Braze × Luxury Escapes
Braze x Luxury Escapes, AI agent lifts revenue per user 10% by replacing rigid segmentation rules
10% lift in revenue per user
Rigid rules-based segmentation to adaptive AI-driven cohort assignment
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Why it works
The case study earns its headline by isolating the variable: the A/B test changed only the decisioning layer, so the revenue lift is cleanly attributable to the agent. The detail about the agent routing fewer users to the promo cohort, and those users redeeming more codes, is a concrete mechanism that explains the result rather than asserting it. The quote from Niru carries real information about the team's pre-launch hypothesis and how the agent confounded it.
Steal this
Test the decisioning layer independently from content: keep emails identical across groups and vary only who assigns the cohort, so the result is unambiguous.
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