---
title: Markin + Braze
url: https://markin.ai/integrations/braze
category: Engagement and CRM
description: Connect Markin to Braze: write next-best-action decisions as custom attributes or trigger events, with frequency, consent and quiet hours enforced by Braze.
---

# Markin + Braze

> Braze keeps the channel. Markin decides what deserves to be sent.

**Job:** Decide what deserves to be sent; let Braze send it.

A Markin and Braze setup keeps Braze as the channel and adds the decision. Markin reads delivery, engagement and Canvas exposure history, proposes actions nobody has written yet, and writes the chosen one back as a custom attribute or trigger event. Frequency, consent and quiet hours stay enforced by Braze.

Braze executes exceptionally well within the set of campaigns someone wrote. Markin widens that set: it proposes actions nobody has written yet, sizes them, and lets Braze do what it is best at once the decision exists.

## What can you ask Markin to do in Braze?

- How do I choose the next best action for a Braze Canvas?
- How do I reduce churn in Braze without sending more messages?
- Can I add AI decisioning to Braze without rebuilding my Canvases?
- How do I measure incremental revenue from Braze campaigns?

## What Markin does in Braze

- Read delivery, open, click and conversion events.
- Read Canvas and campaign exposure history per user.
- Read subscription groups and channel eligibility.
- Detect fatigue and suppress contacts that cost more than they return.
- Write a custom attribute with the chosen action, reason and expiry.
- Fire a trigger event that starts or advances a Canvas.
- Hold back a randomised control inside the same audience.
- Read the outcome back keyed to the decision ID.
- Arbitrate between competing Canvases so only one fires.
- Report incremental revenue per Canvas against its holdout.

## The growth work behind Braze

### Read the estate

- Reconcile the same customer across billing, CRM, product and support identities.
- Read consent, subscription and channel eligibility state before anything else.

### Find where revenue is leaking

- Detect involuntary churn from failed payments, card expiry and retry behaviour.
- Spot onboarding steps where activation falls off and revenue never starts.

### Explain why

- Keep the query trail so an analyst can reproduce every number.
- Run the investigation automatically and return the drivers with their evidence.

### Write hypotheses worth funding

- Write hypotheses continuously across marketing, product, pricing and technical health.
- Attach the expected revenue effect and the population it applies to.

### Decide per customer

- Suppress an action rather than send a weak one, and log why.
- Choose the next best action for each customer, for each moment.

### Execute in the tools you already run

- Roll a decision batch back cleanly when something looks wrong.
- Leave the estate exactly as it was if Markin stops writing.

### Prove it caused the revenue

- Read uplift in revenue per customer against that control.
- Report retention, ARPU, margin and contact pressure side by side.

### Retire, govern and hand over

- Show the whole decision trail when legal, finance or an auditor asks.
- Hand the team a portfolio they can read, question and overrule.

Full catalogue: [Everything a growth team does, running every day.](https://markin.ai/growth-work)

## Activation patterns

- **Attribute write-back.** Markin writes the decision onto the customer profile; your existing journeys read it as an entry condition. Latency is the platform's sync interval.
- **Triggered event.** Markin emits an event that starts or advances a journey. Near-immediate, and the channel keeps its own governance.

## What Markin reads from Braze

- Delivery, open, click and conversion events
- Campaign and canvas exposure history
- Subscription and channel eligibility state

## What Markin writes back

- Custom attributes carrying the chosen action, reason and expiry
- Trigger events that start or advance a Canvas

## Used together with

- [segment](https://markin.ai/integrations/segment)
- [snowflake](https://markin.ai/integrations/snowflake)
- [amplitude](https://markin.ai/integrations/amplitude)
- [stripe](https://markin.ai/integrations/stripe)
- [hightouch](https://markin.ai/integrations/hightouch)
- [zendesk](https://markin.ai/integrations/zendesk)

## Related

- [Solution: lifecycle-automation](https://markin.ai/solutions/lifecycle-automation)
- [Solution: churn-prevention](https://markin.ai/solutions/churn-prevention)
- [Comparison](https://markin.ai/compare/markin-and-braze)

## FAQ

**How does Markin connect to Braze?**

Attribute write-back, Triggered event. Markin writes the decision onto the customer profile; your existing journeys read it as an entry condition. Latency is the platform's sync interval.

**What does Markin read from Braze?**

Delivery, open, click and conversion events; Campaign and canvas exposure history; Subscription and channel eligibility state.

**What does Markin write back into Braze?**

Custom attributes carrying the chosen action, reason and expiry Trigger events that start or advance a Canvas

**Do we have to move our data to Markin?**

No. Markin reads from your warehouse, product events and operational systems in place, on your compute, under the access rules your data team already set. Nothing is copied into a separate customer base and there is no vendor-side profile store to migrate off later.

**Does Markin replace our engagement platform or CDP?**

No, and it should not. Your engagement platform keeps the channel, the templates, the deliverability and the governance. Your CDP keeps identity and consent. Markin adds the layer neither has: deciding which action deserves to exist for each customer, and proving it against a holdout.

**What if the system we use is not listed?**

The four activation patterns cover almost everything: attribute write-back, triggered event, decision API and direct surface rendering. Any system that exposes an API, accepts a table, or can read a warehouse column can receive decisions. New connectors are built during deployment, typically in days.

Source: https://markin.ai/integrations/braze