Dynamic decisioning is a powerful technique for optimizing user journeys. Layering machine learning technology into the dynamic decisioning process, including techniques like Uplift Modeling and Real-Time Inference, enables businesses to maximize subscription conversions, registration sign-ups, and product purchases.
One natural question is how can a business enable fast experimentation and deployment of dynamic decisioning experiences without needing to make large ongoing investments in engineering and data science?
Zephr and Vidora
Vidora recently partnered with Zephr for the explicit purpose of providing a no-code solution for building machine-learning powered dynamic decisioning experiences. The direct integration enables a Cortex user to build machine learning pipelines to optimize conversions and have those pipelines be used directly as decision points within the Zephr platform. The no-code experience minimizes engineering overhead and enables marketers and product managers more flexibility to try new experiences.
How does it Work?
Let’s take the example of building a dynamic decisioning point to optimize subscription conversions. Here are the 3 steps needed
More Machine Learning Dynamic Decisioning!
The Vidora-Zephr integration is designed to enable fast deployment and experimentation of dynamic decisioning experiences. No coding and minimal engineering work. If you have questions, please let us know by reaching out to firstname.lastname@example.org.