---
title: Braze Decisioning Studio vs Markin: who writes the list
url: https://markin.ai/compare/markin-vs-braze-ai-decisioning-studio
kind: vs
description: Braze Decisioning Studio picks the best message inside Canvas. Compare what each layer decides, where it runs and how incremental revenue is measured.
updated: 2026-08-31
---

# Braze Decisioning Studio vs Markin: who writes the list

> Braze Decisioning Studio is the AI decisioning layer inside Braze: it chooses content, channel and send time per individual, inside the messages Braze already sends. Markin decides which commercial opportunity is worth pursuing for that customer at all, across marketing, product and pricing, and proves it against a holdout. One optimises the list; the other writes it.

## In short

- BrazeAI Decisioning Studio: Which message, on which channel, at what time, for this person? Output: A delivered, individually selected message inside a Canvas.
- Markin: Which opportunity is worth acting on for this customer, and what is it worth? Output: A ranked, sized decision per customer, including hold, executed through your stack.
- Nothing sizes the revenue at stake behind a journey before it is built.
- The Canvas entry stops being an eligibility segment and becomes a ranked, sized decision with a control group attached.

## The short answer

Both are called decisioning, and both are real. Braze Decisioning Studio optimises what Braze sends, message, channel and time, inside Canvas. Markin sits earlier: it forms hypotheses about why ARPU is stuck, sizes them, picks one action per customer including hold, and measures it against a control group.

## The two options

**BrazeAI Decisioning Studio**, The AI decisioning layer inside Braze. It selects content, channel and send time per individual across the channels Braze itself delivers, inside Canvas journeys.

Choose it when the journeys are already written and the job is to send the best variant of them to each person.

- Runs inside Canvas
- Optimises message, channel, timing
- Braze-delivered channels

**Markin**, An autonomous growth-science team for large B2C bases. It investigates the base, forms and sizes its own hypotheses, chooses one action per customer, launches it through your stack and reads it against a holdout.

Choose it when the bottleneck is how many good hypotheses get tested and proven, not how well the existing ones are delivered.

- Decides before the journey
- Holdout on every action
- Executes in Braze

## Line by line

| Dimension | Markin | BrazeAI Decisioning Studio |
| --- | --- | --- |
| What it is | An autonomous growth-science team: it investigates why revenue per customer is stuck and acts on what it finds. | The AI decisioning layer of a customer engagement platform, embedded in Canvas. |
| What it decides | Which commercial opportunity deserves to exist for each customer this week, what it is worth, and when the right answer is to do nothing. | Which message, channel and send time each individual gets inside a journey a human designed. |
| Where hypotheses come from | Generated by Markin from customer, product, pricing and technical-health data, then sized before anyone builds anything. | Written by the lifecycle team as Canvases, campaigns and eligibility rules. |
| Scope of action | Marketing, product, pricing and technical-health hypotheses, arbitrated against each other in one queue. | Messaging: email, push, in-app, SMS and web delivered by Braze. |
| How the work reaches the customer | Written back into the systems you already run, as attributes, events or API calls. Markin does not add a new customer-facing surface. | Braze delivers the message itself, which is what it is built to do well. |
| How impact is proven | A randomised holdout on every decision. The reported number is incremental revenue and ARPU, not attributed conversions. | Conversion attributed to the Canvas, plus built-in variant testing. The published performance research is a Forrester TEI study commissioned by Braze. |
| Where the data sits | Reads context where it already lives, warehouse, CDP, product and billing systems. No new system of record. | Braze user profiles and custom events, fed from your warehouse or CDP. |
| Governance and control | Every action carries its hypothesis, its expected value, its guardrails and its control group, reviewable before launch. | Channel governance, frequency caps, brand controls and consent handling. |
| Time to a verified number | One revenue theme, one channel, one holdout: a defensible incremental number inside 90 days. | Fast for a new Canvas; the constraint is how quickly the team can write the next one. |
| Best fit | Large B2C bases where the constraint is how many good hypotheses get tested, not how many messages get sent. | Teams whose messaging programme is the growth programme. |

## What each layer is

**BrazeAI Decisioning Studio**, The decisioning layer inside Braze. Selects content, channel and timing per individual across the channels Braze delivers.

Answers: Which message, on which channel, at what time, for this person?

**Markin**, A growth-science layer that generates and sizes hypotheses, picks one action per customer and proves it against a holdout.

Answers: Which opportunity is worth acting on for this customer, and what is it worth?

## Side by side

|  | BrazeAI Decisioning Studio | Markin |
| --- | --- | --- |
| Question it answers | Which message, on which channel, at what time, for this person? | Which opportunity is worth acting on for this customer, and what is it worth? |
| Primary input | Braze profiles, custom events, journey logic, content variants. | Warehouse and product context, margins, contact history, past experiment results. |
| Primary output | A delivered, individually selected message inside a Canvas. | A ranked, sized decision per customer, including hold, executed through your stack. |
| Usual owner | CRM and lifecycle marketing. | Growth, data science and revenue leadership. |
| How it's measured | Engagement and conversion attributed to the Canvas. | Incremental revenue and ARPU against a randomised control group. |

## What BrazeAI Decisioning Studio does better

- **Delivery is genuinely hard, and Braze is very good at it.** Deliverability, throughput, channel governance, consent, localisation and template management at hundreds of millions of sends a month. Markin does none of that and should not: it hands the decision to Braze and lets Braze do the part it is world-class at.
- **Message-level optimisation is closer to the send.** Choosing the subject line, the channel and the hour for one individual, at send time, is best done where the send happens. Decisioning Studio has context Markin deliberately does not carry.
- **One vendor is a real advantage.** If your programme is small enough that the same team writes and sends everything, adding a second system costs more than it returns. Braze alone is the right answer more often than we would like to admit.

## Which one to pick

**Choose Markin if**

- Your base is large enough that a customer qualifies for several journeys in the same week and priority is settled by caps, not by value.
- You want the reported number to be incremental revenue against a control group, not conversions credited to a Canvas.
- The hypotheses that would move ARPU are not all messaging hypotheses: pricing, product friction and technical anomalies matter too.
- You need a decision that can legitimately be do not contact.
- You want more tested hypotheses per quarter without hiring proportionally.

**Choose Braze alone if**

- Your growth programme is your messaging programme, end to end.
- The lifecycle team can still reason about priority across journeys in one meeting.
- Customer data is not yet reliable enough to size opportunities in money.
- You want one vendor, one contract and one surface.
- The current constraint is send quality and deliverability, not hypothesis supply.

## What Decisioning Studio does not answer

Optimising the variant assumes the campaign deserved to exist. In a mature Braze estate there are usually more journeys than the base can absorb, and no system decides which of them earns the customer's attention this week.

- Nothing sizes the revenue at stake behind a journey before it is built.
- Nothing arbitrates a Braze message against an in-product placement, a service contact or a pricing change.
- Reporting is per-Canvas, so no single number says what everything a customer received was worth.
- Hypotheses arrive at the speed briefs are written, not at the speed the data changes.

## Where Markin fits

Markin runs before Braze and executes through it. The Canvas entry stops being an eligibility segment and becomes a ranked, sized decision with a control group attached.

- **Braze keeps the channel.** Templates, brand controls, consent and delivery stay exactly where they are.
- **Markin supplies the reason to send.** The entry signal carries an expected value, so the highest-value action wins the week.
- **Every action carries a holdout.** Incremental ARPU is measured, not attributed.

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

## When Markin is not the right answer

- You run a handful of journeys and priority is obvious.
- You want a cheaper way to send messages: this does not replace or reduce Braze.
- Your identity and event data are not yet trustworthy enough to size opportunities.

## FAQ

**What is Braze Decisioning Studio?**

Braze Decisioning Studio, also written BrazeAI Decisioning Studio, is the AI decisioning layer inside Braze. It selects content, channel and send time for each individual across the channels Braze delivers, inside Canvas journeys, so a single journey adapts per person instead of running one fixed variant.

**Is Markin an alternative to Braze Decisioning Studio?**

Not really, and pretending otherwise would be dishonest. Braze Decisioning Studio optimises the messages Braze sends. Markin decides which commercial opportunity deserves a message at all, across channels and beyond messaging, then hands the chosen action to Braze to deliver. Most customers run both.

**Do we have to replace Braze?**

No. Braze stays the execution layer. Markin writes the decision into Braze as an attribute or event and an existing Canvas picks it up.

**What does Braze do better?**

Delivery at scale: deliverability, throughput, channel governance, consent, localisation and templates. Also send-time and channel selection for an individual message, which is best decided where the send happens.

**How much does Braze Decisioning Studio cost?**

Braze does not publish pricing for Decisioning Studio; it is quoted as part of a Braze contract and depends on monthly active users and the channels in scope. Ask Braze for the incremental cost on top of your current plan, and compare it with the incremental revenue you can prove with a holdout.

**How is the impact measured differently?**

Braze reports conversions attributed to a Canvas. Markin assigns a randomised holdout to every decision and reports the difference in revenue per customer between treated and control.

**Can Markin decide not to contact someone?**

Yes, and it frequently does. Hold is a legitimate output when no available action has positive expected value net of margin and contact cost.

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

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 brazeai decisioning studio 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 brazeai decisioning studio?**

On a large B2C base, 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-vs-braze-ai-decisioning-studio