How to Use Dimension Widening to Improve Affiliate Reporting

How to Use Dimension Widening to Improve Affiliate Reporting

Posted by on Thu, Feb 27, 2014
Filed Under | Analytics, Google Analytics


Universal Analytics Feature Turns Ugly Data into Usable Data

Alibris.com, a seller of both new, used and rare books, recently came to Blast Analytics & Marketing looking for a solution to a very specific Google Analytics reporting problem.  Being a company with over ten thousand affiliate channels, a significant amount of traffic routinely gets tagged with an encoded source code to mask the affiliate’s real name.

This blog post shows how Google Analytics’ Dimension Widening paved the way to construction of a custom report where the analysts could see the actual name of the affiliate and not have to manually look up an encoded source code in a look-up table.

(Keep in mind, that this dimension widening solution can be applied to a wide variety of similar reporting challenges and is not just for this specific affiliate use case.)

Masking Affiliates

Each data feed our client sends to their affiliates has a number of items for sale.  Each item in that feed is available for sale on that individual affiliate’s site, and when selected for purchase takes the user to the Alibris website to complete the transaction.  The links that take the user back to Alibris are embedded with additional parameters such as the following:

somestore.com/product?utm_medium=email&utm_source=FwEz3vxnlVE&utm_campaign=sp&utm_content=blast

The bolded text above is the affiliate’s store codified by the affiliate management system.

The problem can really be seen when you look at an example of the Google Analytics Acquisition Channels report for all affiliates.  

blog-acquistion-channels

It’s great that we’re seeing good traffic to the affiliate GwEz7vxblVU…  um, who is that again?

The Dimension Widening Solution

Our client stated, “What we need is a way to map the affiliate source used into something we recognize when looking at Google Analytics’ reports.  Can we do that somehow?”  Dimension Widening offers this capability, almost perfectly.

Here’s what we did to accomplish the objective, followed by more detail on each step:

  • Define 2 custom dimensions in Google Analytics (user scope)
  • Create custom tracking code to extract utm_source
  • Prepare a CSV data mapping file
  • Import the data set into Google Analytics
  • Test a hypothetical campaign URL
  • Build a custom report

Define 2 Custom Dimensions in Google Analytics

The Google Analytics support pages specifically state you cannot widen campaign dimensions, which becomes evident when you begin to use the data import in later steps.  So for our project to succeed, we needed to capture the utm_source and store it away in a custom dimension, which we called Source-Encoded.  We also created and reserve a second dimension for the Source-Decoded data.  Both of these were defined as user-level dimensions.

blog-custom-dimensions

We set the custom dimensions’ scope to ‘User’ because it is more closely aligned (but not perfectly) with how native ‘Source’ dimensions work.  As an example, if a user comes in through an affiliate campaign link on Monday, but doesn’t convert on that visit, then later comes back to the site on Tuesday and converts, Google Analytics credits the original campaign (not as a ‘direct’).  If we had selected a scope of ‘Session’ we would have seen issues.  Since the purpose of this new decoded dimension is to understand affiliate performance, we felt ‘User’ best represented our objectives.

Create Custom Tracking Code to Extract utm_source

As this customer was already using the Google Tag Manager solution, we added custom JavaScript to test for and capture any utm_source in the URL string to set the first custom dimension.

var dimValue = any defined utm_source from the campaign URL;
ga('set', 'dimension1', dimValue);

Preparing a CSV File

The client provided a 2-column Excel file with approximately 11,000 rows. Google Analytics asks for a CSV file under 1 GB with the first row being header detail, so the preparation needed was to make sure the header row contained the correct information.  Google Analytics uses the header data to know which column is the affiliate code (Key) and which is the readable text (Widen-To Dimension).

To extract the data properly you have to use Google Analytics’ naming convention, putting in row 1, cell 1, the header “ga:dimension1”, and row 1, cell 2, the header “ga:dimension2”.

After this small change to our file, we exported it to CSV format.

3 Steps to Import Data into Google Analytics

The Google Analytics admin interface page shows a link called “Data Import” and requires 3 steps to complete the data import process.

Step 1, we select the type using the radio selection for “Generic Dimension Widening.”

blog-data-set-step-1

Step 2 lets you name your data set and assign it to one or a number of views.  Google Analytics strongly suggests you create a test view first and make sure the data widens as expected before using it on a production or default view (since you cannot remove the data once it is added).

blog-data-set-step-2

On Step 3 we define the transformation we want to occur by selecting the appropriate “key” and “widen to” dimensions from the selection window.  This is where we noted that you cannot simply select a campaign variable, but you can select from the custom dimensions we defined earlier.

blog-data-set-step-3

After step 3, we have our data set defined and can now manage the file upload process.  Once we select and upload a file, and Google Analytics validates that it is usable noting it as “Completed”, new custom dimensions are being created as data come in.

Testing a Hypothetical Campaign URL

Now that we have set up the rules for the source extraction from a URL string, set up the two custom dimensions, and imported the CSV file; we are ready to create a fictitious URL with a sample from one of the affiliate source codes. From earlier, we stated the URL would look like the following:

somestore.com/product?utm_medium=email&utm_source=FwEz3vxnlVE&utm_campaign=sp&utm_content=blast

So all we need to do is paste this into a browser, perhaps monitor it with a proxy tool (like Charles Proxy) to see the details sent off to Google Analytics.  Then wait until the following day to see the data in GA, and validate that it was widened.

Build a Custom Report

Since we have to wait to see the data, we can take a moment to build a custom report for ease of accessing the data.  The standard Acquisition Channel report might get us within a couple of clicks but why not take advantage of the custom reporting feature, and make it a no-brainer to drill down to the needed affiliate data.

As you can see, the next day we see our custom report showing our “Source-Decoded” field front and center, and the decoded text is a URL (by design) of the affiliate company.

blog-decoded

Conclusion

With Google Analytics’ dimension widening flexibility we were able to create an elegant solution for our client without a lot of fancy table manipulations or time-intensive manual processes.  These newer Google Analytics features continue to address day-to-day processing needs associated with running an affiliate-oriented business.

Now that we have solved our client’s affiliate naming issue, perhaps next we will suggest tackling product-level dimension widening with external meta-data.  I saw that option at Step 1 of the data set creation and couldn’t help but start to think of additional uses dimension widening could offer.

If you would like more high level information on Google Analytics Dimension Widening for importing data, check out this great post by Justin Cutroni.

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This post was written by:

has written 5 posts on the Web Analytics Blog.

Paul is a Google Analytics Consultant at Blast Analytics & Marketing. He has spent 20+ years venturing into the depths of software development, designing and implementing analytic solutions to automate business processes within all sizes of financial services institutions.

Paul's experience spans software development, Business Intelligence design, eCommerce, database design, predictive modeling, and adaptive decision systems. Add Paul to your circles on Google+


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  • http://www.farhanonline.com/seo/ Farhan Fawzer

    Google analytics have got better and better.

  • John Carter

    As a Google PPC ninja once told me, it is important to constantly split-test new ads so that messaging is consistently being refined in a competitive marketplace. That happens on at least two levels: 1) The ads themselves, inside Adwords. It has easy split testing for ads and reports optimal performance. 2) On your landing page itself. That’s where a lot of people miss the boat on conversions. They blame the ads but don’t split test elements or versions of their landing pages. There are lots of ways to do this, but it’s kind of technical and as a business owner I couldn’t really be bothered with learning the details, but I can refer you to Simon. Call him – 219-733-4687.

  • John Doe

    How do you get the ability to use widening for products and for customs dimensions with user scope? This does not seem to be possible at my account. May it be a premium feature?

  • Paul Lear

    Hi John. This feature is available for all Universal Analytics customers. When setting up of the custom dimensions, you select the scope: hit, session, user and PRODUCT. While I used USER level for this project, it seems product level might be the best fit for your use.

  • btatvic

    Hi Paul, Awesome Post on Dimension Widening. It really is an interesting feature in Universal Analytics.

    We too recently build up a USE CASE on Dimension Widening by Integrating SEO Data into Google Analytics, and you know what, the analysis was darn interesting!

    Cheers! :)



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