Using Go game as an example of Segmentation & Propensity Modeling

Marketing teams use data too. Segmentation and propensity modeling uses machine learning models to find targeted marketing audiences and highly evolved decision models. These models focus on human behavior and how likely an individual or group will choose one option among the many presented to them.  

The segmentation and propensity modeling applications can include areas such as the likelihood for an individual to 

  • vote for a candidate
  • contribute to a cause 
  • cancel a subscription 
  • watch a particular TV show 
  • commit fraud
  • or nearly any other choice a consumer can make 

An optimized machine learning segmentation model can increase the marketing team’s ROI significantly over the team’s current audience segmentation. These models are also designed to be continuously learning, meaning they improve over time as they learn more about individual buyers, customers, or data points.   

Expect this engagement to work primarily with the marketing or sales teams, but C-Suite may be called in should the modeling contribute to the company’s overall business strategy. Data visualization solutions are also available to communicate the findings across the business teams and to stakeholders.  

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