Closed-Loop Intelligence
Every send makes the next one smarter.
Analytical
What it does
Closed-Loop Intelligence connects customer activity, behaviour and campaign performance into one learning cycle. What Braze sends flows back to the warehouse, gets joined to what customers actually did, and returns as sharper scores and segments for the next send. Nothing is fire-and-forget: every campaign becomes training data, and the programme compounds instead of repeating itself.
What feeds it
- ·Braze Currents, streaming every send, open, click and conversion as it happens
- ·Campaign performance, so results are read at the customer level not the dashboard level
- ·Warehouse activity: orders, behaviour and value, joined to the message history
How to switch it on in Braze
- 1Stream Currents into the warehouse so every message event lands as data.
- 2Join message events to behaviour and revenue at the customer level.
- 3Re-score nightly, so segments and predictions reflect yesterday's sends.
- 4Sync fresh attributes and segments back to Braze, ready for the next campaign.
How Fuse builds for and utilises this
- ·We build the Currents pipeline, so every send, open and click lands in the warehouse as usable data.
- ·We own the nightly re-score jobs that turn yesterday's campaigns into today's sharper segments.
- ·We publish the scorecard, so performance is read in one place and argued from the same numbers.
- ·We run the holdout programme that proves what actually changed behaviour, not just what correlated with it.
What to measure
A capability that fires on people who would have converted anyway proves nothing. Hold a slice out, measure only the gap above them, and let the recipe earn its place on evidence.
- ·Segment migration: are customers moving into higher-value segments over time?
- ·KPI movement against a holdout, so the loop is proven to cause the gain
- ·Model drift, so scores are retrained before they quietly go stale
Pairs with
On its own it is useful. Alongside these models it compounds.