- 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
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.
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
- 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