Braze
Braze is a customer engagement platform represented in this wiki by its article on multi-armed bandits for real-time marketing experimentation.
Key facts
- Type: Company / customer engagement platform
- Source role: Publisher of “What is a multi-armed bandit? Smarter experimentation for real-time marketing” [src-027]
- Key framing: Multi-armed bandits are a decisioning approach embedded in platforms such as Braze, not a standalone marketing tool [src-027]
- Concepts introduced here: Intelligent Selection, Marketing Bandit Optimisation, Dynamic Traffic Allocation, A/B Testing vs Bandits, AI Decisioning [src-027]
What it does
The Braze article frames Multi-Armed Bandits as a live campaign optimisation mechanism. Instead of waiting for a fixed A/B test to finish, a bandit continuously learns which message, offer, channel, timing, or creative variation is performing best and reallocates traffic while the campaign is still running [src-027].
Braze’s product-specific layer is Intelligent Selection, described as part of Braze AI Decisioning Studio. In that framing, MAB algorithms handle the allocation problem, while contextual bandits and predictive scoring contribute broader personalisation and next-best-action decisions [src-027].
The source also uses Kayo Sports as an example of adaptive decisioning in customer engagement, with triggered campaigns improving conversion, message engagement, and churn outcomes through live message selection and timing [src-027].
Related
- See also: Intelligent Selection, Marketing Bandit Optimisation, Multi-Armed Bandits, Dynamic Traffic Allocation, Contextual Bandits, AI Decisioning, A/B Testing vs Bandits
Source references
- [src-027] Team Braze — “What is a multi-armed bandit? Smarter experimentation for real-time marketing” (2025-12-08)