = [{'PageType':'Local'}, {'timestamp': 2849200492}];
  console.log(dataLayer); // Logs '[ { "PageType": "Local" }, {'timestamp': 2849200492} ]'
</script>
<!-- Other HTML -->
<script>
  var dataLayer = [{'event': 'Server Error'}];
  console.log(dataLayer); // Logs '[ { "event": "Server Error" } ]'
</script>

Should you need to reference either the PageType or timestamp values later, they would return undefined. Using dataLayer = dataLayer||[]; and then dataLayer.push() instead of just instantiating the dataLayer prevents this from happening.

FOR UNIVERSAL ANALYTICS:

The Universal Analytics Measurement Protocol provides a relatively painless way to send hits to Google Analytics directly from your server. All it takes is a POST request, which can be pretty simple to put together. Here’s a quick and dirty example using NodeJS:

var http = require('http');

function fireMeasurementProtocolEvent(category, action, label, value, nonInteraction, cid) {

  var ni = !nonInteraction ? '1' : '0';

  var eventData = "?v=1&tid=UA-XXXXXXX-XX&t=event&cid=" + cid + "&ec=" + category + "&ea=" + action + "&el=" + label + "&ev=" + value + "&ni=" + ni;

  var payload = {

    'hostname': 'www.google-analytics.com',
    'path': '/collect' + eventData,
    'method': 'POST'

  };

  var req = http.request(payload);
  req.end();

};

FOR CLASSIC GOOGLE ANALYTICS:

The truly intrepid can manufacturer fake __utm.gif requests – all you need to do is generate a GET request to http://www.google-analytics.com/__utm.gif with the correct parameters appended to the request. Here’s a great reference from Google on how to do just that.

Modal & Dialog Errors

Finally, a great ‘error’ type that I like to catch is any kind of modal or error dialog presented to a user. Even mundane error messages can reveal more sinister patterns lurking underneath. Tracking and analyzing common site messages is essential for good site hygiene.

FOR GOOGLE TAG MANAGER:

Either create an event tag and a macro to grab the text of the modal, or using the generic tracking event outlined above:

var modal = document.getElementById('modal-message');
dataLayer.push({
  'event': 'eventFired',
  'eventCategory': 'Error Modal',
  'eventAction': modal.textContent,
  'eventLabel': '',
  'eventValue':0,
  'eventNonInteraction': true
});

FOR UNIVERSAL ANALYTICS:

var modal = document.getElementById('modal-message');
ga('send', 'event', 'Error Modal', modal.textContent, '', 0, {'nonInteraction': true});

FOR CLASSIC GOOGLE ANALYTICS:

var modal = document.getElementById('modal-message');
_gaq.push(['_trackEvent','Error Modal', modal.textContent, '', 0, true]);

Have any error-collecting techniques of your own that I missed? Tweet them to me at @notdanwilkerson.

Oft overlooked, error tracking is one of the most valuable ways to use Google Analytics. This essential guide covers how to use Google Analytics to track:

(click a link to jump ahead)

Using:

  • Google Tag Manager
  • Universal Analytics
  • Classic Google Analytics

404 Errors

Tracking 404s is great for:

  • Finding broken links (internal and external)
  • Proactively catching broken functionality
  • Tidying up older site architecture

Use Events to capture the previous page of the visit and the complete location of where the 404 occured. These are available at document.referrer and document.location.href, respectively. These two pieces of information can let you track down exactly where the offending link happens to be; capture these instead of relying on the 'Page' or 'Full Referrer' dimension in Google Analytics. Those values can easily be altered by filters, campaign parameters, or by other features built into Google Analytics.

FOR GOOGLE TAG MANAGER:

If you're using Google Tag Manager, checking the title element for the default '404' text is a sturdy test to fire a tracking event. Something like {{title}} equals '404 Page' or {{title}} contains '404 Page', as recommended by Samantha Barnes in her excellent guide to capturing 404 metrics with Google Tag Manager. You can also reference other tags that are on the page - be creative and find a standard value to watch for.

If that's not possible, you'll need to get your developers to populate the dataLayer with an event that you can watch for in Tag Manager, like:

var dataLayer = dataLayer||[];
dataLayer.push({'event':'404 Error'});

Then, you add a rule that watches for the 404 Error event, like so:

Then you create a Google Analytics Event tag and populate the Event Action and Event Label with the full URL of the page and the referrer.

As a best practice, I recommend that you try and decouple the mechanism that fires the event into Google Analytics from the actual event. Doing this means you can use a single 'generic event' tag to handle the processing of all of your events, and instead just push data into the dataLayer for the event tag to reference. For example:

In the above, each macro simply returns a corresponding dataLayer variable for an event value. It watches for 'event':'eventFired' in the dataLayer. To generate a 404 event, all that needs to happen is the following be pushed into the dataLayer:

var referrer = document.referrer;
if (referrer === '') {
  referrer = 'No Referrer';
}
dataLayer.push({
  'event': 'eventFired',
  'eventCategory': '404 Error',
  'eventAction': document.location.href,
  'eventLabel': referrer,
  'eventValue': 0,
  'eventNonInteraction': true
});

This pattern makes adding future events a snap - all you have to do is push the 'eventFired' event into the dataLayer with the corresponding data; no need to create a whole new Google Analytics event tag.

If you're not using Google Tag Manager, you'll have to manually fire these events into Google Analytics. You can either include the following code as part of the 404 page template, or add it wrapped in a test into the header of each page on your site, e.g.:

if (document.title === 'Oops! You've found a 404 Page') {
  // Appropriate event tracking code from below goes here
}

FOR UNIVERSAL ANALYTICS:

var referrer = document.referrer;
if (referrer === '') {
  referrer = 'No Referrer';
}
ga('send', 'event', '404 Error', document.location.href, referrer, 0, {'nonInteraction': true});

FOR CLASSIC GOOGLE ANALYTICS:

var referrer = document.referrer;
if (referrer === '') {
  referrer = 'No Referrer';
}
_gaq.push(['_trackEvent','404 Pages', document.location.href, referrer, 0, true]);

JavaScript Errors

JavaScript errors are errors that the browser throws while executing the code that you've delivered to it. There are many common causes for JavaScript errors - browser compatibility, coding mistakes, and namespace collisions, to name a few. Tracking JavaScript errors can help you ensure a consistent user experience across browsers, catch bugs that were missed during development, and gauge the impact an error is truly having on users so that a fix can be appropriately prioritized.

FOR GOOGLE TAG MANAGER:

It's your lucky day. Google Tag Manager offers a JavaScript Error Listener right out of the box. Just select 'JavaScript Error Listener' from the 'Event Listeners'; I recommend you add this to every page.

Then, you'll need to add another tag that listens for 'gtm.pageError' and uses the associated values to populate an Event for Analytics. I recommend reusing the pattern above for events, like this:

dataLayer.push({
  'event':'eventFired',
  'eventCategory': 'JS Errors',
  'eventAction': {{gtm.errorMessage}},
  'eventLabel': URL: {{href}} | File: {{gtm.errorUrl}} | Line: {{gtm.errorLineNumber}},
  'eventValue': 0,
  'eventNonInteraction': true
});

Otherwise, you'll have to create another tag specifically for turning errors into events, like this:

I make the Event Action the gtm.errorMessage value and the Event Label URL: {{href}} | File: {{gtm.errorUrl}} | Line: {{gtm.errorLineNumber}}. This makes an for an easy-to-read format that can be drilled into for deeper context.

FOR UNIVERSAL ANALYTICS & CLASSIC ANALYTICS:

To track errors in the browser, you'll need to bind an event listener to the window.onerror event that will generate a hit to send to Google Analytics. Doing this in a 100% backwards-compatible, error-free way can be super tricky, but here's a copy-and-paste-friendly snippet you could utilize that should work with most browsers and Google Analytics configurations. Feel free to copy and modify to your tastes:

if (typeof window.onerror === 'object' && window.onerror === null) {  // Checks if this seat is taken/exists
  window.onerror = function(message, file, lineNumber) {
    if (typeof ga === 'object') {  // Detects Universal Analytics
      ga('send','event','JS Error', message, 'URL: ' + document.location.href + '| File: ' + file + '| Line: ' + lineNumber, 0, {'nonInteraction': true});
    } else if (typeof _gaq === 'object') {  // Detects Classic Analytics
      _gaq.push(['_trackEvent','JS Error', message, 'URL: ' + document.location.href + '| File: ' + file + '| Line: ' + lineNumber, 0, true]);
    }
  }
}

Server-side Errors

Sending application error data into Google Analytics directly from your server let's you connect backend performance with front-end behavior. It can also contextualize seemingly unconnected issues across your site.

FOR GOOGLE TAG MANAGER:

Although technically not server-side, you could expose some error information into the dataLayer and then pass it into Google Analytics. On your server, this might look something like:

var dataLayer = dataLayer||[];
<?php
foreach ( $err in $errors ) { ?>
  dataLayer.push({
    'event': 'eventFired',
    'eventCategory': 'Server Error',
    'eventAction': '<?php echo $err->errno . $err->errstr ?>',
    'eventLabel': 'URL: ' + {{href}} + '| File: <?php echo $err->errfile ?> | Line: <?php echo $err->errline ?>';
  });
<?php } ?>

And on the client, you'd see:

<script>
  var dataLayer = dataLayer||[];
  dataLayer.push({
    'event': 'eventFired',
    'eventCategory': 'Server Error',
    'eventAction': 'Fatal error: Undefined class constant "MYSQL_ATTR_USE_BUFFERED_QUERY"'
    'eventLabel': 'URL: http://example.com/test/ | File: /local/www/example.com/includes/database/mysql/database.inc | Line: 43',
    'eventValue': 0,
    'eventNonInteraction': true
  });
</script>

Sidenote:

You'll notice me using the syntax var dataLayer = dataLayer||[]; and then using dataLayer.push({}) in order to populate the dataLayer in my code; I recommend this as best practice to avoid accidentally overwriting needed information in the dataLayer. Basically, this is read by the browser as 'The dataLayer is equal to itself, or it's an empty array'. If the dataLayer doesn't exists, the next section of the 'OR' (||) statement is evaluated, namely 'dataLayer is an empty array'. In this way, if it exists, it will preserve itself, and if it doesn't, it will create a new array named dataLayer for us to use.

Not instantiating dataLayer in this way can lead to problems if your code gets messy. For example, if you're loading the dataLayer at the top of the page, then server errors inside the <content> of the page, this could happen:

<!-- Other HTML -->
<script>
  var dataLayer = [{'PageType':'Local'}, {'timestamp': 2849200492}];
  console.log(dataLayer); // Logs '[ { "PageType": "Local" }, {'timestamp': 2849200492} ]'
</script>
<!-- Other HTML -->
<script>
  var dataLayer = [{'event': 'Server Error'}];
  console.log(dataLayer); // Logs '[ { "event": "Server Error" } ]'
</script>

Should you need to reference either the PageType or timestamp values later, they would return undefined. Using dataLayer = dataLayer||[]; and then dataLayer.push() instead of just instantiating the dataLayer prevents this from happening.

FOR UNIVERSAL ANALYTICS:

The Universal Analytics Measurement Protocol provides a relatively painless way to send hits to Google Analytics directly from your server. All it takes is a POST request, which can be pretty simple to put together. Here's a quick and dirty example using NodeJS:

var http = require('http');

function fireMeasurementProtocolEvent(category, action, label, value, nonInteraction, cid) {

  var ni = !nonInteraction ? '1' : '0';

  var eventData = "?v=1&tid=UA-XXXXXXX-XX&t=event&cid=" + cid + "&ec=" + category + "&ea=" + action + "&el=" + label + "&ev=" + value + "&ni=" + ni;

  var payload = {

    'hostname': 'www.google-analytics.com',
    'path': '/collect' + eventData,
    'method': 'POST'

  };

  var req = http.request(payload);
  req.end();

};

FOR CLASSIC GOOGLE ANALYTICS:

The truly intrepid can manufacturer fake __utm.gif requests - all you need to do is generate a GET request to http://www.google-analytics.com/__utm.gif with the correct parameters appended to the request. Here's a great reference from Google on how to do just that.

Modal & Dialog Errors

Finally, a great 'error' type that I like to catch is any kind of modal or error dialog presented to a user. Even mundane error messages can reveal more sinister patterns lurking underneath. Tracking and analyzing common site messages is essential for good site hygiene.

FOR GOOGLE TAG MANAGER:

Either create an event tag and a macro to grab the text of the modal, or using the generic tracking event outlined above:

var modal = document.getElementById('modal-message');
dataLayer.push({
  'event': 'eventFired',
  'eventCategory': 'Error Modal',
  'eventAction': modal.textContent,
  'eventLabel': '',
  'eventValue':0,
  'eventNonInteraction': true
});

FOR UNIVERSAL ANALYTICS:

var modal = document.getElementById('modal-message');
ga('send', 'event', 'Error Modal', modal.textContent, '', 0, {'nonInteraction': true});

FOR CLASSIC GOOGLE ANALYTICS:

var modal = document.getElementById('modal-message');
_gaq.push(['_trackEvent','Error Modal', modal.textContent, '', 0, true]);

Have any error-collecting techniques of your own that I missed? Tweet them to me at @notdanwilkerson.

Understanding Cross Device Measurement and the User-ID

One of the fundamental new features of Universal Analytics is user-centric measurement. This includes measurement across multiple devices – computers, smart phones, tablets, kiosks, etc. But this change introduces a number of new challenges for analysts and marketers. In order to do cross device measurement you need to understand some of the challenges and limitation. […]

Understanding Cross Device Measurement and the User-ID is a post from: Analytics Talk by Justin Cutroni

The post Understanding Cross Device Measurement and the User-ID appeared first on Analytics Talk.

One of the fundamental new features of Universal Analytics is user-centric measurement. This includes measurement across multiple devices – computers, smart phones, tablets, kiosks, etc.

The User-ID feature let's you measure the user journey across multiple devices - and even in stores.

But this change introduces a number of new challenges for analysts and marketers. In order to do cross device measurement you need to understand some of the challenges and limitation. Let’s begin our exploration of cross device data with a discussion about how it works.

How Cross Device Measurement Works

You’ll recall that most analytics tools set an anonymous identifier to measure users. On websites the JavaScript code creates the identifier and stores it in a cookie. On mobile apps the SDK creates the identifier and stores it in a database on the device. (We call this default ID the Client-ID).

We actually discussed this concept in our Google Analytics Platform Principles course. Skip to 21 seconds for details – but the whole video is helpful :)

The User-ID feature lets you override this default behavior. So rather than letting the tracking code create the a Client-ID, YOU can create and use your own identifier. How do you do that?

Well, your business needs to have some way of identifying users. Don’t worry, most businesses do. A CRM system or customer database usually has a User-ID that you can use.

The important thing is that you can create the technology that moves the ID from your database into your website, app or other digital experience where your users interact with your content.

The User-ID value must originate from your systems. It must eventually appear in the tracking code on your site or in your app. The User-ID will then be sent to Google Analytics  with each data hit.

The User-ID value must originate from your systems. It must eventually appear in the tracking code on your site or in your app. The User-ID will then be sent to Google Analytics with each data hit.

In the above diagram the company would need to create code that pulls the User-ID from the database, then passes it through the web servers, and finally places it in the Google Analytics Tracking code that appears on the website.

I know – that seems like a lot of work! But a good tech person can make this happen.

After you add all the necessary code, and set up the User-ID feature in Google Analytics, then the actual id value that you supply is sent to Google Analytics with each hit (see my post on hits, sessions and users to learn about all the different hit types in Google Analytics.)

Then, as Google Analytics processes the data, it groups hits with the same User-ID together. It does not matter if the hits come from a website, mobile app or some other device.

Hits from the same user can be grouped together as long as each hit has the same User-ID.

Hits from the same user can be grouped together as long as each hit has the same User-ID.

In the above image there would be three unique users – one user with a User-ID=1, one user with a User-ID=2 and one user with a User-ID=5. Again – it doesn’t matter where the hits come from (mobile, web, kiosk, etc.).

But what about instances when the User-ID is not present? For example, what if the user is not logged in and we can not retrieve the User-ID? Good question.

In this case, Google Analytics will go back to its default behavior and generate it’s own User-ID (again, this is called a Client-ID, because the ID is specific to the client or device). Obviously this ID can not be used to measure across devices as it will only exist on the device where it is set.

But now we have a scenario where a user might have two different User-ID numbers for a single user? Isn’t this going to have an impact on the data? Aren’t we trying to avoid that? This sucks!

Well – it’s not that simple. Let’s talk about something called Session Unification.

Session Unification

The above scenario is very common. You will not always be able to set the User-ID in Google Analytics – even for known users. The result is some hits and sessions will have a User-ID value, and some with an automatically generated User-ID value.

It's not always possible to send your own User-ID to Google Analytics.

It’s not always possible to send your own User-ID to Google Analytics.

Google Analytics has a feature called Session Unification. When activated, it will unify, or group, hits with the manually set User-ID and hits with an auto-generated User-ID together. This means that Google Analytics can associate some hits that were received prior to setting the User-ID.

Here’s the definition of Session Unification from the Google Analytics Help center (I think it’s pretty good!):

Session unification allows hits collected before the User-ID is assigned to be associated with the ID, as long as those hits happen within the same session in which a specific ID value is assigned for the first time.

This means that Google Analytics will only associate hits collected in the same session AND it must be in the first session where the User-ID is set.

This functionality is sometimes called “stitching” – and it differs from one tool to another.

Google Analytics will not go back in time and stitch every single session from a given user together. I can hear the groans now – and the comparisons to other tools. I hope to write about session unification in a later post. But I think a lot of people that are going to complain about this are missing the point. It’s not “can we stitch data together” it’s “should we stitch data together?”. Rant over.

So how does Session Unification impact the data? Well, to understand that we need to talk about another topic the User-ID View and reports.

The User-ID View

We all know the basic hierarchy of a Google Analytics account. Within your account you can create a number of properties. Under a property you can create a number of views – which were formerly called profiles.

Now you can designate certain views as User-ID views. This means that the view will be filtered and the only data in this view is data that contains hits where you set the User-ID value.

Only a Google Analytics view with User-ID enabled will display information about cross-device users.

Only a Google Analytics view with User-ID enabled will display information about cross-device users.

Obviously this view will have less data than a standard view where the User-ID is not enabled. But the idea is that this view will be able to provide deeper insights into how users who are logged in – a VERY valuable segment of your users – interact with your business across multiple devices.

To view all of the data, hits with and without a User-ID, you would use a standard view.

Let’s also consider the scenario from above – when a manually set User-ID is present in some sessions, but not others. The result is that the data from sessions without your User-ID will not be in the User-ID view.

Only hits that contain a manually set User-ID will be included in a User-ID view.

Only hits that contain a manually set User-ID will be included in a User-ID view.

There are also some other significant differences between views that do, and do not, have User-ID feature enabled.

1. Certain metrics are calculated differently. Obviously if a User-ID view contains different data, then certain metrics will be calculated differently.

For example, the number of Users is calculated based on the number of unique User-ID values. This will provide a fairly accurate view of the number of users. It will probably be less than the number of users in a standard view because that that view will also include users where you do not set a User-ID.

2. Cross device reports. This is the HUGE benefit of a User-ID view. These reports provide some awesome insights into how users access your content from multiple devices. More info about the reports below.

3. Limited date range. When working with a User-ID view you can only change the date range to the past 90 days. This is consistent with the standard 90-day user look-back window in other features, like Multi-Channel Funnels and user segments.

Implementing Cross Device Measurement

Implementing the User-ID feature can be involved, depending on your specific infrastructure. Here’s a brief overview of the process.

1. First, you need to “turn on” the user ID feature for a given property.

2. Second, you need to add the actual user ID value to the data collection. For website, this means you need to modify the JavaScript tracking code. For mobile apps you need to change the SDK.

3. Third, create a User-ID view. This is a special data view that includes new reports.

Let’s get into a bit more detail.

1. Enable User-ID Feature

This is really important to read and understand the terms. For example, it’s important to note that you can not use personally identifiable information as the User-ID. This includes an email address, name, etc.

To use the User-ID feature you must read the User-ID policy and agree to the terms.

To use the User-ID feature you must read the User-ID policy and agree to the terms.

You also really need to take a look at your own privacy policy and make sure it complies with the following:

You will give your end users proper notice about the implementations and features of Google Analytics you use (e.g. notice about what data you will collect via Google Analytics, and whether this data can be connected to other data you have about the end user). You will either get consent from your end users, or provide them with the opportunity to opt-out from the implementations and features you use.

2. Implement User-ID in your Tracking Code

Now for the coding! Go get your nerds and Red Bull! Just kidding.

As I mentioned above, the User-ID value must come from you. You must generate the ID from one of your systems. Once you do that you must place it inside the tracking code. The hard part is writing the code that moves the User-ID from your systems and puts it in {{ USER_ID }} in the code snippets below..

Let’s look at some of the most common code formats.

Adding the User-ID to website tracking

Adding a User-ID to the JavaScript code is fairly easy – it’s a single line.

ga('create', 'UA-XXXX-Y', 'auto');
ga('set', '&uid', {{ USER_ID }});
ga('send', 'pageview');

Remember, the User-ID needs to be set before any hits are sent to Google Analytics. So make sure you call the set command before a pageview, event, transaction, etc. is sent.

It’s also recommended that you include the set method on ALL of the pages, not just one page.

See the developer docs for more about JavaScript information.

Adding the User-ID to the Android SDK

t.set("&uid", {{ USER_ID }});

See the developer docs for more Android information.

Adding the User-ID to the iOS SDK

[tracker set:@"&uid" value:{{ USER_ID }}];

See the developer docs for more iOS information.

For both the Android code and the iOS code, you only need to set the User-ID once. Once it is set once the User-ID will be sent with all subsequent hits. But try to set it before any hit is sent to Google Analytics.

Adding the User-ID to the Measurement Protocol

Adding the User-ID to a measurement protocol hit is actually really easy. All you need to do is add the uid parameter in each hit. So a hit might look something like this:

http://www.google-analytics.com/collect?v=1&_v=j16&a=164718749&t=pageview&_s=1&dl=http%3A%2F%2Fcutroni.com%2F&ul=en-us&uid=hsjfy4782jduyth6k4

Adding the User-ID via Google Tag Manager

A quick note that you can also set the User-ID with Google Tag Manager. You’ll find the setting in the ‘More Settings > Fields to set’. You’ll also need to create a macro to pull the actual User-ID value from a cookie or the data layer.

You can set the User-ID value with Google Tag Manager.

You can set the User-ID value with Google Tag Manager.

In addition to adding the User-ID to your data collection code, you must also choose if you want to use Session Unification. See above for more information on session unification.

The second step in setting up the User-ID is to add the actual identifier to the tracking code for your site or app.

The second step in setting up the User-ID is to add the actual identifier to the tracking code for your site or app AND configuring the session unification setting.

Now it’s time to add a User-ID view.

3. Create a User ID View

As mentioned above, a User-ID view is a filtered view of your data. It only includes hits in which you have set the User-ID value. This view also contains reports that show cross device usage and other user-centric metrics.

Please note, this view is in ADDITION to the other views that you have for a property. This means that you will need to configure things like goals, filters custom reports, dashboards, etc. on this new view.

You must create a new User-ID view to see the Google Analytics cross device reports.

You must create a new User-ID view to see the Google Analytics cross device reports.

That’s really it. I don’t want to oversimplify the implementation. But most of the work is really creating the code that pulls your User-ID from your systems and then places it in the correct tracking code.

Data and Reports

We finally made it, let’s look at some data and figure out how we can use this.

Remember, in all of these reports we’re trying to understand the behavior of our users. And we’re not just looking at the behavior of everyone – we’re looking at the behavior of those users that have self identified. This is really important as this group is naturally very valuable.

User-ID Coverage

Let’s start by understanding what percentage of our users are actually logged in.

Remember, you’ll have two different profiles with data. One profile will just be all of the data, the other will be a User-ID profile that only contains information about logged in users.

The Coverage Report identifies the data that has a User-ID associate with it vs. the data that does not have a User-ID.

The User-ID Coverage report shows what percentage of your sessions have a User-ID associated with them.

The User-ID Coverage report shows what percentage of your sessions have a User-ID associated with them.

Remember, you’re probably not going to get 100% User-ID coverage – unless your online experience requires authentication. But this depends on your specific business and your specific implementation.

You can use this data to get a better understanding of how big your data pool is – will you be making decisions on 1% of sessions or 50%?

Device Overlap

Ok this is where we start to get into the really interesting data. Here’s a visualization that shows the device overlap. That means the percentage of users that use various combinations of devices.

Device overlap shows the number of users and the value of users based on combinations of devices.

Device overlap shows the number of users and the value of users based on combinations of devices.

Rather than just looking at how many people use certain combinations of devices, let’s look at the associated revenue from those combinations. Notice that you can change the display using the selector at the top of the chart?

The Device overlap report also includes detailed information about how combinations of devices drive value.

The Device overlap report also includes detailed information about how combinations of devices drive value.

So what’s the actionability here?

Do people who use a certain combination of devices behave differently than others? Are people who use tablet and desktop more valuable than those that use tablet and phone? If so – how do we encourage more of that behavior? Is it via marketing? Changes to the platform?

And don’t forget, you need to add a LOT of context to this data. You need to keep in mind your marketing initiatives along with the user experience that you offer your users on each device.

Device Pathing

Now let’s move on to device pathing. This report shows the device used for a sequence of sessions.

Device Pathing shows the user sequence of devices.

Device Pathing shows the user sequence of devices.

You can look at a specific path prior to, or after, a user action. The action could be a goal conversion, a pageview, a transaction or an event.

You can view the device path prior to, or after, specific user actions.

You can view the device path prior to, or after, specific user actions.

What’s the actionability here? Let’s look at a use-case.

If you have a SaaS business you may offer your users a free trial. In this case the user would create an account for the trial and then use your service. At the end of the trial they would need to upgrade to a real account.

The first thing you could do is look at the user’s device behavior after creating their free trial account. Did they perform any specific tasks on a specific device? Was one device more popular than another? If so maybe you can simplify the workflow on that device.

You could also look at the device path at the end of their trial, when they upgraded to a paying account. Did they perform the upgrade on a certain device? Or, more importantly, was the conversion rate higher on on a certain type of device? If so, you might want to simplify or optimize the process on that type of device.

Don’t forget to look back at the Device Overlap report to understand if a certain combination of devices yielded a more valuable customer.

Also notice that there are a lot of ways that you can configure this report to view different paths. One of the most important things to note is that you can not choose specific instances of each item. For example, if a user generates multiple transactions you can not choose a specific transaction.

You can choose to view the device path before or after various user actions.

You can choose to view the device path before or after various user actions.

My suggestion is to create a goal, page or unique event for the most important user actions – the ones that represent the transition from one phase of the customer lifecycle to another. That way you will always be able to see the device path before and after the action.

Device Acquisition Report

Finally we have the Acquisition Device report. This is similar to the Device Overlap report in that it helps you understand the value of users on a certain device. But the difference is that it shows the value based on the first device type.

Use the Device Acquisition report to understand if users acquired on a certain device generate revenue on the same device or different devices.

Use the Device Acquisition report to understand if users acquired on a certain device generate revenue on the same device or different devices.

What’s the actionability here? Do users acquired on a specific type of device generate more value on the same device or on other devices? If so how can you drive more of that behavior?

Segmentation

One final thing to mention. You may have noticed that you can apply segmentation to all of these reports. Segmentation will work the same in these reports as it does in all other GA reports.

If you create a session based segment then Google Analytics will show all the paths that include a session that meets your criteria.

If you create a user based segment then Google Analytics will show all of the paths generated from users that match your criteria.

Did you make it to the end? I hope this post gave you some insights into how Cross Device Measurement works. There’s going to be a lot of chatter about User-ID and cross device measurement – some positive, some negative. And I have a lot more to say – but this post is long enough!

Understanding Cross Device Measurement and the User-ID is a post from: Analytics Talk by Justin Cutroni

The post Understanding Cross Device Measurement and the User-ID appeared first on Analytics Talk.

Universal Analytics: Now out of beta!

We’ve been talking about Universal Analytics for a long time – over a year. In that time Universal has always been in beta because it was not 100% compatible with the existing version of GA. Sure, various parts of the Universal platform have rolled out, like the Measurement Protocol and Dimension Widening, but we were […]

Universal Analytics: Now out of beta! is a post from: Analytics Talk by Justin Cutroni

The post Universal Analytics: Now out of beta! appeared first on Analytics Talk.

We’ve been talking about Universal Analytics for a long time – over a year. In that time Universal has always been in beta because it was not 100% compatible with the existing version of GA. Sure, various parts of the Universal platform have rolled out, like the Measurement Protocol and Dimension Widening, but we were missing things like Remarketing and Audience data. But no more :)

I’m excited to say that as of today, April 2, 2014, Universal Analytics is out of beta!

Universal Analytics: The next generation of Google Analytics

Let’s run through everything you need to know about the announcement.

100% Feature Compatibility

Universal Analytics now supports all standard Google Analytics features. This includes:

Remarketing with Google Analytics. This is one of my favorite analytics features – and it made me very sad that Universal Analytics did not support it. But that’s in the past – You can now use the remarketing feature with Universal Analytics.

Audience reporting. The audience reports are an awesome way to understand who is using your site. They include data like gender and interest categories. This can be incredible helpful when trying to understand if the correct audience is using your site. Now you can use this feature with Universal Analytics.

Premium SLA Support. For all of those using Google Analytics Premium, all of your standard SLAs now apply to Universal Analytics. This includes data collection, data processing, etc.

Full Google Tag Manager support. Google Tag Manager now fully supports all Universal Analytics features, this includes audience data and the new User ID feature (discussed below).

I’ve said it many, many times – I’m a big fan of tag management. If you are going to migrate to Universal Analytics you might as well migrate to Tag Manager (or any tag management solution) now!

Universal Analytics is Google Analytics – and vice versa. Everything that Google Analytics can do, Universal Analytics can do – and more :)

Cross Device Measurement

In addition to complete feature compatibility, cross device measurement, via the User-ID feature, is now available.

The User-ID feature let's you measure the user journey across multiple devices - and even in stores.

The User-ID feature let’s you measure the user journey across multiple devices – and even in stores.

As you recall, this feature lets businesses use their own User-ID to measure customers across multiple devices. This feature includes some awesome reports to help businesses understand which devices and behaviors generate value. Here’s a quick overview:

Device Overlap: This report can help you identify what types of devices your users use to access your service or content.

The Device Overlap report shows what percentage of users access your content from multiple devices.

The Device Overlap report

Device Paths: This report will show the last five devices that were used prior to a conversion. It’s a bit like the Multi-Channel Funnels report – but for devices.

The Device Path report shows the last five devices that were used prior to a conversion.

The Device Path report

Acquisition Device: This report shows revenue based on the device that generated the first conversion. It’s can help you understand if users on a certain device have a larger impact on revenue.

The Acquisition Device Report.

The Acquisition Device Report

Understanding cross device measurement, and implementing it correct, is a huge topic – way more than I can cover in one post. I’ll be publishing a few other articles that explain cross device measurement in Google Analytics ASAP.

Time-zone Based Processing

In addition to the above features, there’s one more piece that is rolling out today. Google Analytics users can now specify the time-zone where their data is processed. In the past all data was processed in the Pacific Timezone (because that’s there Google is).

But now data processing will occur in the time zone of each data view.

The time zone setting in a view now controls when your data is processed.

The time zone setting for a view now controls when your data is processed.

While most people will not notice a big difference, this is a HUGE improvement for many users in Australia, Japan and other parts of Asia.

This also means that, for some users, automated daily reports will arrive on the correct day!

Do you need to migrate?

Ok, so that’s a brief overview of what’s happening today. But the big question that everyone will ask is, “do I need to migrate to Universal Analytics?”

No, you do not need to migrate to Universal Analytics – at least not now.

However, you need to start planning to migrate.

Universal Analytics is the new platform – all new features will be developed for UA. So if you want to use the new shiny things in the future you need to be on UA.

But migrating t can be a lot of work depending on your specific measurement plan. I’ll address that in another post.

Ok, that’s it for this post. But there is a lot more on Universal Analytics coming.

Universal Analytics: Now out of beta! is a post from: Analytics Talk by Justin Cutroni

The post Universal Analytics: Now out of beta! appeared first on Analytics Talk.

Advanced Content Tracking with Universal Analytics

A while ago I wrote Advanced Content Tracking – a post about how to measure if users are actually reading your content. I’ve been getting a lot of requests to update this code for Universal Analytics. So here it is – an updated script specifically for use with Universal Analytics. This Google Analytics customization collects […]

Advanced Content Tracking with Universal Analytics is a post from: Analytics Talk by Justin Cutroni

The post Advanced Content Tracking with Universal Analytics appeared first on Analytics Talk.

A while ago I wrote Advanced Content Tracking – a post about how to measure if users are actually reading your content. I’ve been getting a lot of requests to update this code for Universal Analytics.

So here it is – an updated script specifically for use with Universal Analytics.

This Google Analytics customization collects data as users scroll down a page. It uses events to track when a post loads, when the user scrolls more than 150 pixels, when the user reaches the bottom of the content and when the user reaches the bottom of the page.

This technique uses Google Analytics events to track a user as they scroll down a page of content.

This technique uses Google Analytics events to track a user as they scroll down a page of content.

The end result is some cool data about how many users actually read content. Here’s a sample of what the data looks like. This is just an basic event report with the Event Action and Event Label.

You can access the Reading data in your Event reports. Here we see a single article and how often users scrolled, read the whole article and got to the bottom of the page.

You can access the Reading data in your Event reports. Here we see a single article and how often users scrolled, read the whole article and got to the bottom of the page.

The Scroll Tracking Code

Here is the JavaScript code that measures user scrolling.


TIP – You can use the tabs at the top of the code window to try the script. Just click on Result.

What’s changed in this version?

First, the blog post title is now collected as part of the event. Specifically I’m pulling the page title from the HTML and putting it into the event label. This makes it easier to drill down and see which pages people are reading. This was possible before using the Page Title dimension, but using the event label makes it just a bit easier. See the image above.

Another thing I change is I now use a Custom Dimension rather than a Custom Variable, to collect the ‘reader type’. Custom variables do not exist in Universal Analytics.

This change will impact your data! You will no longer see data in the Custom Variables report – because you’re not using Custom Variables. Custom Dimensions are only available in Custom Reports and Custom Dashboards.

I also changed how the Custom Dimensions are set. This script will set a Custom Dimension when the user reaches the bottom of the post content – not the bottom of the page. When they reach the bottom of the content they are categorized as a scanner or a reader.

  • A scanner is someone that simple scrolls to the bottom of the content in less than 60 seconds.
  • A reader is someone that take more than 60 seconds to reach the bottom of the content.

This is hardly a scientific way to categorize users, but it works for me :)

Finally, I added three custom metrics to store the time metrics: time to scroll, time to content bottom and time to page bottom.

Remember, in order to configure Custom Dimensions and Custom Metric you must first add them via your Google Analytics admin settings.

Other than the above changes the functionality is still the same.

Implementing the code

Step 1: There are a few code changes that you must make in order for this code to work on YOUR site.

1. Turn off debugging: This flag will display alert messages, rather than send GA data, when the user scrolls, reaches the bottom of the content and reaches the bottom of the page. If you do not set this to FALSE your users will get all sorts of alert messages :)

2. Decide how far you want for scroll depth: I send an event after the user scrolls 150 px. You can change this value, but I believe it works fine and does capture user engagement.

3. Specify where the bottom of your content is: This is the most important setting. This script sends an event when the user gets to the bottom of a post. That’s determined by the HTML. For me, the HTML is identified as .entry-content, as shown in this code.

if (bottom >= $('.entry-content').scrollTop() + $('.entry-content').innerHeight() && !endContent) {

You must change this line of code to identify a piece of HTML on your site that signifies the end of the content. This is the hardest part of the implementation.

Step 2: Add the code before the closing on your site. Make sure it appears AFTER the Universal Analytics page tag. It should look something like this when complete:

<head>

... all sorts of tags ...

<script>
  //
  // Universal Analytics page tag
  //
  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');

  ga('create', 'UA-XXXXXX-YY');
  ga('send', 'pageview');

  //
  // Scroll tracking script
  //
  jQuery(function($) {
    // Debug flag
    var debugMode = true;

    // Default time delay before checking location
    var callBackTime = 100;

    // # px before tracking a reader
    var readerLocation = 150;

    // Set some flags for tracking & execution
    var timer = 0;
    var scroller = false;
    var endContent = false;
    var didComplete = false;

... More code here ...

</script>

That should be it. You should see data instantly in the Real Time Event reports.

I encourage you to read the instructions in my original post.

Finally, a lot of people have asked me about implementing this script with Google Tag Manager. This really warms my heart :) I love tag management!

You can use this script with Google Tag Manager – but it takes a bit of work. I’ll write a separate post on that topic.

That’s it. I hope you find this script useful. Feel free to modify it to fit your needs. I’ve really enjoyed the data that it generates – it’s helped me better understand my readers and content.

Advanced Content Tracking with Universal Analytics is a post from: Analytics Talk by Justin Cutroni

The post Advanced Content Tracking with Universal Analytics appeared first on Analytics Talk.

Hits, Sessions & Users: Understanding Digital Analytics Data

We talk about data every day – sessions, visits, conversions, pages, hits, etc. etc. etc. But sometimes we fail to understand how all of these metrics fit together and where they come from. Let’s take a look at how digital analytics tools organize data. All digital analytics data is organized into a general hierarchy of […]

Hits, Sessions & Users: Understanding Digital Analytics Data is a post from: Analytics Talk by Justin Cutroni

The post Hits, Sessions & Users: Understanding Digital Analytics Data appeared first on Analytics Talk.

We talk about data every day – sessions, visits, conversions, pages, hits, etc. etc. etc. But sometimes we fail to understand how all of these metrics fit together and where they come from. Let’s take a look at how digital analytics tools organize data.

All digital analytics data is organized into a general hierarchy of users, sessions and hits. It doesn’t matter where the data comes from, it could be a website or a mobile app or a kiosk. This model works for web, apps or anything else.

Digital analytics data is organized into a hierarchy of hits, sessions and users.

Digital analytics data is organized into a hierarchy of hits, sessions and users.

Sometimes we use the terms visitors instead of users and visits instead of sessions – they’re analogous. The onset of mobile devices (and other devices, like set top boxes) have prompted us to introduce new terms into our vocabulary.

It’s important to understand each piece of the hierarchy and how it builds on the other to create a view of our customers and potential customers. Because, at the end of the day, we need to use this data to evaluate our decisions and look for new business opportunities.

Let’s start at the bottom, with hits, and work our way up to users.

Hits

A hit is the most granular piece of data in an analytics tool. It’s how most analytics tools send data to a collection server. In reality, a hit is a request for a small image file. This image request is how the data is transmitted from a website or app to the data collection server.

All data is sent using a hit. Most hits are actually the request for an invisible image file.

All data is sent using a hit. Most hits are actually the request for an invisible image file.

There are many different kinds of hits depending on your analytics tool. Here are some of the most common hits in Google Analytics:

Pageviews/Screenviews: A pageview (for web, or screenview for mobile) is usually automatically generated and measures a user viewing a piece of content. A pageview is one of the fundamental metrics in digital analytics. It is used to calculate many other metrics, like Pageviews per Visit and Avg. Time on Page.

Events: An event is like a counter. It’s used to measure how often a user takes action on a piece of content. Unlike a pageview which is automatically generated, an event must be manually implemented. You usually trigger an event when the user takes some kind of action. The action may be clicking on a button, clicking on a link, swiping a screen, etc. The key is that the user is interacting with content that is on a page or a screen.

Transactions: A transaction is sent when a user completes an ecommerce transaction. You must manually implement ecommerce tracking to collect transactions. You can send all sorts of data related to the transaction including product information (ID, color, sku, etc.) and transactional information (shipping, tax, payment type, etc.)

Social interaction hit: A social interaction is whenever a user clicks on a ReTweet button, +1 button, or Like button. If you want to know if people are clicking on social buttons then use this feature! Social interaction tracking must be manually implemented.

Customized user timings:User timings provide a simple way to measure the actual time between two activities. For example, you can measure the time between when a page loads and when the user clicks a button. Custom timings must be implemented with additional code.

That’s a lot of hit types!

All hit types are sent to Google Analytics via a tracking code. The tracking code variation depends on what you are tracking. If you are tracing a website then JavaScript code, named analytics.js, generates the hits. If you are tracking a mobile app then an SDK (either Android or iOS) generates the hits. If you are tracking a kiosk, then YOU generate the hits with the measurement protocol.

Regardless of the hit type, the hits are all formatted in a similar manner. They are a request for an invisible image and contain data in query string parameters.

http://www.google-analytics.com/collect?v=1&_v=j16&a=164718749&t=pageview&_s=1&dl=http%3A%2F%2Fcutroni.com%2F&ul=en-us&de=UTF-8&dt=Analytics%20Talk%20-%20Digital%20Analytics%20for%20Business&sd=24-bit&sr=1920x1080&vp=1308x417&je=1&fl=12.0%20r0&
_utma=32856364.1751219558.1391525474.1391525475.1391525475.1&
_utmz=32856364.1391525475.1.1.utmcsr%3D(direct)
%7Cutmccn%3D(direct)%7Cutmcmd%3D(none)&_utmht=1391525534970&
_u=cACC~&cid=1751219558.1391525474&tid=UA-91817-11&z=378275262

For all the nerds out there, the data hits can be sent via a GET request or a POST request. This is really important to know, because the amount of data can change. With a GET request you can only send 2048 characters of data. Technically a post can be any length (it’s a setting on most servers), but it’s around 8000 characters when sending data to Google Analytics.

The information in a hit is transformed into dimensions during processing. Every report is just a single dimension, and the corresponding metrics for each value. that you see in your reports.

Each report in Google Analytics shows all of the values for a single dimension, and the corresponding metrics for each value.

Each report in Google Analytics shows all of the values for a single dimension, and the corresponding metrics for each value.

A quick note on mobile…

The mobile SDKs do not send data in real time. They actually store the hits locally and them send them in bursts. This is called dispatching and it’s used for a couple of reasons. First, mobile devices are not always connected to a network. So analytics must store the hits until it detects a connection and then it sends the hits. Second, sending hits in a bunches can help conserve battery life. Don’t worry, dispatching does not impact session calculations – which we’ll talk about right now :)

Session

A session is simply a collection of hits, from the same user, grouped together. By default, most analytics tools, including Google Analytics, will group hits together based on activity. When the analytics tool detects that the user is no longer active it will terminate the session and start a new one when the user becomes active.

Most analytics tools use 30 minutes of inactivity to separate sessions. This 30-minute period is called the timeout.

A session is a collection of hits. It ends when there has been 30 minutes of inactivity.

A session is a collection of hits. It ends when there has been 30 minutes of inactivity.

Google Analytics, and most tools, use the time between the first hit and the last hit to calculate the time on site. The time between hits is also used to calculate other metrics, like time on page. You can read more in my overview of how Google Analytics performs time calculations.

Most tools let you change the default timeout to better suit your needs. For example, if you have a lot of video on your site you might want to change the timeout – especially if your video last more than 30 minutes.

Why?

If a user is watching a 60 minute video (and by watching I mean that no other hits are sent to analytics) their session will end 30 minutes after the first hit. To insure that the session lasts until the end of the video you could change the timeout to match the longest video length.

OR, a better way to extend the session, would be to send additional hits while the user is watching the video. Think about it – more hits create more data points that can be used to calculate time. Trust me, take 12 minutes to read more about how Google Analytics performs time calculations.

Now that we know that hits are grouped together into sessions, let’s look at how sessions are grouped based on users.

Users

Here’s where things start to get interesting. A user is the tools best-guess of an anonymous person. Users are identified using an anonymous number or a string of characters. The analytics tool normally creates the identifier the first time a user is detected. Then that identifier persists until it expires or is deleted.

The identifier is sent to the analytics tool with every hit of data. Then the analytics tools can group hits (and thus sessions) together using the identifier in the hits.

Make sense?

Sessions from the same user can be grouped together as long as each hit has the same user ID.

Sessions from the same user can be grouped together as long as each hit has the same user ID.

Here’s how users are detected on some of today’s most common digital platforms.

Website Users

To measure a user on a website almost all analytics tools use a cookie. A cookie is a small text file. The cookie contains the anonymous identifier. Every time a hit is sent from the browser back to the analytics server identifier stored in the cookie is sent along with the data.

When measuring a website, the analytics tool usually uses a first party cookie to store an anonymous ID.

When measuring a website, the analytics tool usually uses a first party cookie to store an anonymous ID.

Now let’s have the cookie talk.

Google Analytics uses a first party cookie. A first party cookie is connected to the domain that creates it. A first-party can only be used by the domain that sets it. So on this site, the cookie has a domain of cutroni.com and can only be used by this website.

In Universal Analytics the cookie is named _ga and lasts for two years. In the previous version of Google Analytics the cookie was named __utma.

The good thing about a first party cookie is that almost all browsers will allow a first party cookie. It’s a very reliable piece of technology.

First party cookies are challenging when your site spans multiple domains. When a user leaves your site, and traverses to another site that you own, they do not take their first party cookies. In most situations, unless you configure analytics correctly, analytics will set another cookie when the user lands on the second domain.

Analytics uses a first party cookie to maintain a user ID.

Analytics uses a first party cookie to maintain a user identifier.

Now you have one user with two cookies. That could lead to double counting of users. Plus, if we want to create really cool metrics, like Revenue per user, it becomes very, very hard because we don’t know the true number of users.

The other type of cookie, a third-party cookie, can be set and accessed by domains other than the domain that creates it. Some analytics tools will let you use a third party cookie.

The value of a third party cookie is that the analytics tool can use a third party cookie to identify a user as they move from one domain to another.

A third party cookie can be used by multiple domains.

A third party cookie can be used by multiple domains.

However, third-party cookies are not permitted by most browsers – that means no data.

Google Analytics does not use a third party cookie. You can read all about the Google Analytics cookies in the developer documentation.

So what’s the solution here? How do you correctly identify a user if your website spans multiple domains? In the Google Analytics world we use a feature called Cross Domain Tracking. I’m not going to talk about it in this post, but you can read about it in our support documentation.

Mobile Users

Now let’s move on to mobile platforms – something that is very popular :)

Mobile tracking is similar to web tracking. There is an anonymous identifier stored on the device. The identifier is generated every time the app is installed. So if a user deletes the app the identifier will also be deleted. But if a user updates the app the identifier will not change.

The big difference between mobile and web is that the identifier is not stored in a cookie. It’s stored in a database on the mobile device – but it basically functions the same way as a cookie. The identifier is sent on every hit back to the analytics server. The analytics server then uses the identifier to create metrics like unique users.

Here’s one challenge with user measurement on an app. Many apps are not just an app. They’re a hybrid app/website. They use a browser within the app to “frame” content from a website. This can mess up the data collection.

In this situation we have two technologies with two different user identifiers. The app will measure a user based on the ID stored on the device and the website will use a cookie when a page loads in the app.

Mobile apps that "frame in" content from a website, might be sending duplicate hits to the analytics tool.

Mobile apps that “frame in” content from a website, might be sending duplicate hits to the analytics tool.

There are some ways around this, but it’s a long solution that need it’s own blog post. But just be aware of this potential data issue.

Ok, so now we know about website users and mobile users. But what about other digital touch-points, like a kiosk?

Other Digital Touch-points

In today’s world a user can interact with your digital content on lots of different devices (computers, mobile, kiosks, set top boxes, etc.). And that can cause a lot of issues as tools try to de-duplicate users and get an accurate count of users.

One of the key features of Universal Analytics is the ability to track users on devices other than websites and mobile devices, things like a point-of-sale system or a kiosk. It does this using a technology called the measurement protocol.

But how does it actually work?

The measurement protocol works by – wait for it – collecting hits :) These are the same hits that I described above. The big difference is that you must manually build the hits. So if you want to implement analytics on a kiosk, you must create MORE code to build the hits that are sent to Google Analytics.

But what about measuring users when you use the measurement protocol?

When you create the hit you must insert a user identifier into the hit. Google Analytics will then use this identifier as the unique identifier when it processes the data.

To measure users when tracking other devices, like a kiosk, you must insert your own identifier and generate your own data hits.

To measure users when tracking other devices, like a kiosk, you must insert your own identifier and generate your own data hits.

Unlike websites and mobile apps, there is no cookie or database to store the identifier. So the ID does not persist from one hit to another, or from one session to another. You must manually insert the identifier into every hit in every session.

Your code must control the generation of the identifier and the storage of the identifier.

Let’s end it there. That’s a pretty good overview of digital analytics data.

I know this was a really geeky post, but it’s an important subject and will become more and more important.

Now it’s your turn. Thoughts? Please feel free to leave a comment.

Hits, Sessions & Users: Understanding Digital Analytics Data is a post from: Analytics Talk by Justin Cutroni

The post Hits, Sessions & Users: Understanding Digital Analytics Data appeared first on Analytics Talk.

How to Use Google Analytics Content Grouping: 4 Business Examples

Content Grouping is a useful feature that let’s you group your website or app content together and view aggregate metrics for each group. This is particularly useful if you have a lot of content to analyze. Rolling up your content, based on your specific business structure, is very helpful when creating dashboards and other custom […]

How to Use Google Analytics Content Grouping: 4 Business Examples is a post from: Analytics Talk by Justin Cutroni

The post How to Use Google Analytics Content Grouping: 4 Business Examples appeared first on Analytics Talk.

Content Grouping is a useful feature that let’s you group your website or app content together and view aggregate metrics for each group. This is particularly useful if you have a lot of content to analyze. Rolling up your content, based on your specific business structure, is very helpful when creating dashboards and other custom reports.

In this post I’ll talk about how to actually use the data and walk through some examples for various business types.

If you have not set up content groupings, please check out my post on how to set up Google Analytics content groupings.

Standard GA Reports

Your content groupings are available in Google Analytics behavior reports. Navigate to the Behavior > Site Content > All Pages report. Notice at the top of the data table there is a selector for the primary dimension. This drop down list all of the content groupings that you added to Google Analytics.

Use the selector to choose a specific content grouping in your Google Analytics Content reports.

Use the selector to choose a specific content grouping in your Google Analytics Content reports.

This selector also exists in the navigation flow, so rather than viewing how users move from page to page, you can view how users move between the different types of content on your site.

You can also use your content groupings in the Navigation Summary report.

You can also use your content groupings in the Navigation Summary report.

Very handy for understanding the behavior of users!

It also exists in many other content reports, like the Landing Pages report and the Site Speed Page Timings report.

But who uses the standard reports these days? :) Analysis driven organisations use Custom Reports and Dashboards. Let’s look at how you can use content groupings in both features.

Custom Reports & Dashboards with Content Groupings

When you create a content grouping, Google Analytics will create a dimension for each content grouping.

Remember, a content grouping contains a number of groups, and each group can contain a number of pages or screens.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

This means that the values for the content grouping dimension will be all of the content groups that you created within that grouping.

You can create up to five content groupings in Google Analytics, therefore you could have five new dimensions, one for each content grouping.

Use the content grouping dimensions just like you would any other dimension. Here’s a simple custom report that shows some a potential content grouping for a blog.

You can use your content groupings in a Google Analytics custom report.

You can use your content groupings in a Google Analytics custom report.

Then, when you look at the report, you’ll see something like this:

When you add a content grouping to a Google Analytics custom report, the data will be aggregated based on content group.

When you add a content grouping to a Google Analytics custom report, the data will be aggregated based on content group.

Note: I added this custom report to the Google Analytics solutions gallery. You can add it directly to your account here.

You can also use the content grouping dimension in your dashboards. Here is a very simple example using the page value metric and the content grouping dimension.

You can also use the Content Grouping dimension in a Google Analytics Custom Dashboard.

You can also use the Content Grouping dimension in a Google Analytics Custom Dashboard.

That’s really all there is to using content grouping in Google Analytics custom reports and custom dashboards. No go and give it a try!

One other note – the content grouping dimensions are hit level dimensions. This means that you can only use them with hit level metrics, like pageview, time on page, etc. You can not use them with session level metrics, like conversion rate, or revenue per visit.

Content Grouping Strategies

To really take advantage of content groupings you need to plan your content grouping carefully. You need to understand how your organization wants to analyze this data. So let’s look at a how different types of businesses might use content grouping.

Ecommerce: Patagonia.com

Patagonia sells outdoor equipment for men, women and children. They’re known for their ethos that you should travel “fast and light” when in the outdoors – take only what you need. They’re also known for their environmental advocacy. They incorporate both of these messages into their marketing stories.

Effectively breaking down the content structure could help each department at Patagonia better understand their marketing initiatives and site optimization efforts.

So how might we create a content grouping strategy based on their business?

Google Analytics Content Grouping can be used to organize the content on an ecommerce website.

Google Analytics Content Grouping can be used to organize the content on an ecommerce website.

Product pages: I would start by grouping all product pages together. It’s really important to understand what percentage of your users are making it to product pages. If people don’t look at product pages then they usually can’t buy something. And I’d take it one step further – group product pages by product line. I’d also be sure to differentiate category pages from the generic product pages.

You can mimic your product architecture with your content groupings.

You can mimic your product architecture with your content groupings.

Special selling tools: One cool feature that the Patagonia site has is the ‘kit builder’. This is a tool that let’s a customer build the best clothing combination for different conditions or activities. This is another section that could really use it’s own content group.

Special shopping tools can be categorized in their own group.

Special shopping tools can be categorized in their own group.

Checkout pages: Next I’d group all checkout pages together. These are all the pages in your checkout process. The percentage of people that see checkout pages might be very small, but I like to put these pages in their own group. They’re not product related, and they’re not marketing related. So they need their own group.

Account management pages: Many ecommerce sites let customers manage account settings, check the status of their order, manage returns, etc. I would lump all of these pages together in an Account Management group.

Marketing pages: Now we get into a large chunk of the content – marketing pages. Patagonia has a lot of information about their brand, and initiatives. Rather than lump all of this together as just Marketing pages, I would actually break all of this up into groups based on the different initiatives.

In the case of Patagonia I would use all of these different groups that you can see in the navigation.

Use a Google Analytics Content Grouping to categories marketing pages.

Use a Google Analytics Content Grouping to categories marketing pages.

Support pages: Business is all about relationships – and that’s represented by different types of support content. We can create a support group that containing any materials related to support. Again, you can create sub-groups for different types of support content (product support, order support, etc.)

Error pages: I like to group all error pages into a single group, then I can drill into the group and view the specific errors. This group can contain all different kinds of errors, depending on your personal preference. It could be technical errors, like 404 or 502 errors. Or it could be more functional errors, like when a user adds an incorrect credit card number during their purchase.

Software as a Service: Mailchimp.com

Mailchimp is a popular service that helps businesses manage their email marketing initiatives. Like all SaaS sites it’s primarily divided into two sections: a marketing section and an application section. The content grouping will mimic this general structure of content.

Product marketing pages: If people are going to sign up for the Mailchimp service then they need to know about the features! Product marketing page are pages dedicated to product information, this includes information about price, features, etc.

For a SaaS site, create groups for different kinds of marketing content.

For a SaaS site, create groups for different kinds of marketing content.

In addition to specific product information, there’s also a lot of thought leadership material to help drive marketing.

Marketing content pages: These pages are non-product marketing pages that help you demonstrate your thought leadership. It may be blog pages, or other content. In our example of mailchimp.com, there might be multiple groups. For example, they have a blog, but they also have a ton of research about email marketing. I would put this material in a marketing content group. Or even better, in the Reports group!

I would create a Google Analytics content group for the research reports on the MailChimp site.

I would create a Google Analytics content group for the research reports on the MailChimp site.

Application pages: The other side to a SaaS site is the actual application. This is the section of the site where you log in and actually use the product. Like the marketing pages, there can be many different types of application pages. Let’s go back to our example of Mailchimp.com. I would break down the content based on product features.

Perhaps we could use the application navigation as a template for the content structure.

You can create different groups for each part of the online application.

You can create different groups for each part of the online application.

Account management pages: Here’s another example of grouping different parts of the application together. We could easily group together the pages that control account management. And you can see from the image above that there are sections of the app dedicated to other functionality – all should be grouped accordingly.

Error pages: Like other types of sites it’s a good idea to group all error pages together. See the ecommerce section above for more details. These groups can be both website errors or application errors – like a login error page.

Gaming Application: Clash of Clans

We all use our mobile devices for incredibly important things, like waging medieval warfare on other clans! HA! Anyone out there like Clash of the Clans?

You can categorize app content using Google Analytics Content Groupings.

You can categorize app content using Google Analytics Content Groupings.

In reality, gaming apps are very similar to other business models – like publishing and commerce. Some games generate revenue from in-game ads while others up-sell users on features, like new levels. Some do both. We can group games content together just like we do ecommerce.

Game level screens: Most of the content for a game is probably level based. We can replicate this base structure in Google Analytics. If you’re a fan of Clash of the Clans then you there are other parts to the game in addition to levels. There are attack screens, chat windows, etc. All of these screens can be added to groups to roll-up the data.

Ecommerce screens: These screens are used to sell the user on pay features. In the case of Clash of Clans you can buy more gems, which can then be used to purchase other items, like more armies!

I would put all ecommerce app screens into a separate content group.

I would put all ecommerce app screens into a separate content group.

Configuration screens: Most apps have a configuration section. This is where the user can change everything from the language, to colors, etc.

Error screens: Last but not least we have error screens. Again, these can be technical app errors or functional errors, like login issues.

For Publishers: MarketingLand.com

Let’s face it, content grouping was made for the publishing industry! They’re the ones that have to organize thousands of pages of content. I don’t want to dwell on publishing too much, but let’s take a look at MarketingLand.com, a popular destination for anyone working in the digital marketing world.

I’ve actually written about how to customize google analytics for publishing sites in the posts Custom Variables for Publishers and how to measure how far users scroll down a page. I think both of those techniques still apply.

But now, if you’re a publisher, you can also use content groupings to organize the data about your content. This provides one more way to roll up data for analysis.

Content Category: Almost all publishers group content by category – and now this can be done with the content grouping feature.

Publishers can create content groups based on the organization of their content.

Publishers can create content groups based on the organization of their content.

Some publishing sites organize content in other ways, like by author or publication date. I would suggest creating content groups for topic categorization, and custom dimensions for any secondary organization (author, date, etc.)

Account pages: Some publishers, like the New York Times, offer a premium membership service. This is not the case with MarketingLand.com. But, if it did have a member’s section, you could group all of those pages together.

Error pages: Do I need to go over this again :)

I hope this post provides some inspiration for how you might use Content Grouping for your business. Ultimately how you organize your content groupings will be based on your organization. There is no right or wrong – just use a structure that is useful.

Questions or comment? Leave a note below – and happy grouping!

How to Use Google Analytics Content Grouping: 4 Business Examples is a post from: Analytics Talk by Justin Cutroni

The post How to Use Google Analytics Content Grouping: 4 Business Examples appeared first on Analytics Talk.

How to Set Up Google Analytics Content Grouping

Today everyone is creating content – lots and lots of content. Measuring that content can be a challenge given the sheer volume that’s out there. That’s where Google Analytics Content Grouping can help. This feature let’s you categorize your content based on your own business rules. Then, rather than view your data based on page […]

How to Set Up Google Analytics Content Grouping is a post from: Analytics Talk by Justin Cutroni

The post How to Set Up Google Analytics Content Grouping appeared first on Analytics Talk.

Today everyone is creating content – lots and lots of content. Measuring that content can be a challenge given the sheer volume that’s out there. That’s where Google Analytics Content Grouping can help.

This feature let’s you categorize your content based on your own business rules. Then, rather than view your data based on page URL or screen name, you can view based on your specific groups.

In this post I’m going to talk about how content grouping works and how you set it up.

Key Vocabulary: Groupings and Groups

There is a little terminology we need to cover before we get into the setup: groupings and groups.

You can create multiple content groupings in Google Analytics.

Within a grouping you can create multiple content groups.

A group is a collection of content. It could be pages in a certain section of your website. Or it might be screens from a certain part of your app. It can be just about anything.

A grouping is just a bunch of groups.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

You can create multiple content groupings in Google Analytics and switch between them in the reports.

Here’s an example. For my blog I created a grouping called Blog Content Categories.

Within that grouping I create a number of groups to categorize the different types of content on my blog. There’s a group for posts, a group for about me pages, a group for error pages, etc. In the configuration I created a rule that puts each page in a group based on the structure of the URL.

You can view your content data based on groups, rather than URL, screen name or title.

You can view your content data based on groups, rather than URL, screen name or title.

Any item that is not added to a group will appear in the (not set) content group.

It’s important to know that there is not one specific report where you access this data. When you create a grouping it’s literally becomes a new dimension of data. You choose to view that dimension in almost all of the content reports.

Let’s take a look at how you actually create a grouping and groups.

Creating Groupings & Groups

Google Analytics does not automatically create content groupings – you must configure the tool to do that. Navigate to the settings for a specific view and choose Content Groupings.

Content Grouping is a view level setting.

Content Grouping is a view level setting.

Here you will see a list of all your groupings. You can choose to create a new group or edit an existing group.

Here's a list of your Google Analytics content groupings. You can add or edit groupings here.

Here’s a list of your Google Analytics content groupings. You can add or edit groupings here.

There are three methods you can use to create a content group – let’s take a look at each.

Tracking Code Method

This method requires you to add a small piece of code to each page on your site or in your app. The code will literally set the name of the content group when the page or screen renders. Here’s how the code would look for Universal Analytics:

ga('create', 'UA-XXXXXXXX-Y', 'example.com');
ga('set', 'contentGroup5', 'Group Name');
ga('send', 'pageview');

Or, if you’re working in iOS the code might look like this:

id tracker = [[GAI sharedInstance] trackerWithTrackingId:@"UA-XXXX-Y"];
[tracker set:[GAIFields contentGroupForIndex:5]
value:@"Group Name"];

The code for a content group is similar to the code for a custom dimension. You can set 5 content groups using the tracking code. Each group is associated with a number, one through five, as shown in the example above.

Check the Google Analytics support documentation for more code examples.

Basically this method let’s you suck in the group name, via code, from some other system. It might be a CMS, a data layer, or just the HTML of the page.

The key is that you somehow add the name of the group to the Google Analytics code.

Pros: Using the tracking code method you can use code to automatically adjust to changes in your content and new content groups.

Cons: It requires IT involvement to set up. But once it’s configured very little IT support.

I should also mention that content grouping is coming to Google Tag Manager. This will provide another way to programmatically set the content group – so stay tuned.

Extraction Method

The extraction method extracts (get it) the name of your content groups from an existing dimension of data. The idea is that you use a regular expression to parse the dimension and automatically extract the name of your group.

For example, the name of your content groups might be in the page title, like this:

Your website might use the name of the content in the Page Title or Screen Name dimension.

Your website might use the name of the content in the Page Title or Screen Name dimension.

I would need to specify that my group name is in the Page Title dimension, and then provide a regular expression that extracts the appropriate value.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

For those of you that do not use regular expression, the value in the parenthesis will automatically be extracted. Google Analytics will then use the value as the group name.

You can see that this one rule will work for every product page on my site – as long as they are well formatted.

Pros: No coding involved. Flexible collection.

Cons: You might need to update your regular expressions when you add new content to your site or app. Specifically something that does not match your existing rules. Believe me – updating settings SUCKS. People forget to do it all the time.

In you’re new to regular expressions check out this reg ex tutorial in the Google Analytics help center.

Rules Method

The rules method is almost exactly like the extract method. The ONLY difference is that you have to MANUALLY name the group. The value for the name is not automatically pulled from a dimension of data.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

Like the extract method you can create rules based on different dimensions of data- the page title, page url or the screen name. If the dimension value matches the rule then the content is added to the group.

Pros: No coding. Don’t need to know regular expressions.

Cons: You need to remember to update your rules when you add new content or if your site urls or app screen names change. Again – updating your analytics settings SUCKS. People forget to do it all the time.

Which method should you use?

That’s a tough question. Personally, I think page category is a critical piece of data that should be added to a page data layer. If you take this approach then using the tracking code method is very scalable.

I also like the extract method. It’s very flexible and reliable – as long as you have processes in place to maintain your implementation :)

Important things to know

Ok, so here are a few very important things to know.

You can use all three methods for creating groups within the same content grouping.

The grouping logic is applied to your data sequentially. That means that Google Analytics first applies the tracking code method first. Then it applies the extraction method. And finally it applies the rules method. You can use all three methods for your implementation.

When a page or screen matches a rule it is added to that group.

A page or screen can only be in ONE content group at a time! That means that an page or screen can only belong to one group at a time.

And finally, content groups are NOT applied to historical data. They are only applied from the moment you configure the feature.

A Best Practice

Because Google Analytics applies all grouping methods to your data, it is possible to use a combination of grouping methods.

But, because they they are applied SEQUENTIALLY, it’s a good idea to put your very specific grouping rules first, followed by your general rules. This way the later, general rules will catch anything that slips through the early, specific rules.

Content Group methods are applied sequentially.

All three content grouping methods are applied to each piece of content. They are applied sequentially.

It’s really, really important to try and get your groups right the first time. While you can edit your groups, there is no way to change the data that has already been processed.

Make sure you test your groups first before announcing them to your entire team.

It’s also a good idea to add an annotation to Google Analytics so everyone knows when the data was added.

Ok, I think that’s it for how to implement this feature.

Don’t worry – I’ll explain how to use content groups in a couple of days.

How to Set Up Google Analytics Content Grouping is a post from: Analytics Talk by Justin Cutroni

The post How to Set Up Google Analytics Content Grouping appeared first on Analytics Talk.