As I mentioned above, business context is critical to these exercises. My examples below aren’t intended to serve as specific guidance for a business, but rather a way to consider the setup.
You can reach out to me directly if you want to discuss your business needs.

We’re uniquely positioned as an industry to be able to bring together 1P and 3P data in a more integrated way, through platforms like CDP’s and using various API’s and server side solutions. Historically, while we’ve had more access to raw, granular tracking data, privacy centricity and regulatory considerations have necessarily changed that approach. This makes operational efficiency more important than ever, and properly structuring data drives faster time to insight and more opportunities to activate on that data.

For the rare business that is starting fresh, identifying and then prioritizing the data sets to properly classify is a great exercise to do from the start. For the overwhelming majority of businesses that already have a significant amount of data, structured in a variety of ways, the exercise becomes a matter of reclassifying the metadata and identifying how best to marry data sets to derive the right insights. Prioritization is still key, and that’s based on what the most critical outcomes and use cases are at the time that the exercise is being performed.

UTM parameters, or Urchin Tracking Module parameters, are values added to a URL to track campaign results. These parameters can be ingested in to Analytics systems like Adobe Analytics and Google Analytics, via processing rules that parse the values into meaningful classifications for reporting. Below is an example of the structure and data that can be appended to the URLs.

In order to effectively analyze the attributes, and results, from URL parameters, it’s important to know how the business rules translate those parameters into actionable insights. Finding an illustrative way to associate these values will save time for teams and individuals to understand how to choose the best KPI’s for campaigns, based on forecasts, historical trends, and results. Understanding how best to tie the funnel (Awareness, Interest, Consideration, etc) to channels (SEM, Display, Out of Home (OOH), Social, etc…) to sub-channels, tactics, and products, is an important way to link all of these aspects of a campaign together.

For businesses that are selling products, having them properly associated with categories is another critical exercise. Fruit is a great example of this. Fruit is a top category, followed by type of fruit, then by subtype, color, variety, etc… If you want to sell more apples, understanding what attributes make that type of fruit the most desirable, by season, is achievable through how the metadata is associated with transactions, and ultimately to the people making the purchases. We’ll address audience building, based on these attributes and results, in another post.

Structuring data isn’t the sexiest effort, but many campaigns certainly are. Properly bringing organized data together can ensure you have robust measurement to accurately plan and activate. While the campaigns themselves are important for engagement, awareness and revenue, if you can’t determine what aspects of the campaign resonated and most effectively drove the intended results, then there’s money being left on the table.