---
title: Markin + Braze: from customer data to prioritised retention actions
url: https://markin.ai/compare/markin-and-braze
kind: stack
description: Braze orchestrates and delivers cross-channel journeys. Markin decides which revenue opportunity deserves one. How a decision layer works alongside Braze.
updated: 2026-09-03
---

# Markin + Braze: from customer data to prioritised retention actions

> Braze is a customer engagement platform: Canvas journeys, cross-channel delivery and message personalisation. Markin sits before it and decides which commercial opportunity is worth acting on for each customer, and what it is worth. The decision is made in Markin; the message is still built, personalised and delivered by Braze.

## In short

- Braze: How do we build, personalise and deliver this journey across channels? Output: Delivered messages, journey state and engagement reporting.
- Markin, the decision + execution layer: Which opportunity justifies contact for this customer, and which treatment wins? Output: A ranked, sized decision per customer,  including hold, pushed into Braze as the entry signal.
- A customer can qualify for several Canvases in the same week. Priority is resolved by frequency caps and eligibility rules, not by expected value.
- It decides which of the things you could send is the one that adds revenue, then hands that decision to Braze to execute.

## What each layer is

**Braze**, A customer engagement platform for designing and delivering cross-channel journeys,  email, push, in-app, SMS and web, with Canvas orchestration, Liquid personalisation and delivery analytics.

Answers: How do we build, personalise and deliver this journey across channels?

**Markin, the decision + execution layer**, A layer that ranks commercial opportunities per customer, sizes the revenue at stake, selects the treatment with the highest expected incremental value and decides when not to contact at all.

Answers: Which opportunity justifies contact for this customer, and which treatment wins?

## Side by side

|  | Braze | Markin, the decision + execution layer |
| --- | --- | --- |
| Question it answers | How do we build, personalise and deliver this journey across channels? | Which opportunity justifies contact for this customer, and which treatment wins? |
| Primary input | Audiences and user attributes, custom events, journey logic, content templates. | Customer context, outcomes, contact history, margins, constraints, past experiment results. |
| Primary output | Delivered messages, journey state and engagement reporting. | A ranked, sized decision per customer,  including hold, pushed into Braze as the entry signal. |
| Usual owner | CRM and lifecycle marketing. | Growth, data science and revenue leadership. |
| How it's measured | Deliverability, open and click rate, conversion attributed to the Canvas. | Incremental revenue and ARPU against a holdout. |

## What stays unsolved when Braze is running well

A mature Braze setup usually has more Canvases than the customer base can absorb. The platform delivers whatever it is asked to deliver; deciding what deserves to be asked is a separate job, and it is normally done in planning meetings and spreadsheets.

- A customer can qualify for several Canvases in the same week. Priority is resolved by frequency caps and eligibility rules, not by expected value.
- Campaign reporting is per-Canvas. Nothing sums the effect of everything a customer received into one revenue number.
- There is no explicit decision to leave a customer alone when no message has positive expected value.
- New hypotheses arrive at the speed the lifecycle team can write briefs, not at the speed the data changes.

## How the two run together

1. **Context in.** Markin reads customer context where it already lives,  warehouse, CDP, product and billing systems, plus outcome history. Braze exports of engagement and delivery events can be part of that context.
2. **Decision.** Markin generates and sizes revenue opportunities, ranks them per customer, chooses a treatment and assigns a control group. The output is one decision per customer, with an expected value attached.
3. **Activation back into Braze.** The chosen decision is written back as user attributes or a triggered event, so an existing Canvas picks it up and delivers it. Content, channel governance and send-time logic remain in Braze.

## Braze's own decisioning layer

Products: BrazeAI™, Sage AI by Braze, BrazeAI Decisioning Studio™, Intelligence Suite

Braze does have a decisioning layer, and it is a real one. It selects content, channel and timing per individual, inside Canvas, across the channels Braze itself sends. That is a different scope from deciding which commercial opportunity is worth pursuing in the first place.

**What it optimises**

- BrazeAI is presented as a suite of AI marketing and personalisation tools embedded across the Braze platform, with Sage AI as the collective name for capabilities integrated into Braze data flows and the execution stack. [Vendor page: Braze, BrazeAI product page](https://www.braze.com/product/brazeai)
- BrazeAI Decisioning Studio™ is positioned to power autonomous, individualised decisioning for customer engagement strategies, and became available through the Google Cloud Marketplace in December 2025. [Vendor page: Braze, BrazeAI Decisioning Studio via Google Cloud Marketplace (Dec 2025)](https://www.businesswire.com/news/home/20251202496602/en/Braze-Delivers-BrazeAI-Decisioning-Studio-Through-Google-Cloud-Marketplace)
- The Intelligence Suite is documented as a way to automate decision making from data-based insights within Braze. [Vendor docs: Braze Learning, Automate decisioning with the Intelligence Suite](https://learning.braze.com/automate-decisioning-with-the-intelligence-suite)

**Documented constraints**

- Decisioning operates through Canvas. It optimises the messages Braze itself sends; it is not an arbitration layer over channels and touchpoints outside the Braze estate. [Vendor page: Braze, BrazeAI product page](https://www.braze.com/product/brazeai)
- The unit of optimisation is content, channel and send time within a journey, not the revenue at stake behind the journey, its margin, or whether the journey should run at all. [Vendor page: Braze, BrazeAI product page](https://www.braze.com/product/brazeai)

**Evidence**

- The only quantified performance research for Decisioning Studio is a Total Economic Impact™ study conducted by Forrester Consulting and commissioned by Braze (May 2026). The figures sit behind a registration form, and the study is vendor-funded rather than independent Forrester research. [Vendor-commissioned: Forrester TEI, commissioned by Braze (May 2026)](https://tei.forrester.com/go/braze/aidecisioningspotlight/)
- Braze's April 2026 research announcement references the same TEI study and a further commissioned report; no independent benchmark of Decisioning Studio uplift is published. [Vendor page: Braze research announcement (April 2026)](https://www.businesswire.com/news/home/20260423105729/en/Braze-Unveils-New-Research-Showing-How-AI-Data-and-Decisioning-Are-Redefining-Customer-Engagement)

**Where Markin differs**

- **Decides across the estate, not one channel.** Markin arbitrates between everything a customer could receive this week,  Braze journeys, service contact, in-product placements, field or care outreach, and picks one. Braze optimises what it sends; Markin decides whether Braze should be the one sending.
- **Optimises revenue, not engagement.** Decisioning Studio ranks by likelihood of engagement. Markin ranks by expected incremental revenue net of margin and contact cost, which is why hold is a legitimate output.
- **Measured against holdout, by default.** Every Markin decision carries a control group and reports incremental ARPU against it, rather than conversion attributed to a Canvas.

## What Markin does not replace

To be explicit about scope, because procurement will ask:

- Markin does not send email, push, SMS or in-app messages. Delivery stays in Braze.
- Markin does not manage templates, brand controls or channel credentials.
- Markin is not a system of record for consent or communication preferences.
- Markin does not replace Canvas orchestration; it supplies the signal that starts the right one.

## Where Markin fits

Markin does not send messages. It decides which of the things you could send is the one that adds revenue, then hands that decision to Braze to execute. Lifecycle keeps its templates, its brand controls and its channel expertise; what changes is the input that triggers the journey.

- **Canvas entry becomes a decision, not a rule.** Instead of an eligibility segment, the entry signal is a ranked opportunity with an expected value, so the highest-value action wins the customer's attention that week.
- **Contact budget is allocated, not capped.** Frequency caps stop over-messaging. Decisioning goes further: it spends each contact on the opportunity with the best expected return, and holds when none clears the bar.
- **Measured against control, not attributed.** Every decision carries a holdout, so the reported number is incremental revenue rather than conversions credited to a send.

Next: [Customer Decisioning](https://markin.ai/solutions/customer-decisioning)

## When Markin is not the right answer

- You run a handful of lifecycle journeys and the team can still reason about priority in a single meeting.
- Your customer data is not yet reliable enough to size opportunities: fix ingestion and identity first.
- You want a cheaper way to send messages. This layer does not replace an engagement platform and does not reduce its cost.

## FAQ

**Does Braze already have AI decisioning?**

Yes. BrazeAI Decisioning Studio™ selects content, channel and timing per individual inside Canvas, and Sage AI powers predictive and generative features across the platform. Its scope is the messages Braze sends. It does not size the revenue opportunity behind a journey, arbitrate against touchpoints outside Braze, or decide that no contact is the best option. The only published performance research is a Forrester Total Economic Impact study commissioned by Braze (May 2026).

**Do we have to replace Braze?**

No. Braze remains the execution layer. Markin decides which opportunity is worth a message and passes that decision into Braze, which builds and delivers it exactly as it does today.

**How does the decision reach Braze?**

As customer attributes or triggered events on the profile, so existing Canvases can key off them. The integration surface is the same one your team already uses for any other upstream signal.

**Does this add another audience-building tool?**

No. Markin produces ranked, sized opportunities per customer rather than segments. Audience building for broadcast and brand campaigns stays where it is.

**Who owns the output, marketing or data?**

Both, deliberately. Data science owns the models and the experiment design; lifecycle owns the treatments and the channel. The decision layer is the shared contract between them.

**What does the first ninety days look like?**

One revenue theme, one channel, a real holdout. The point of the first quarter is a defensible incremental number, not full coverage of the journey map.

**How is Markin different from the decisioning or AI already inside Braze?**

A decisioning engine ranks actions a human already defined, inside the campaign surface it was given. Markin forms the hypotheses itself,  marketing, product, pricing or a technical anomaly holding growth back, sizes them, executes them inside Braze and your product surfaces, and reads each one against a randomised holdout. It behaves like a data science and growth team, not like an optimiser.

**Does Markin only test messages and offers?**

No. Anything a human growth scientist would investigate is in scope: onboarding friction, feature adoption, pricing and packaging, dunning, and technical health issues such as a checkout error rate or a broken deeplink quietly killing conversion. Marketing is one of four hypothesis domains, not the boundary.

**What is the business case for adding Markin on top of Braze?**

On the assumptions preloaded above, 4.0M customers at 18 a month, a small move in ARPU is a large number in absolute terms, because it applies to the whole installed base every month rather than to a campaign. Across Markin deployments the verified range on treated cohorts is +17% to +35% ARPU against a randomised holdout. The point is not more messages: it is finding the highest-value action per customer, launching it, and proving it against control before it scales.

**How long before it pays for itself?**

First sized opportunities are in test within six weeks and the first holdout-verified result lands inside 90 days. Payback depends on your base, margin and programme cost, the calculator on this page computes it from your own numbers, after applying the 20% to 40% haircut BCG finds when next-best-action programmes are incrementality-tested.

Source: https://markin.ai/compare/markin-and-braze