Reinforcement learning personalization uses observed customer response as a reward signal to adjust future messages or offers. Braze's 2025 decisioning announcement describes continual experimentation and adaptation from customer behavior.
The demo shifts the next round's allocation after observing wins for two offers. It focuses on updating a policy from outcomes, while AI decisioning can describe a choice among current predicted scores.
A click-only reward can favor unwanted messages. Include long-term value and opt-outs when defining success.
When to use
Use it when offer allocation should keep learning from customer responses.