Google Analytics
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Google Analytics lets you do more than measure sales and conversions. It also gives insights into how visitors find and use your site, and how to keep them coming back.
This case study demonstrates Tomorrow Software as an easy integration option for adding tracking code to web pages typically done so outside of the normal software development life cycle (SDLC). Not only does this provide an easy, and rapid deployment of such third-party services, but also ensures that as and when new pages are introduced it provides comfort that tracking code will be ‘appended’ to each and every page the web application responds with back to the user’s browser.
This example is a common method whereby you can simply read a JavaScript file containing the required tracking code, insert your account ID and append it to any web page.
For information regarding the Google Analytics service please refer to:
https://www.google.com/analytics/web/
The first step of any rule writing is to determine what we want to do and how it can be accomplished.
Before you begin you will need to ensure that you have a valid Google Account email address and password for using the service, or alternatively sign up, it only takes a couple of minutes. https://accounts.google.com
We will discuss tracking code throughout this case study, which is only accessible once you have logged in to Google Analytics.
To access your tracking code:
Log in to Google Analytics https://www.google.com/analytics/web/.
From the Admin page, select the .js Tracking Info property from within the list of accounts. Please note that tracking code is profile-specific.
The tracking code can be copied and pasted from the Website Tracking text box from the Tracking Code menu item.
The code will be similar to the below (where x replaces your specific account code 'UA-xxxxxxx-x' ):
Replace the code with 'UA-xxxxxxx-x' as we can set the account ID in Tomorrow Software rules later, which makes managing the rules and different Google Analytics accounts much easier.
Copy and paste the above JavaScript code into a file named "google.js" and save somewhere local i.e. your desktop, for use later on in the exercise.
It is this tracking code that performs the task of collecting the browser data of visitors.
Start by creating a new repository called “Google Analytics Example”.
It’s recommended that the process involved in adding the Google Tracking code be split into two:
setting a variable which holds the unique Google User account 'UA-1234567-1’.
and then inserting this value into the tracking code itself.
This means that you can subsequently update the account or the code separately in future deployments, or when Google amend their tracking code.
Keeping this in mind, you should create the following blank rule sets:
GoogleAnalytics: this rule set will be responsible for creating the new UA variable plus reading the tracking code and adding it to the page.
Qwerty_test: this rule set will allow you to test how a deployment can work in the demonstration Qwerty example application.
The two new blank Rules will be visible now within the repository.
In the Tomorrow Software console select the Data Files folder, then upload the ‘google.js’ file you created above and saved to your desktop.
Ensure you upload to the newly created “Google Analytics Example” repository that will now be available in the drop-down list of available folders.
Press upload and the file will now be visible in the repository in data files for the rules to use.
Using a Set Variable rule set a new variable called Google_UA with the value “UA-1234567-1” where 1234567-1 is replaced with your specific Google Analytics user account.
Then using the File Reader read the google.js file and give it a variable name called ‘GOOGLE_ADD’
Next use a String Replacer rule to insert the newly created Google_UA variable into the tracking code .js file, followed by the HTTP Response Addition rule, to append the Google Tracking code to the response.
The string replacer rule will basically look through the code (which is now called ‘GOOGLE_ADD’) and replace any found content with the value of the variable we have defined ‘Google_UA’.
The HTTP Response Addition rule will now take effect and provide the amended google.js file as an addition to the page response and will activate this in the user’s browser.
The final step for this rule set is to add a couple of Exit rules called “OK” and “Fail” which will assist in rules performance to tell you if the rule is working, and to help with embedding this as a rule set within another rule set.
This rule set will allow you to see an example deployment to the Qwerty demo application.
Of course, with every response from the application there is static content which you don’t wish to add Google tracking code to, so take a couple of simple steps to filter off transactions which don’t require code appending.
For example, a jpg image may be served up on a page each and every time a user navigates to this page so adding code to this page will not provide any additional customer insight.
Using the Name Splitter rule to identify the URI extension is a useful way to filter out unwanted data before reading the Google Analytics rule set.
Variable Name: URI
Last Name Variable: we are only interested in the last part of the URI so we name this variable EXT.
Split Pattern: “.” is the identifier of the URI which tells the rule which part of the value we want to use or split.
Using the Switch rule set the Switch Variable properties to EXT as created above and proceed to ‘Add Chain Points’ for the static content you wish to ignore such as gif, css, html, js, jpg.
The final step is to connect the newly created GoogleAnalytics.xml rule set now located in the Rule Sets folder.
Finally, you can set up the configuration file. Click the Configurations menu, select the “Google Analytics Example” repository from the drop-down list, and enter some basic information about the rule to load.
The following screen shots show the information required for the “General”, and “Input Source” tabs.
You can now click the “Create” button to create your configuration file. Once created, click the “Deploy” button to deploy it to your Qwerty demo server.
The above case study shows how to implement Google Analytics tracking code in a specific environment, though of course each individual application will be different.
You will be able to log into your Google Analytics and select real-time traffic reports within the reporting dashboard, to validate the tracking code has been inserted, and is working correctly on your website.
You can also right click the page in the browser to view source code to verify the Google tracking code has been correctly inserted into the target application page.
