Google Steps It Up With Data Driven Attribution

More news from Mountain View on Tuesday, where Internet powerhouse Google released the much-anticipated Data Driven Attribution (DDA) feature for its Premium users. The release of Google’s DDA approach comes as no surprise to the analytics and measurement community. The world of attribution measurement is constantly evolving and new attribution approaches, new players, and new tools regularly enter the market, enabling marketers to select the right attribution tool for their business needs. It was only a matter of time before Google released a persuasive, more advanced measurement offering.

First, the Data Driven Attribution feature is only available for Google Analytics Premium users. It has several notable features worth highlighting:

  • Google DDA’s approach is statistically driven methodology. Google’s DDA approach is a huge improvement over its rules-based Attribution Modeling tool (which is available for FREE for Google Analytics users). The DDA approach uses probability modeling to best estimate the values of each interaction. The approach itself is transparent, understandable, and Google is extremely open about how it calculates the value parameters.
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What Is Your Customer Analytics Persona?

Buy analytics software, hire marketing scientists, and engage analytics consultants. Now wait for the magic of customer analytics to happen. Right?

Wrong. Building a successful customer analytics capability involves careful orchestration of several capabilities and requires customer insights (CI) professionals to answer some key questions about their current state of customer analytics:

  • What is the level of importance given to customer analytics in your organization?  
  • Have you clearly defined where you will use the output of customer analytics?
  • How is your analytics team structured and supported?
  • How do you manage and process your customer data?
  • Do you have clear line of sight between analytics efforts and business outcomes?
  • What is the process of sharing insights from analytics projects?
  • What type of technology do you need to produce, consume and activate analytics?
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