Funnels are visual representations of the path visitors took through a process on your website. Instead of looking at text, you can see images of exactly what happened on a visitor’s path to completing a goal that you’ve configured in your Analytics account.

Finding Funnel Reports in Google Analytics

You must have a goal configured, running and collecting data in order to see that data in a funnel visualization. Funnel reports can be found under Conversions -> Goals -> Funnel Visualization. You can also view multi channel funnels under conversions.

Configuring Funnel Reports in Google Analytics

At the time of this writing, the detail of your funnel report and how it will look is directly related to goals in Google Analytics and how you configure them. With the old interface, this was sort of ambiguous, but Google’s new interface has a button that you can click to turn on the funnel option.

Once this has been enabled, you can enter steps for your goal. The data that you enter into the steps of your goal (i.e. name and URL) is that which will appear on funnel visualization reports. Note that if you name your step something and later on in the report you don’t like the way it looks, you can always rename it, and it will dynamically change on the report itself.

Be careful which steps you label as required. Labeling a step as required means that it has to happen or Analytics will not record the goal, and it will not be included in your funnel visualization report.

Interpreting Funnel Reports in Google Analytics

Funnel visualizations show you a lot of data in a condensed image. Along with the conversion rate and overall visitors that completed the funnel, you can also see:

• The number of visitors who entered the funnel on the page where the funnel began
• The other pages that a visitor came from to start the funnel
• The number of visitors that did not complete the funnel
• The point in the funnel where various visitors may have abandoned the process
• Segmented groups of visitors and the pages they traveled to after abandoning the funnel
• The number of visitors who successfully completed the funnel

You can view all of this data for either a specified time period or for the entire duration of time that the goal has been active and running.

Useful Data

Funnel Abandonment

Goals and funnel visualizations are so useful in Google Analytics because they give us a way to see if our online processes are working or not. Every funnel has some kind of abandonment rate, whether it’s a transaction funnel, a signup process, an account-creation process, etc.  At some point, a visitor either decides they don’t want to or cannot complete a process, or there is some technical issue that does not let them complete the process.

With a funnel visualization, you can see areas of concern in multi step funnels. For instance, maybe a lot of people abandon the funnel at the very beginning of the process. Common reasons for this are long or ambiguous forms, confusing checkout processes, bad page design or slow load times, among other issues.

Perhaps you see that visitors are abandoning ecommerce transactions just before they are complete. Maybe your process is seamless, but visitors don’t feel your site is secure. Or maybe there is some technical issue with that part of the process.

Looking at pages that visitors diverted to after abandoning a funnel can also give you some clues as to what they are doing. Maybe you see visitors going to a help page or an FAQ page from the funnel; signs that they could be looking for guidance on how to complete a process.

Referring Pages

You can also see the pages that sent traffic to the start of your funnel process in your funnel visualization report. This data can indicate that calls to action on certain pages, internal ads, links or other sources are doing their job. It could also indicate that one page is better suited to help assist in completing conversions for whatever reason.

Goals and funnel visualizations give you data that you wouldn’t otherwise have and make it easy to read. They help you identify problems with processes on your website, and they also give you visual proof of techniques that might be working out well.

 

Do you use funnels in Google Analytics? In what ways do they help you measure conversions? Let us know by dropping a line or leaving a comment below.

Sometimes it is necessary to track specific actions on a website in order to learn things. Goals in Google Analytics are the perfect tool for doing that. Using goals, webmasters can track things like when someone landed on a specific page, how long that person stayed there, and events that happened on their website. Goals track these occurrences and display them in reports as an integer each time they happen so webmasters can keep track of how many times the action was completed. Goal reports also display data in a visually appealing and easy-to-understand way.

What Can I Track with Goals in Google Analytics?

With goals, you can track destination pages, visit duration, pages/screens per visit or an event.

Destination:  A common use of the destination goal is to track when someone lands on a confirmation page. For instance, you may have advertising out on the web that leads visitors through a conversion funnel. The funnel may end at a final web page (such as a thank-you or confirmation page) where the funnel is complete. By using goals to track when users hit these pages, you can determine the success of your advertising and conversion funnel.

Duration:  This goal triggers when a visitor has spent a specified amount of time on a page. For instance, maybe you have blog pages, and you want to determine if visitors are spending time on those pages. You might set a goal to record if a visitor spends more than 5 minutes on the page in order to determine if you are meeting content or SEO related goals. Conversely, you may want to know if people are having trouble with a form page. You could set a goal to record if they are spending too long on the page. A high number might suggest that the form needs to be redesigned.

Pages/Screens per visit:  This goal is useful for tracking how many pages someone looked at per visit. You may want people to travel deeper into your site.  This goal type would help you determine if any changes you made were effective at doing that.

Event:  It used to be you were not able to track events as goals in Analytics. Events are simply user interactions that happen on a web page, but that cannot be recorded by the Analytics tracking code because they do not trigger a pageview. Things like video views, clicks on external links and downloads are examples of actions that are recorded as events. In the past, you would have to trick Google into thinking a pageview had happened in order to track the event as a goal. This was a complex workaround, and it also inflated pageviews artificially. Now you can track events as goals instead.

Setting Up Goals in Google Analytics

1. Log into your Google Analtyics account
2. Click the Admin tab near the top of the page
3. Click on the profile that you want to create the goal in
4. Click on “create a goal”
5. At the time of this writing, you will be prompted to choose from one of many pre populated goal templates. These are simply pre named templates that automatically select the radio button for the goal type in the next step based on the nature of the action. You can change the radio button selection in the next step, if necessary.
6. If the templates don’t fit your purposes, select “custom” and click “next”
7. In the next step, you will either confirm your goal type (for templates) or choose one (for custom)
8. Select the appropriate goal type based on what you are measuring
9. The next steps will vary based on the goal type that you choose. For URL destinations, you will have to enter the URL of the page. For events, you will have to enter the category, action, label and value of the event.
10. After you are finished, save your goal

To view data from goals in reports, go to standard reporting and then Conversions->Goals. Here, you can look at an overview of all the goals you have running, goal URLs if you have specified a page, reverse goal paths, which show how users achieved the goal, and funnel visualization reports that give you a graphical representation of how users completed a goal or abandoned the funnel. The goal flow report gives you another graphical representation of the process, but it is a little more interactive.

Goals are a great way to measure specific actions on a website.  They have advanced to the point where you don’t need to use virtual pageviews in order to record goals that don’t trigger the Analytics tracking code

Are you using goals in Google Analytics? What has your experience been like? Let us know by dropping us a line or leaving a comment below.

Sometimes you just want to see one piece of information about your website and not the onslaught of data provided by Google Analytics. You could filter your data (which is nice), but that has its own caveats. Advanced segments, on the other hand, are an excellent way of isolating subgroups of information about your website for analysis. There are lots of ready-made segments, and you can also create your own.

What Are Advanced Segments in Google Analytics?

As mentioned before, an advanced segment is a setting that can be configured in a profile in your Google Analytics account to exclude other data and only look at one specific metric. For instance, using common default segments, you can view only direct traffic or only search traffic to a site. Using custom segments, you could do things like only see traffic related to mobile devices. Advanced segments are useful because you can cut out other data and only look at what is important to you. You can also compare that data to the site overall and other metrics, plus (even though you may have just created a segment) they can be applied historically; this is a feature not available when you create a filter on your profile.

Advanced Segments vs. Filters in Google Analytics

Although advanced segments are essentially filters that use logical expressions to exclude data, they are much different than the filters that can be applied to profiles.

Historical reports: One of the most notable differences with advanced segments compared with filters is that you can apply them to historical data. When you set up a filter on a profile, this is not possible because the only data the filter is affecting is that which is collected after the filter has been implemented. Conversely, advanced segments can be applied retroactively on profile data. In this way, they are far more powerful than a regular filter because you can see your trends over time. The only drawback is that you have to reapply the filter each time you leave and come back to your Analytics account.

Comparing data: By selecting more than one segment at a time, you can compare the metrics together in reports. This is something that cannot be done by applying a filter to an entire profile. With profile filters, data that has been marked for exclusion is thrown out and is no longer retrievable.

Excluding traffic: An area where filters can be more useful than advanced segments is with excluding traffic. For instance, many businesses will exclude their own internal traffic to get a more accurate picture of prospects visiting their website. There may be visits that you want in reports all the time or ones that you don’t. Whatever the case may be, remembering to always manipulate this traffic data with advanced segments can be a pain, and that is where filters come in handy.

User Roles: Filters are also better for managing user roles. For instance, you may have team members working on a specific area of a site that they only need to see certain data for. In this regard, filters can exclude that data or manipulate it in other ways that are permanent and don’t require those team members to see all data.

Using Advanced Segments in Google Analytics

Default Segments

Using advanced segments is super easy. Follow the steps below to use default advanced segments.

1. Log into your Google Analytics account.
2. Click on the profile that you want to view data for.
3. Navigate to a report that you want to view data for (note that if you are just playing around with segments, you can do it in any report).
4. Click on “Advanced Segments” near the top of the page.
   
5. A menu will drop down showing default segments on the left and any custom segments on the right. Note that custom segments are created at the account level. Even if you have been made a user on another Analytics property, as long as you are viewing a property in your account, the segments for that account will appear regardless of whether or not they will work.
   
6. Click on any segment that you want to view data for. Note that by clicking on more than one segment, you will be viewing data in side-by-side comparison and not in aggregation.
7. Click “Apply”.

Custom Segments

Customized segments are a little different. They work in the same way that the default segments do; however, they are created by the user and are very versatile. For instance, you can use customized code on your web page to capture information and then use it in advanced segments like this code to capture visits where people were logged into one or more popular social media accounts.

Setting up a custom segment

1. Log into your Analytics account.
2. Click on the profile where you want to create the advanced segment.
3. Click on the “Advanced Segments” button. 
4. On the right hand side of the dialogue box that appears, click “+New Custom Segment”.
   
5. Next, you will have to set the parameters for your custom segment. This includes whether or not you want to include or exclude data, what data that is and the logic you want Google Analytics to use in finding that data. Note that after you create the segment, you can test it by clicking the “preview” or “test segment” buttons. Analytics will then apply the segment to your data. If you don’t get the desired result, you can tweak it.
   
6. If you are satisfied with your segment, click “Save Segment”.

Unlike filters on profiles, you can also add “and/or” statements to your advanced segments. This means that when the segment is applied, Google can include other data that you have specified in the segment as opposed to throwing everything out and only looking for one set of data that was entered.

Advanced segments in Google Analytics are a nice tool for looking at subsets of data where filters don’t quite do the job or are overkill. You can choose from one of many default segments or you can create your own. The coolest part is that you can use your own custom programming to create data to be used by advanced segments.

How do you use advanced segments? Do you have any innovative tutorials to share that incorporate advanced segments? Join the conversation by commenting below.

Regular expressions (RegEx) have some useful applications in Google Analytics. You can use them to create more accurate and versatile tracking scenarios than are available by default on the platform. Regular expressions are essentially special characters that instruct Analytics on which sets of data to capture and record. This post will highlight some scenarios where regular expressions are useful.

Regular Expressions in Google Analytics Goals

You can utilize a RegEx when setting up a goal in Analytics. Using this method for a goal is best when the URL is variable. Head match is another good option, but the regular expressions gives you a bit more control if implemented properly. The cool thing about regular expressions with goal URLs is that you can write your regular expression to match any part of the URL exactly and the rest of it to be variable. For instance, using the dot . matches any single character, the carat ^ tells analytics that your data must be at the beginning of the field and the * tells analytics to match zero or more of the preceding character. If you wanted to match a URL that always began with /regex but was always variable after that term, you could write ^/regex\./.*.  Note that the . after the word regex has a back slash before it. This tells Analytics that the dot is actually a dot and not part of the expression. The period after the star is actually ending my sentence. The characters .* tells Analytics to match anything.

To use regular expressions in goals:

1. Log into your Analytics account.
2. Click on the Admin button in the top right corner.
3. Click the profile you would like to create a goal for.
4. Click the goals tab.
5. Click +Goal.
6. Select URL destination for the Goal Type.
7. Make sure you select regular expression match from the Match Type drop down menu.

Excluding IP Addresses from Analytics Data

Another use for regular expressions is to exclude data from being collected based on user IP address. This is a common filter in Analytics that allows users to exclude things like their own machine or perhaps a contractor’s machine if there is work being performed to a website. Another reasonable use is the desire to exclude employee traffic from a profile so that more accurate data can be gleaned from a website used by a business. Businesses, especially larger ones, utilize dozens, hundreds and even thousands of computers. Entering in an IP address for every single machine is not practical, and also would not be effective. For these purposes, a range of IP addresses written as a regular expression works to exclude employee or other unwanted traffic from reports.

You can use all of the same regular expression rules that you would use for capturing specific URLs in your goal above; however, Google has a really great tool if you are excluding one or more IP addresses from data. It’s called the IP address range tool, and all you have to do is enter in at least one IP address and click the “generate RegEx” button. You then simply copy and paste the snippet of characters into your filter field.

 

To set up a filter excluding one or more IP addresses:

1. Log into your Analytics account.
2. Click the Admin tab in the top right hand corner of the page.
3. Select the profile that you want to set your filter up in.
4. Click the filters tab.
5. Click the +New Filter button.
6. Create new filter will be selected by default.
7. Select the Custom Filter radio button.
8. Select the Exclude radio button.
9. In the Filter Field drop down menu select IP Address
10. Paste your RegEx from the range tool into the Filter Pattern box.
11. Make sure the “no” radio button is selected for Case Sensitive.
12. Click save.

Be sure you are applying this filter to a copy of your default profile. You never want to apply filters to the default profile. Always apply filters to copies of the default profile. That way, if something is done wrong or, for some reason, you want to look at the data again, it will still be there.

Excluding Data in Reports

Another handy use for the RegEx is to exclude or include specific URL or other data from reports. This trick is cool because you aren’t actually changing any of your data — you’re just telling Analytics to do it for that moment in that specific report. After you leave that report or Analytics in general, your reports go back to normal. For instance, in your content reports, you just click on the advanced dimension filter and let Analytics know which pages you would like to see or not see in the report.

To use regular expressions to exclude URLs from content reports:

1. Log in to Analytics.
2. Select the content report that you want to manipulate.
3. Click on the link that says Advanced at the top of the report.
4. Choose include or exclude depending on your purposes.
5. Note that you must use a Dimension in order to use a RegEx.
6. Make sure “Matching RegExp” is selected.
7. Write your regular expression in the box and then click Apply.

This use of the RegEx is particularly useful when sifting through many URLs that may be very similar. You can then only view the ones that have a particular parameter in them, for example. Note that you can add more dimensions and other regular expressions if you wish; however, you must remember how Analytics applies these. It will apply the filters in the order in which they are applied to the report (the first one first). Subsequent filters may not work if you have thrown the data out or told Analytics to keep it using the first filter in the series.

Writing regular expressions can be very confusing at first. It is almost like learning a new language, but is even more cumbersome because they use characters that already have alternative (and well-worn) meanings for us. There are tons of great resources on the web for learning about regular expressions. Remember to create duplicate profiles in your Analytics account so that if you make a mistake or want your data back when dealing with filters, it will still be around.

You can learn more about using regular expressions in Google Analytics here.

How do you use regular expressions in Analytics? Do you find them confusing or have you mastered them already? Let us know what you think by leaving a comment or dropping us a line.

 

The default setup of Google Analytics provides lots of insightful data; however, business owners need to know more about their websites. In particular, they want to see data directly related to their bottom line. This is data that the default data in reports can’t provide. Webmasters can enable eCommerce tracking to get in-depth data about monetary transactions happening on their website.

What is eCommerce Tracking?

This tracking configuration for Analytics is a combination of profile settings and code deployed on a website in order to track eCommerce transactions. Normally, when a visitor comes to your site, a pageview is recorded by the Analytics tracking code. This data is then sent to Google to be processed. With eCommerce tracking, instead of the pageview data being processed, eCommerce data is sent to Analytics. Google gets this data by individual website owners using a collection of tracking methods installed manually on elements of a website.

ECommerce tracking is used for (you guessed it) keeping track of transactions on a website. Important data related to transactions can be captured and reported by Analytics. Things like transaction numbers, product numbers, product descriptions, shipping information, purchase price, tax information, store name and affiliate ID are all examples of data that can be captured. This tracking configuration is also instrumental for measuring remarketing success.

Using data from eCommerce tracking, you can make correlations among the elements of your site and visitor behavior. For instance, you can determine if your price point is appropriate or if you are charging too much or too little for shipping. You may be able to infer if your checkout process is too long or if the product pages on your site are too difficult to use. Of course, these are just examples, and you must make your own assumptions based on the products or services that you sell and your own website configuration.

Enabling eCommerce Tracking

ECommerce tracking is not enabled by default, so you must turn it on in the profile of your choice. Follow the steps below to enable eCommerce tracking.

1. Log in to your Analytics account
2. Click on the “admin” tab in the top right corner of the screen
3. Choose the profile that you wish to enable eCommerce tracking on
4. Click on “profile settings”
5. For the section that says “eCommerce site?” select yes
6. Click “apply settings”

You now have eCommerce tracking enabled, but there is some more in-depth work ahead. You also have to install additional tracking code on your site. For these steps, you must have access to the source code of your website. If your website is administered by someone else, you can provide the code to them and instruct them where to apply it.

Adding eCommerce Tracking Code to Your Website

 

Note:  Make sure to inspect which version of the Anlaytics tracking code you are using as methods for installing eCommerce code will vary depending on what you are using.

 

eCommerce Tracking code (ga.js)

 

In most cases, you will configure your code on the final page of an eCommerce transaction to send data to Analytics. Here is a complete example of how eCommerce tracking might look on your HTML “thank you” page. Note that the code example below is the asynchronous code, and is installed in the head section of the page.

 

<html>

<head>

<title>Your eCommerce Thank You Page</title>

<script type=”text/javascript”>

 

var _gaq = _gaq || [];

_gaq.push([‘_setAccount’, ‘UA-XXXXX-X’]);

_gaq.push([‘_trackPageview’]);

_gaq.push([‘_addTrans’,                  //This is your tracking object.  There are many different kinds

‘3456’,           // transaction ID – required (This is a required element of your tracking code)

‘Your Business’,  // affiliation or store name

‘13.99’,          // total – required (So is this)

‘1.29’,           // tax (Note that everything after the preceding line is not required; however, the more information you can provide, the better it is for your overall tracking efforts.)

‘5’,              // shipping

‘a city’,       // city

‘a state’,     // state or province

‘USA’             // country

]);

 

// add item might be called for every item in the shopping cart

// where your eCommerce engine loops through each item in the cart and

// prints out _addItem for each

_gaq.push([‘_addItem’,

‘1234’,           // transaction ID – required

‘DD44′,           // SKU/code – required

‘your item’,        // product name

‘description of item’,   // category or variation

‘price of item’,          // unit price – required

‘some quantity’               // quantity – required

]);

_gaq.push([‘_trackTrans’]); //submits transaction to the Analytics servers

 

(function() {

var ga = document.createElement(‘script’); ga.type = ‘text/javascript'; ga.async = true;

ga.src = (‘https:’ == document.location.protocol ? ‘https://ssl’ : ‘http://www’) + ‘.google-analytics.com/ga.js';

var s = document.getElementsByTagName(‘script’)[0]; s.parentNode.insertBefore(ga, s);

})();

 

</script>

</head>

<body>

 

This is where the content of your page will go. Note that all the tracking code goes in the head section of your web page.

 

</body>

</html>

Note that the first function _addTrans() initializes or starts the transaction. The second function _addItem() associates an item with the transaction by way of the ID number. The last function _trackTrans() sends the information to the Analytics servers to be processed. These functions must be placed in this order to work. For example, you cannot send information to Analytics before it is stored by _addTrans().

Another notable element of the code is the presence of _trackPageview(). Earlier, we talked about how eCommerce tracking sends transaction data and not pageview data to Analytics servers. While this is true, it is a best practice to include the _trackPageview() function so that you can track your eCommerce pages as you would any other page on your website. Should you exclude this from your page, you would not be able to associate transaction data with other reports in Analytics.

This is a basic example similar to the one provided by Google in its eCommerce tracking tutorial. There could be many different ways you set up eCommerce tracking depending on your particular configuration.

ECommerce Tracking (analytics.js)

For analytics.js, you must load the eCommerce plugin (which speeds everything up by not loading the entire analytics.js library). You do this as follows:

ga(‘require’,’ecommerce’,’ecommerce.js’)

Installing the analytics.js version is a bit trickier, and requires a little bit of PHP knowledge. A common snippet might look something like this:

 

<?php

// Function to return the JavaScript representation of a TransactionData object.  This is so Analytics can interpret the data.

function getTransactionJs(&$trans) {

return <<<HTML

ga(‘ecommerce:addTransaction’, {

‘id': ‘{$trans[‘id’]}’,

‘affiliation': ‘{$trans[‘affiliation’]}’,

‘revenue': ‘{$trans[‘revenue’]}’,

‘shipping': ‘{$trans[‘shipping’]}’,

‘tax': ‘{$trans[‘tax’]}’

});

HTML;

}

 

// Function to return the JavaScript representation of an ItemData object.

function getItemJs(&$transId, &$item) {

return <<<HTML

ga(‘ecommerce:addItem’, {

‘id': ‘$transId’,

‘name': ‘{$item[‘name’]}’,

‘sku': ‘{$item[‘sku’]}’,

‘category': ‘{$item[‘category’]}’,

‘price': ‘{$item[‘price’]}‘,

‘quantity': ‘{$item[‘quantity’]}’

});

HTML;

}

?>

 

Then, in a script tag, output the data for the transaction:

 

<!– Begin HTML –>

<script>

ga(‘require’, ‘ecommerce’, ‘ecommerce.js’);

<?php

echo getTransactionJs($trans);

foreach ($items as &$item)

{

echo getItemJs($trans[‘id’], $item);

}

?>

ga(‘ecommerce:send’);

</script>

 

Notice how the syntax is different. In the first example, functions were written as _addTrans() and _trackTrans(). In analytics.js, they are called with ga().

 

Viewing eCommerce Data in Analytics Reports

To look at the data you have collected, log in to Analytics and go to the profile where you have eCommerce tracking enabled. Under “conversions” go to “eCommerce.” There, you can see an overview report, product and sales performance, transaction data and time to purchase data. You can do all the same things to these reports as others in Analytics, such as exporting or filtering them.

ECommerce tracking is a great way to get more detailed data for transactions happening on your website. The default setup for Analytics does not provide this information, and with a little copying and pasting of code into your website, you can unlock a whole world of information about your business. Using that data, you can improve your sale processes, product pages and your overall online business activity.

Do you use eCommerce tracking?  How do you use it to make your online business better? Let us know what you think in the comments section below.

Tracking your ad performance is important on a number of levels, and it should be a part of any strategic PPC plan. You need to figure out if the money you’re spending on ads is worth it, if your campaigns are as effective as they could be and tagging automatically or manually on your URLs supports more detailed reporting in Analytics and AdWords accounts. This post will show you how to enable and use this feature in AdWords and Analytics.

Options for tagging your URLs

When you set up tagging, you have the option to either enable auto-tagging or tag your URLs manually. There are pros and cons to both approaches.

Auto-tagging

• Less work:  You won’t have to manually tag all the URLs you want to track
• No errors
• Discrepancies in data from using both auto-tagging and manual tagging
• More detailed reporting in Analytics
• This method provides many more data dimensions than manual tagging

Manual Tagging

• Can be used on websites that do not allow for arbitrary URL parameters:  Some websites display an error page when the URL used to reach the page contains parameters that do not match the destination file
• When you want to customize reports with specific data

In most cases, Google recommends to its users that they enable auto-tagging. This feature provides a wealth of data on your AdWords account activity and keywords in your Analytics reports. You’ll be able to see the match type, the ad group associated with the visit or conversion, the destination URL, the network the ad was on (whether search or display), as well as the placement domain if it was the display network.

Enabling Auto-Tagging in Google Analytics

Before you can enable auto-tagging on your ads, you must link your Google AdWords and Google Analytics accounts so that data can be shared. This also assumes that you have an Analytics property set up and collecting data on the website you are advertising.

1. Log into your AdWords account
2. Click the tools and analysis tab
3. Click Google Analytics
4. Click the Admin tab
5. Select the Analytics account you want to link your AdWords account to.
6. Click the Data Sources tab and then click the link accounts button.

Conversely, you do not have to log into your AdWords account to gain access to Analytics. You can simply visit google.com/analytics and follow steps 4-6 above.

Benefits of URL tagging

Auto-tagging your URLs allows data to be pulled into Analytics from your AdWords account. This opens up a wealth of information from your AdWords campaigns that can be viewed in your Analytics reports. You can also see what people did after they clicked on your ads and came to your website. Google does this using the gclid parameter appended to the end of the URL that is your landing page. For example, an appended URL might look something like this:

 

www.example.com/?gclid=86578hgyu

 

Naturally, the characters after gclid will be randomly generated, and this is the parameter that allows AdWords and Analytics to share information with each other.

Checking to See if Auto-Tagging Will Work on Your Site

There are some scenarios where auto-tagging will not work on a website. For instance, server-side redirects can cause parameters to be dropped resulting in missing visit and CPC data. You can test whether auto-tagging will work on your website before it is implemented.

A simple test on a URL that is redirected:

• Take the destination URL from your AdWords campaign and paste it into a browser
• Append a test parameter as follows to the end of the URL: ?gclid=123-abCD
• Hit enter or click the browser’s go button for an address entry. If the parameter above disappears, it was lost due to the redirect
  • Note:  If you are using a landing page that is not redirected, you shouldn’t have to worry about this phenomenon. Also, if you already have parameters in your URL that begin with ?, you should use the & symbol instead of the ? after your initial parameter but before any manually applied URL tagging
  • You can troubleshoot redirect problems with auto-tagging here.

Viewing Your Data in Analytics Reports

Once you have some data accumulated, simply log into your Analytics account to look at it. You can find data related to AdWords reports under Audience -> Advertising -> Adwords. You can see overview progress for any campaigns you have running, keyword data related to your campaigns, and data related to your destination URLs among other metrics. One of the most powerful aspects of these reports is your ability to view keyword performance in relation to ad position in search engine results pages. From these reports you can determine whether some keywords are more effective at enticing users to click than others. You can also determine if users who clicked on your ads found your website content relevant by using metrics like time on site and the number of pages visited.

Importing AdWords data into Analytics is a great way to get deeper insight into how your campaigns are performing. Google can do this easily through auto-tagging on AdWords campaigns. Remember to test your URL parameters before setting things up so you can be sure that data is aggregating appropriately in your account.

 

How do you use auto-tagging to improve your ad performance? Do you know of any benefits to manually tagging your campaign URLs that aren’t offered with auto-tagging?

 

When important things change in your life, you usually want to know as soon as possible. When you use your website as a source of income, knowing when activity changes enough to make an impact on your earnings is very important. If you use Google Analytics for tracking, you can set up standard and/or custom alerts to let you know if there are positive or negative changes occurring when you aren’t looking. Alerts also enable you to not look at your account so frequently while still maintaining some peace of mind. This post will show you the different types of automatic and custom alerts, as well as how to configure them.

Intelligence Events and Alerts in Analytics

Google Analytics intelligence reports are generated by an algorithm that measures changes in website data. Interactions that are measured include metrics like percentage of new visits, bounce rates, average visit duration, clicks, events and many other interactions. Intelligence events are already set up in Analytics, and there is no need to configure them. You can find intelligence events reports by clicking on the “intelligence events” menu in the right column of your standard reports. Here, you can view your overall events as well as daily, weekly and monthly events.

Automatic Alerts in Google Analytics

Automatic alerts are default alerts in Google Analytics intelligence reports that tell you when there have been significant changes in data. For instance, you may have a spike in visitors from a part of the world that you never had visitors from before. Your bounce rate may have increased or decreased by substantial amounts on specific pages. Alerts are designed to catch your attention so that you can investigate further. The cool thing about auto alerts is that they are set up by default, and you don’t have to do anything special. Google is already monitoring your website all the time for changes that could be of interest to you.

Automatic alerts can be helpful in a number of ways that are both positive and negative. For instance, if you run an eCommerce store online, you may notice an alert that shows you a large spike in revenue on one of your landing pages. You can then look into the alert to figure out why you are receiving so much more activity than you were before. Conversely, you can see when bad things are happening, as well. For example, maybe you run a lot of campaigns that you are tracking in Analytics. You might see an alert that traffic for one of your campaigns has dropped off rapidly. Without alerts, it could have been a long time before you noticed anything was wrong, which means lost revenue and more time to get back on track.

Custom Alerts in Google Analytics

Google measures large changes in data on your website, but you might want to know about smaller fluctuations or perhaps any fluctuation at all. For example, automatic alerts are triggered when visits for a particular page increase by more than 20%. If you wanted to know about any change in visits to a particular page, you can configure a custom alert. You can configure alerts using a number of combinations of different conditions. You can also view data for alerts just like you do with automatic alert reports.

Setting up a custom alert

1. Log into your account
2. At the time of this writing, there are two different ways that you can start a new alert. You can either click on a property to view its reports, intelligence -> Overview -> Custom alerts tab in the intelligence events overview report -> create new custom alert. Alternatively, you can go to the “admin” button at the top right corner of the page, click the profile and property that you want to create an alert for, then click “custom alerts” under the assets tab.
3. Name your alert
4. If you want to apply it to more than the profile you have already selected, pick one from the drop-down menu.
5. Select how often you would like to be alerted when there is an alert to send (currently, you can select daily, weekly or monthly).
6. Tell Analytics how you would like to receive your alert. You can choose email or text message (sweet!)
7. Next, choose your alert conditions.
8. Save your new alert.

Making Annotations for Your Alerts

If you work in an environment where there is more than one person looking at Analytics data, annotations are very helpful for describing what is happening with intelligence events. Annotations save time for others who may need to work with data by telling them information about an event right in reports. You can make annotations public or private (in terms of account users). Annotations can also be beneficial for single users who do a lot of work in Analytics. With the sheer volume of data that we as web marketers look at on a daily basis, it’s really easy to forget what we were thinking about from report to report. Annotations are saved in Analytics, and they help us remember details about data.

Setting up an annotation on intelligence reports

Start from any intelligence event report (note: you can make annotations in virtually any report in Analytics; however, they are especially helpful in intelligence events reports to explain drastic changes in data).

1. Click on the little arrow underneath the graph at the top of a report.

1. Click +create new annotation
2. Use the current date, or change it to another date where you would like the annotation to be placed.
3. Write what you want the annotation to say (160 character max)
4. Select “share” or “private” for visibility

1. Click “save.”

Your annotation will now be saved so it will be visible the next time you or someone else looks at the report.

Intelligence events and alerts are a great way to keep up to date on notable changes that are happening with your website. You can use automatic alerts if you are just interested in the default values Google makes account owners aware of, or you can set up custom alerts for more subtle changes. Annotations on intelligence events are also useful for noting data spikes in your reports.

How do you use custom alerts and intelligence events in Analytics? Do you find them to be a useful feature of the platform?