Well, within Google Analytics, this can be done but requires some extra work to be done on your part.  First thing you will need to do is, if you don’t have this already, create a thank you or order confirmation page that resides on your domain.  Within this page, make sure to add the GA tracking code to the page to denote a successful conversion has taken place. To grab the values being sent back from paypal, you will need to leverage either the GET or POST methods (depending on your current site setup).  If you need help properly configuring this, please feel free to contact me. You can also put these values into a cookie and then retrieve them when the user returns back to the site (making sure that the referring URL is from paypal and its the thank.php).

After this is done, please take the following steps within Paypal:

1. Navigate to Paypal
2. Login
3. Go to My Account
4. Click on the Profile link
5. Click on Website Payment Preferences
6. Click the radio button for Auto Return to be ON. This will redirect your visitors back to your site once they finish the transaction on paypal’s site.
7. Add the URL to the page that you just created for your new onsite “thank you” or “order confirmation” pages
8. Within the destination URL, you will need to add the query string paramter: utm_nooveride=1.  This parameter ensures that traffic doesn’t overwrite referral data
9. Lastly, make sure that Payment Data Transfer is turned ON

That’s it! With this in place, after modifying the thank you page you created to handle GET or POST method data being sent via Paypal, you will be able to track ecommerce transactions originating on Paypal.

Before we go into non-interaction, I wanted to show everyone the _trackEvent method:

_gaq.push(['_trackEvent', 'Category', 'Action', 'optional label', 'optional non-interaction']);

Non-interaction is the last parameter, a boolean which is an optional value, within the _trackEvent method that provides users with the ability to define how bounce rate is calculated on the given site when an event is trigggered.  This comes in handy if you have items on a given landing page that users can interact/interface with that should be considered when coming up with a true bounce rate as you might consider users interactions as engagement and thus do not want that factored into your overall bounce rate.  Lets look at an example.

Say you had a mortgage calculator on your home page that a user could interact with but that didn’t generate an additional page view. This is where the last parameter, opt_noninteraction, comes into play. By default, every time the _trackEvent is instantiated, Google Analytics sees that interaction and includes it in the bounce rate formula.  If you set this value to true, this will not be counted towards bounce rate.  So here is a key to easily remember the difference between them:

• True – will be counted in the bounce rate formula even if the visitor also triggers the event during the same session
• False -  will not be counted as a bounce if the visitor also triggers the event during the same session

That’s it.  With that extra parameter included in your _trackEvent calls, you will now be able to accurately factor in or out those events into your bounce rate.

This method removes the 1% default sampling that currently makes up the data pool from which Site Speed is calculated from. A good rule of thumb is if your site has a smaller number of visitors, say under 200,000, then it is better to set this method to a value higher than the default.  By adjusting this, you will be able to see a larger slice of granular data for your page’s load time and other captured site speed metrics.  An example of this method in action is below:

_gaq.push(['_setSiteSpeedSampleRate', 10]);
_gaq.push(['_trackPageview']);

In the above example, the setSiteSpeedSampleRate is set to 10% which is the maximum sampling percentage per web property allowed by Google Analytics.

That’s it! With this in place, you will be able to override the default sample rate for site speed and potentially provide increased granularity for business users to see their metrics for their respective pages.

Please Note:
In order to have the method function as expected, you will need to place the _setSiteSpeedSampleRate call before the _trackPageview call.

Previously, you would have needed to use an event and wrap all clicks within the social media sharing buttons to that event(s).  Now, however, Google has opened up a new method that will help in this effort. First, lets look into the method and parameters that make up the _trackSocial call:
_trackSocial(network, socialAction, target, pagePath)

In the above method, the first two parameters, network and socialAction are required.  Network is the domain or social media channel that the action occurs on (i.e. Twitter, Okrut, Facebook, etc). The socialAction parameter is designated to capture the action type that the user interacted/interfaced with (i.e. like, tweet, send to a friend, etc).

The next two parameters, target and pagePath, are optional values.  Target is normally the URL target that the user clicked on.  However, if this isn’t of value to you, you can always reclassify this parameter to house any relevant ID (such as an article/blog/product ID).  One thing to keep in mind though is that if the user clicks on an item say a like or a send to a friend next to an article, you wont need to pass a value for this parameter since the the current URL is passed by default. The last parameter is the pagePath which captures the page by path (and not the actual full URL path) from the page that the action happened on.  This value is only needed in the instance where you are using virtual or custom page paths for reporting.

So a full _trackSocial method would look similar to:

_gaq.push(['_trackSocial', 'Twitter', 'Follow-Us', '12345', 'http://www.mysite.com/article12345']);

where the 12345 is the ID identifying the target and the URL is the page path.

Please Note:
If you wish to leave the target value blank but pass in a value for the pagePath, you will need to explictly specify “undefined” within the code otherwise it will not track correctly.

One reason why someone would want to disable this is if they are using _trackPageview() to create virtual URLs the same URL can and will be reported between the social tracking and the page level tracking.  This will skew results.  To ensure that this doesn’t occur, you can add the following three lines of code prior to the asynchronous GA tracking snippet:

<script src=”https://apis.google.com/js/plusone.js”>

google_analytics: false

</script>

Now the default tracking has been disabled.  To have +1 tracking within your GA account, you will need to create JS logic that calls the _trackSocial() method with your desired parameters.

Page Load tracking will appear within the Content section of the Google Analytics interface.  This has no impact on your overall bounce rate and is really easy to add in.  To add in, you will need to add the following tag below the _trackPageview() call:
_gaq.push(['_trackPageview']);

_gaq.push(['_trackPageLoadTime']);

That’s it.  You will be able to verify if this is tracking correctly by looking at the Content section 24 hours after implementing.  If data is there, then you are set.  If not, check your coding to make sure there are no errors.

Please Note:
The page load tracking for Google Analytics only happens for visits from browsers that support the HTML5 (specifically the NavigationTiming interface) or have the Google toolbar installed.

Let’s say you sign up a new user for service but it cost you 20 dollars on advertising. While it drove revenue, it actually hurt the bottom line since it was an unprofitable sign up.   Without putting value to this action, you would not have gleamed this insight and would think that it was worth wild to continue to do. So what exactly are soft goals? Soft goals are goals that don’t instrinsically drive revenue but are important to know how engaged visitors are on your site. To calculate the value of soft goals, you need to use the following calculation:

Soft goal = Percent of goal value (specific goal/total goals) x Conversion rate x Avg customer value

Breaking down the above equation,

• percent goal value takes the goal. In this example new user sign up divided by total goals.
• conversion rate can be shown by looking at the percent conversions (percent of uvs who then purchased)
• average value generated by each customer per year

With this in place, you can begin to measure the impact and value each of these “soft goals” present. Remember that this isn’t an exact equation but more of one that will help you begin to see the importance (or lack thereof) to these goals.

The way we can tackle this is to make each click into the form field its on page view (we would group it as /registration_funnel/name, age, gender, etc) and then set up a funnel based on URL destination.  So if it was a 5 step funnel we would have as onClick into the form:

pageTracker._trackPageview(“/registration_funnel/name”);
pageTracker._trackPageview(“/registration_funnel/address”);
pageTracker._trackPageview(“/registration_funnel/state”);
pageTracker._trackPageview(“/registration_funnel/zip”);
pageTracker._trackPageview(“/registration_funnel/email”);

Then, we would set up each step of the registration funnel to be:

http://www.domain.com/registration_funnel/name.html

http://www.domain.com/registration_funnel/address.html

http://www.domain.com/registration_funnel/state.html

http://www.domain.com/registration_funnel/zip.html

http://www.domain.com/registration_funnel/email.html

Then within Goals -> Funnel Visualization, you will see the fallout by each field vs. just looking at fallout from page to page.  This is extremely helpful if you are looking to see whether your form is too long (and asks for too much information) or its just right.

Please Note:

• This will need to roll into its own reporting suite as passing these as page views will grossly inflate the page view stats.
• To improve the data capture and accuracy of the data, it is recommended to only capture the value once per session.

So lets just say you have three links on your landing page that you want to track and see how effective they were in adding revenue to the bottom line.  You can leverage the _setCustomVar method to track all the creatives.  Below is an example of the code that would be needed:

<a href=”recipe.htm” onClick=”_gaq.push(['_setCustomVar','1','Clicks','Recipe_Button',2]);”><img src=”recipebutton.jpg”></a>
<a href=”mentor.htm” onClick=”_gaq.push(['_setCustomVar','1','Clicks','Mentor_Button',2]);”><img src=”recipebutton.jpg”></a>
<a href=”homepage.htm” onClick=”_gaq.push(['_setCustomVar','1','Clicks','Homepage_Button',2]);”><img src=”recipebutton.jpg”></a>

Once you have this in place, you will need to set up the conversion page as a goal within the interface.  Then you will be able to see the contribution that the button drove to registrations.  To see your contribution stats, just navigate to Visitors -> Custom Variables and then click on the Ecommerce tab.  There you go.  You can take it even further by creating advanced segments for these users (depending on their behaviors) to then compare that segment to the total traffic to see how it influences all captured KPIs.

To track form entry values, we will need to leverage the _trackEvent method and either store the value in a session cookie and then after submission pass it to Google Analytics or put it into a temporary container (cookie) and pass it after user leaves current field and moves to the next one.

I think the onSubmission method makes the most sense and would be the easiest to implement.  So the code for the _trackEvent would look similar to:

Example of trackEvent method:
_gaq.push(['_trackEvent', 'Form Entries', '<field name>', '<field value>'])’

Example of trackEvent method populated with dummy values:
_gaq.push(['_trackEvent', 'Form Entries', 'Name', 'Derek Jeter'])’
_gaq.push(['_trackEvent', 'Form Entries', 'State', 'NY'])’

That’s it! Once you have this in place you will get to see the entry value’s influence on ecommerce and then create an advanced segment based on the value captured to see how it impacts the other captured key performance indicators.

Please Note:

• The total combined length of any custom variable name and value may not exceed 64 bytes.