One of the best ways to drive incremental revenues with machine learning is to leverage real-time user behaviors to drive conversions through experiences like next-best-action and next-best-offer (here’s more info on Vidora’s approach to enabling real-time decisioning). Real-time data enables one to target based on in-session activity and opens up the opportunity to target first-time users, anonymous users, and existing user intent.

We’ve seen real-time user data increase next-best-action conversions by 40%+ when compared to only using batch data.

One of our goals with Cortex is to make it fast and easy for our customers to integrate real-time machine learning decisioning. With Twilio Segment, sending real-time data to the Vidora Decisioning SDK is relatively straightforward, only requiring a small change to the Segment SDK. Decisions are then available from the Decisioning SDK which can be used to increase conversions, next-best-action, and next-best-offer experiences.

In summary, the steps to enable real-time machine learning decisioning using Vidora Cortex and Twilio Segment are –

  1. Configure the Segment SDK to send data directly to Cortex
  2. Build real-time machine learning pipelines in Cortex (here’s a video on how to build real-time machine learning pipelines) – this is done through the Cortex UI
  3. Act on the Cortex decisions for next-best-action, etc.

This integration will make it faster and easier for joint Twilio Segment and Vidora customers to enable advanced machine learning experiences like next-best-action and dynamic decisioning.

Let us know if you have questions!

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