Einstein
Personalized messaging represents an important new opportunity within the online advertising equation to further improve performance and return on ad spend.
Teracent’s Intelligent Display Advertising™ uses sophisticated statistical machine learning principles to determine the optimal selection of each dynamic element within the ad, based on what is known about the impression and the user, as well as historical performance. All available impression and user inputs – such as behavioral, demographic, contextual, and geographic – can be used to drive performance prediction. The Teracent Optimization infrastructure incorporates innovative “aging” models and learning acceleration algorithms to address longstanding marketing challenges. The optimization infrastructure is designed to maximize flexibility allowing algorithms to be configured to the marketer’s specific campaign objectives and strategies.
The Teracent Optimization infrastructure is configurable across the following dimensions:
- Objective – optimization is configured to maximize click-thru rate, interaction rate, or conversion rate depending on campaign objective.
- Ad Strategy – ad selection can be driven by business rules, optimization or both depending on the configured ad strategy.
- Targeting Input – optimization can be configured to learn on all or specified targeting inputs.
