COMPARE/Markin vs Braze
vsvs BrazeAI Decisioning Studio: who writes the list
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
BrazeAI Decisioning Studio chooses content, channel and timing per individual, inside the messages Braze 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.
The short answerLast updated: August 2026
Both are called decisioning, and both are real. 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.
01
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
02
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
The same ten questions, answered for both.
| 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 is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
2.0M
customers at $24 ARPU / month
Addressable revenue
$259.2M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$44.1M – $90.7M
incremental revenue per year
Measured on treated cohorts against a randomised holdout, read over a full measurement window rather than the first weeks. Anonymised range across Markin deployments in large B2C bases; your own holdout is the number that decides. The figures above apply that range to the reachable share of the base on this page's assumptions; they are arithmetic, not a forecast for your business.
Run it on your own numbersThe unsolved part
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.
Hypothesis space
Everything a human growth scientist would look at.
Most growth problems are not message problems. Markin is not restricted to the campaign surface: if something is holding ARPU back, it is in scope, and it gets tested the same way.
Marketing
The classic surface, but chosen per customer rather than per segment, and always against a holdout.
- Which offer this specific customer is worth making
- Channel and timing chosen per person, not per campaign
- Contact pressure and fatigue arbitrated across every programme
- Win-back economics: who is worth a discount and who is not
Product
Where the customer actually experiences the value, and where most silent revenue loss happens.
- Onboarding steps that lose customers before first value
- A feature with high retention correlation that half the base never discovers
- Paywall and upgrade prompt placement
- In-product surfaces used as a treatment arm, not just email and push
Commercial
Pricing, packaging and the shape of the offer itself, tested rather than argued about.
- Plan and bundle structure by cohort
- Discount depth against margin, not against conversion alone
- Annual versus monthly framing per customer
- Dunning and involuntary churn recovery sequences
Technical health
Anomalies nobody asked it to look for. This is the category no decisioning engine covers.
- A checkout error rate that rose on one device and one region
- Payment failures concentrated in a single issuer or method
- A broken deeplink quietly killing a high-value journey
- Latency or delivery degradation eating conversion before any message does
Think of Markin as a data science and growth team that never sleeps: it investigates, forms hypotheses, ships them into your own stack and proves each one against a control group, at a volume no human team can reach.
Job to be done
The same work, at a different throughput.
Nothing below needs a tool that does not exist. It needs the work to happen continuously instead of once a quarter, and to be proven against a holdout instead of argued about.
| Job to be done | With brazeai decisioning studio alone | With Markin |
|---|---|---|
| Notice that revenue per customer is drifting in a segment | Someone spots it in a dashboard review, weeks after it started. | Detected as a signal the day the drift clears noise, with the segment already sized. |
| Explain why it is happening | An analyst is pulled off the roadmap for a two-week investigation. | An investigation runs automatically and returns the drivers with their evidence. |
| Come up with hypotheses worth testing | A workshop produces the handful of ideas the room happened to think of. | Hypotheses are written continuously across marketing, product, pricing and technical health. |
| Decide which hypotheses deserve budget | Prioritised by seniority and gut feel, with no size attached. | Each one is sized in revenue and ranked before anything is built. |
| Choose the next best action for one customer | Segment rules and campaign calendars decide, refreshed when someone has time. | Chosen per customer, per moment, against everything else competing for that customer. |
| Actually launch it | A ticket to the lifecycle team, then a slot in next month's calendar. | Executed inside the systems you already run, with no new channel to adopt. |
| Prove it caused the revenue | Reported against non-qualifiers or a global holdout, if at all. | Every decision carries a randomised control group; uplift is read against it. |
| Kill what does not work | Programmes survive because nobody owns retiring them. | Failing to beat control retires the programme automatically. |
| Do all of it again next week | Capacity-bound: four to eight tests a quarter. | Hundreds of hypotheses in flight in parallel, continuously. |
Honest take
What BrazeAI Decisioning Studio does better.
A comparison that only flatters one side is not worth reading. These are the cases where we would tell you to stay where you are.
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.
Where Markin fits
Not a replacement. A growth-science team on top.
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.
Evidence standard
Most of this category reports its own lift.
None of the major engagement, CDP or personalisation vendors publishes an independently verified uplift figure for its decisioning product. Where numbers exist, they come from vendor-commissioned studies or single-customer case studies with no disclosed holdout methodology. The most rigorous public research in the category is not flattering to anyone, including us, which is exactly why we build against it.
BCG reports that when organisations adopt rigorous incrementality testing, they typically find 20% to 40% of their active next-best-action programmes deliver marginal to negative lift.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)The same research flags novelty effects, new programmes show inflated early results, and recommends 8 to 12 weeks before drawing conclusions.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)Global-holdout, programme-level ROI measurements often overstate impact through halo effects, pull-forward effects and experiment contamination.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)
How Markin holds itself to it
- Every decision Markin makes carries a control group. Uplift is reported against that holdout, not against the customers who did not qualify.
- Results are read over a full measurement window rather than in the first weeks, so novelty is not mistaken for effect.
- Programmes that fail to beat control are retired automatically. Killing decisions that do not pay is part of the loop, not an annual review.
- The one figure we quote about ourselves is a range, not an average: +17% to +35% ARPU on treated cohorts against a randomised holdout, across Markin deployments in large B2C bases. We publish no industry benchmark, because we could not source one we would be willing to defend. Your holdout is the number that matters.
Time to value
90 days to a number that survived a holdout.
No replatform, no data migration, no rebuild of the channels you already run. If the first cohorts do not beat control, nothing scales and you have lost a quarter, not a roadmap.
Weeks 0–2
Read the context you already have
Markin connects to the data and the channels you run today, brazeai decisioning studio included. No migration, no replatform, no new source of truth.
Weeks 3–6
First sized opportunities in test
Opportunities are ranked by expected value, treatments are chosen per customer, and the first cohorts go live with a randomised holdout attached.
Weeks 7–12
First verified incremental revenue
Results are read over a full measurement window. What beats control scales; what does not is retired. Nothing scales on a number that has not survived a holdout.
Which one you should 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.
When you don’t need Markin.
- 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.
Questions buyers ask.
Is Markin an alternative to BrazeAI Decisioning Studio?
Not really, and pretending otherwise would be dishonest. 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 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.