Culture and Collaboration is Key to Measurement Changes

Digital media created the ability for marketers to observe, measure, and optimize their marketing efforts.  Many companies have been built and sold around various capabilities to more effectively create and measure customer experiences.  Over the last 10 years there have been a variety of changes which have led marketers to shift focus away from blind spots (e.g. Safari) and continue to focus on areas where observations remained (e.g. Chrome).  Apple and Google are taking away two of the remaining few observation sources by deprecating 3rd party cookies in Chrome and restricting access to IDFA through Apple privacy changes.
While Google has postponed the changes with Chrome until 2023, nobody should postpone overhauling their approach to marketing.   One of the biggest challenges appears to be teams that are left wondering where to start the process. In this instance, there isn’t a technology or new ID that is going to fill the gap.  Everyone should use this as a moment to revisit and rethink their approaches to data and measurement which go far beyond a tech stack conversation and it needs to start at the top.
Three areas any brand or marketer should focus on as they think about the upcoming changes are: Culture, Collaboration, and Data Infrastructure.


The single most difficult thing to change, and often the most overlooked, is culture. Many people have been ingrained to use certain KPIs and strategies for measurement and optimization which are based on observations (e.g. Last Click).  It is hard to retrain your brain to trust different metrics and understand that failure is okay but, this is going to be essential to success going forward.
A good first step should be reviewing current KPIs through the lens of the validity of each KPI across walled gardens where observation isn’t possible. There should be alignment of marketing metrics which are not necessarily focused on actions like last click.  The general answer is going to be needing to change your KPI and move to a more experimentation-based data culture.  Most companies do a great deal of experimentation on their web site through A/B testing but they don’t transfer this culture to media campaigns.
Teams are typically comfortable with A/B testing on web sites or mobile apps because it doesn’t cost much to test a channel where you already control and have access to all of the data.  When you move to more experimentation in paid media you need leadership to understand this testing is no longer free – although it is worth the price.  There are media costs to execute these tests and tests are going to fail.  The other challenge will be to learn from the failure, use those learnings as an input to other tests, and finally maintain the rigor to continue testing.


A key aspect of changing the culture requires cross team collaboration.  When we think about teams, it isn’t just internal teams but anyone creating experiences that could impact your experiments which includes your media agencies.  It is essential you are able to plan, collaborate, and communicate everything from the hypothesis, creative, audience, campaign setup, and conversion.
Collaboration is more than just working through the campaign setup but also needs to extend through to your data lake and data sciences teams.  Do they have all the data and context of the campaign objectives to be able to quickly provide feedback which can be used for optimization?  
There are still many teams that spend weeks cleaning up and making sense of data before they are able to use the results for optimization. In a world where you are relying on experimentation results you need to be able to move quickly which requires data standardization and inherent trust in data.  Trust doesn’t come from just the reports but also from trusting the inputs so you can enable automation and trust the outputs.

Data Infrastructure

We wouldn’t be talking about digital marketing if there wasn’t some review of a tech stack.  In this case, the focus should be on the data infrastructure to support a new approach to marketing measurement.  It is no longer sufficient to put everything into a data lake, do some ETL, and create some dashboards.  What is required now is being able to have a common data language across your data sources to measure incrementality in the business.  The data teams need to have the full picture and context as they analyze the incremental lift on the business.
First party data becomes a key component to this process as you apply modeling based on your first party data.   The data includes everything from the hypothesis used for an experiment, what first party audience was used in a campaign, creative attributes, campaign configuration, publisher, transactions, revenue, and any other data specific to your business.  
A common taxonomy and data language is going to be essential for communication around how to optimize and understand the impact any marketing experiments are having on the business.  Since it is no longer about relying on cookies or mobile device IDs for optimization, you need to understand how these other attributes such as placement, creative, and other customer characteristics are influencing the effectiveness of your marketing.
One useful exercise to understand your data infrastructure is to go through a value stream mapping for marketing operations and customer experience creation all the way through to measurement. 
You should be asking the following questions as you go through the process:

  • How does marketing planning and budgeting happen? Who is responsible in this process?
  • How is content created for experiences?  How long does it take?  Are the same teams responsible for creative ad units?
  • What is the method of communication for campaign details (audience, creative, placement, etc.)?  Who is responsible for this data?
  • Is there a taxonomy for marketing or content?  Who manages the taxonomy?
  • Where does first party data reside?  How is first party data leveraged for customer acquisition in walled gardens?
  • How long does it take to receive results for marketing campaigns?  What is the attribution method used by each team?
  • Where are bottlenecks in the process of creating content?
  • Do you have easy access to media performance data and campaign details managed by your agency?  How is this incorporated into internal systems?

In product management and product design we spend a great deal of time doing discovery, learning, and moving quickly for fast feedback.  The path to changing a culture for product teams is a very similar transformation that marketing organizations need to embark on now as it will take time.
Everyone should be taking control of their data strategy before it is too late and they begin to see the results in lower levels of customer acquisition or poor retention. Your organization needs to focus on how to shift culture, collaboration and infrastructure now. Otherwise you’ll be left behind as strategies that once worked lose out in a privacy-centric digital landscape.

About the Author

Chris Comstock headshot
Chris Comstock is Claravine’s Chief Product Officer and has more than 15 years in the digital marketing and data management fields – working as a consultant, brand and product leader for a variety of top companies. He now heads Claravine’s product vision.

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