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USE CASE

Behavioral Targeting

 Financial Services Use Cases

Objective

  • The “User Centric Approach”

  • Send messages/content at the right time with the right content based on individual and collective customer behaviors

  • Help marketing teams at FIs achieve more awareness, higher conversions and better engagements

Outcome

  • Pool large data sets to accurately predict custom audience segments with specific attributes relevant to the brand

  • Identify unique and relevant audiences for the brand that will be in line with the audience data pools initially created

  •  

Business Value

 

  • Increase click through rates by 4x (industry expectations)
  • Reduce costs per engagement

 

H2O's AI and Data Approaches

  • Machine learning interpretability that provides targeting explanations

  • User centric approaches (vs content centric approaches)

  • Ability to provide computational power to advance granular audience targeting capabilities

  • Target identification models - automate and optimize processes for potential consumer identification, information extraction and market segmentation

  • Probability models that will predict level at which consumers will click on ads, click through rates, etc

  • Recommendation models - discover useful patterns to determine what the user finds interesting or not

  • Machine learning interpretability that provides targeting explanations

Resources

Learn More About H2O.ai

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