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USE CASE

Contextual, Cookieless Targeting

 Financial Services Use Cases

Objective

  • The “Content Centric” Approach

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

  • Target customers without cookies, serving relevant ads without the need for 3rd party data

  • Rather than targeting ads based on user behaviour, contextual advertising targets ads based on the environment in which the ad appears

Outcome

  • Use available data (without cookies) such as keywords, page types, phrases and media channels to understand the context of the page and provide the most relevant communications

  • Models that will determine the best ad placements based on a site’s content and relevant phrases that are related to the context, all in real time.

Business Value

  • Engage with customers within their channels of interest while they are in a receptive frame of mind

  • Avoid constraints from privacy legislations, protecting consumer data privacy as it does not collect or use information about users

  • Improve profitability clicks and revenue by making ads relevant to the content of the page

H2O's AI and Data Approaches

  • Natural Language Processing to interpret pages, understand context

  • Machine learning interpretability that provides targeting explanations

  • Make logistic regression model on data in the advertisements and web pages that integrates click feedback and semantic information available from both advertisements and web pages to determine relevancy.

  • Feature extraction, feature selection, and coefficient estimation for feature through a logistic regression.

  • Document classification contextual advertisement applications boosted with sensitive content detection

  • Response prediction models with feature selection algorithms for increasing automation and scalability

Resources

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