COMPARE/Markin and your stack
Markin + Braze: from customer data to prioritised retention actions
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
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.
What is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
4.0M
customers at $18 ARPU / month
Addressable revenue
$535.7M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$91.1M – $187.5M
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 numbersWhat your stack does today.
01
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.
02
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.
Side by side
The differences that change outcomes.
| Dimension | 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. |
The unsolved part
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.
The actual difference
Markin is not another decisioning engine.
Markin is not a decisioning engine. A decisioning engine ranks actions a human already defined. Markin works like a data science and growth team: it forms its own hypotheses about why ARPU is stuck, marketing, product, pricing or technical, sizes them, executes them inside the systems you already run, and reads each one against a holdout.
| A decisioning engine | Markin | |
|---|---|---|
| Where the hypothesis comes from | A human authors it. The engine chooses between options someone already approved. | Markin authors it. It reads the base, finds where revenue is leaking or unclaimed, and writes the hypothesis itself. |
| What it is allowed to question | Message, offer, channel, timing, inside the campaign surface it was given. | Anything that moves ARPU: onboarding friction, pricing and packaging, a feature nobody adopts, a payment failure spike, a broken deeplink. |
| Who does the analysis | Your analysts, before and after. The engine optimises; it does not investigate. | Markin does the analysis. Sizing, segment definition, experiment design and readout are automated end to end. |
| Where it stops | At the recommendation. Someone still has to build and launch it. | It launches. Markin executes inside your existing platforms and product surfaces, then closes the loop on the result. |
| Throughput | As many hypotheses as your roadmap has room for, typically a handful per quarter. | Hundreds in parallel, every one carrying a control group. |
| What happens when it is wrong | The programme keeps running until someone reviews it. | It is retired automatically. Failing to beat control is a normal, cheap outcome. |
A decisioning engine picks the best action from a list you wrote. Markin writes the list, and runs it in your stack.
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.
Their decisioning layer
What Braze decides — and where it stops.
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.
Products referenced: BrazeAI™, Sage AI by Braze, BrazeAI Decisioning Studio™, Intelligence Suite
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 pageBraze, BrazeAI product pageBrazeAI 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 pageBraze, BrazeAI Decisioning Studio via Google Cloud Marketplace (Dec 2025)The Intelligence Suite is documented as a way to automate decision making from data-based insights within Braze.
Vendor docsBraze Learning, Automate decisioning with the Intelligence Suite
Documented boundaries
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 pageBraze, BrazeAI product pageThe 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 pageBraze, BrazeAI product page
What the evidence actually says
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-commissionedForrester TEI, commissioned by Braze (May 2026)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 pageBraze research announcement (April 2026)
Where Markin is different.
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.
Architecture
How the two run together
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.
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.
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.
The last step is execution, not a hand-off. Markin does not email a recommendation to someone who then has to build it: it launches the treatment inside Braze and your product surfaces directly, with the holdout attached, and reads the result itself.
The loop
Execution is a step in the loop, not a hand-off.
- 01
Observe
Markin reads the behavioural, transactional and product signal you already collect, continuously.
- 02
Hypothesise
It writes the hypothesis itself, marketing, product, commercial or technical, and states the expected direction.
- 03
Size
Each opportunity is ranked by expected value, so the queue is ordered by money rather than by opinion.
- 04
Design
Segment, treatment, guardrails and a randomised holdout are set before anything ships.
- 05
Execute
It launches inside the systems you already run, your engagement platform, your product surfaces, your APIs. Nothing waits on a build queue.
- 06
Read
Results are measured against the holdout over a full window, so novelty is not mistaken for effect.
- 07
Scale or retire
What beats control is scaled across the base. What does not is switched off automatically.
Where Markin fits
Not a replacement. A growth-science team on top.
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.
Operating model
The constraint is not ideas. It is how many you can test.
| Today, with Braze | With Markin on top | |
|---|---|---|
| Revenue hypotheses tested per quarter | 4 to 8, whatever the roadmap had room for | Hundreds, generated and run in parallel |
| What can be hypothesised about | Messages, offers and audiences, the campaign surface | Marketing, product, pricing and technical health alike |
| From decision to live in the channel | A ticket, a build queue, a release window | Markin launches it in your existing platforms itself |
| Time from idea to a result you trust | 6 to 10 weeks of analysis, build and readout | Days, because sizing and design are automated |
| Share of decisions with a control group | The flagship programmes, when there is time | Every decision, by default |
| Coverage of the base | Top segments and the customers a rule caught | One decision per customer, across the whole base |
| Cost of testing the 500th hypothesis | Another analyst, another quarter | Effectively zero |
| What the team spends its time on | Pulling data, building lists, reconciling reports | Judgement: constraints, economics, what to scale |
Markin does not replace your data science team. It removes the ceiling on how much of the base that team can act on, and how fast it finds out whether it worked.
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.
- Markin does not sit beside Braze making suggestions. It drives it, the action is launched there, in the system your team already knows, and the result comes back into the loop.
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.
Size it yourself
Size the decision layer on top of your Braze programme
Preloaded for a consumer subscription business running Braze at scale: high reach, frequent contact, a mature messaging team. Braze optimises the messages it sends. The figure below is the incremental margin available from deciding what is worth sending at all, including deciding not to contact, measured against a holdout rather than attributed to a Canvas.
Your base
Accounts that generated revenue in the last 30 days. Not registered users.
Recurring plus non-recurring revenue divided by active customers.
Margin on the next unit sold, not blended company margin.
Your programme today
Consented, non-fatigued, reachable on at least one channel.
Revenue lost to cancellations each month, as a share of the base.
The bet
Licences, data, incentives and the people running it.
Before any incrementality haircut. 2–4% is a defensible planning assumption.
Verified annual impact
$6.7M
Net incremental gross margin in the central case, after the programme cost and after the share of decisioning programmes that independent research finds deliver no real lift.
Reported uplift
$16.1M
What a before/after dashboard would claim, with no control group.
Verified uplift
$11.2M
What survives a holdout in the central case.
Return on programme cost
6.6×
Payback
2 mo
If 20–40% of it does nothing
What it takes to prove it
To detect a 3% lift on revenue per customer you need roughly 40K customers in the control arm, about 1.6% of your addressable base, read over at least 8 weeks, so novelty is not mistaken for effect.
Addressable base
2.5M
Revenue at risk from churn
$279.2M
Annualised, at the current monthly rate.
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, Braze 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.
When you don’t need Markin.
- 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.
Questions buyers ask.
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.