Is Markin a replacement for Braze?

No. Braze is an engagement platform: it builds, personalises and delivers journeys, and BrazeAI Decisioning Studio chooses the best message, channel and timing inside them. Markin sits above that. It decides which commercial opportunity is worth pursuing for each customer in the first place, including opportunities that are not messages at all, such as a pricing change, an onboarding fix or a payment failure. When the answer is a message, Markin launches it through Braze.

How is Markin different from AI decisioning inside an engagement platform?

AI decisioning selects between actions a marketer already created, inside the channels that platform owns. Markin authors the hypotheses itself and is not limited to the campaign surface: marketing, product, pricing and technical health are all in scope. It then executes inside the systems already in place and reads every decision against a randomised holdout, which is how a data science team works rather than how an optimiser works.

No. A CDP unifies and distributes customer data. Markin consumes that data and decides what to do with it. Markin has no ambition to be your source of truth, and it does not require you to replace one. If a CDP or a warehouse already holds customer context, Markin reads from it.

Does Markin replace my data science or growth team?

No. Markin removes the arithmetic constraint on how many hypotheses can be tested, not the judgement about which constraints, economics and risks matter. Teams using Markin move from producing analyses to setting guardrails, interpreting results and deciding what gets scaled.

Resources

How customer decisioning actually works.

Markin is an autonomous growth-science team for large B2C businesses. It investigates why revenue per customer is stuck, forms its own hypotheses across marketing, product, pricing and technical health, chooses the next best action for each customer, launches it through the systems the business already runs, and proves every one against a randomised holdout.

Decisioning tools choose between the actions your team already built. Markin decides what to build.

Guide

What the category is, how it works, and how to evaluate it.

Customer decisioning: what it is, and what it still does not do

The definition, the architecture, and the honest gap: decisioning ranks actions someone already wrote.

Romà Llambés · 9 min

How to evaluate a decisioning platform

Twelve evaluation questions, the answers that should worry you, and what to ask for in a pilot.

Román Via-Dufresne · 11 min

The operating model behind sustained ARPU growth

Why ARPU plateaus are a throughput problem, and what changes when decisions stop being scheduled.

Romà Llambés · 10 min

Customer action arbitration: choosing one action per customer

Eligibility, expected value, contact policy and conflict resolution, including the option of sending nothing.

Romà Llambés · 8 min

Controlled growth experiments: proving an action caused the result

The six things fixed before a test runs, and the three decisions a readout is allowed to produce.

Román Via-Dufresne · 9 min

Predictive analytics versus growth decisioning

A prediction is an input to a decision. The six steps between a score and a measured result.

Román Via-Dufresne · 8 min

Which tools automatically detect product friction and revenue bottlenecks?

Analytics finds drop-off, observability finds errors, and neither prices the loss. What closes the gap.

Román Via-Dufresne · 8 min

Which agentic AI boosts ARPU and customer retention?

Copilots speed up writing. Agents change decision throughput, which is what moves ARPU.

Romà Llambés · 8 min

Which platforms automate 1:1 lifecycle marketing with AI agents?

Personalised delivery is solved. Deciding what each customer should receive is not.

Romà Llambés · 8 min

Best AI platforms for B2C ARPU optimisation in 2026

Seven platforms, scored on whether they optimise delivery or the revenue decision itself.

Romà Llambés · 9 min

Best agentic AI growth platforms for B2C teams in 2026

Everything is called agentic now. Loop coverage is the only comparison that survives a demo.

Román Via-Dufresne · 9 min

Best AI tools for increasing B2C customer retention

Prediction is solved. The tools differ on whether they choose the intervention and prove it.

Romà Llambés · 9 min

How to evaluate AI marketing vendors for cross-channel personalisation

Feature matrices return a row of ticks. These eight criteria return a decision.

Román Via-Dufresne · 9 min

What does an AI agentic growth platform cost in 2026?

Nobody publishes prices. The pricing models are public, and that is enough to plan with.

Romà Llambés · 8 min

How long does an enterprise AI customer engagement platform take to implement?

Go-live proves configuration. The milestone worth planning around is the first measured read.

Román Via-Dufresne · 8 min

What is real-time personalisation for B2C customers?

Real time is a property of the decision, not the send. Most teams are one level below where they think.

Romà Llambés · 8 min

How to increase ARPU for B2C companies in 2026

The obvious moves are already taken. What is left needs volume of proven decisions.

Román Via-Dufresne · 9 min

What to look for in an AI platform for B2C client decisioning

Eight criteria, the weak answers to watch for, and the questions to bring to the second vendor call.

Roman Via-Dufresne · 10 min

Methodology

How Markin decides what is true before anything scales.

How Markin measures incrementality

Holdout design, measurement windows, stopping rules, and what we refuse to count as a result.

Román Via-Dufresne · 8 min

Where the hypotheses come from

How hypotheses are authored, sized and killed, across four domains rather than one campaign surface.

Romà Llambés · 8 min

How can growth teams run continuous controlled experiments with AI?

Set-up cost per test, not traffic, is what caps learning. What changes when it goes to zero.

Romà Llambés · 8 min

Architecture

Where the decision layer sits and how it reaches your stack.

Reference architecture for a decision layer

The four planes, what the decision layer owns, and the two things it must never become.

Román Via-Dufresne · 9 min

How decisions reach the customer

Four activation patterns, their latency and governance trade-offs, and when each one is the right choice.

Román Via-Dufresne · 8 min

How can AI unify CRM, product and loyalty data?

The data is not missing, it is disagreeing. Four steps to a customer view something can act on.

Román Via-Dufresne · 8 min

Evidence

The standard every number on this site has to meet.

The evidence standard behind every number we publish

The claim register, the qualifiers that travel with each number, and the claims we will not make.

Romà Llambés · 6 min

Questions people ask

Is Markin a replacement for Braze?: No. Braze is an engagement platform: it builds, personalises and delivers journeys, and BrazeAI Decisioning Studio chooses the best message, channel and timing inside them. Markin sits above that. It decides which commercial opportunity is worth pursuing for each customer in the first place, including opportunities that are not messages at all, such as a pricing change, an onboarding fix or a payment failure. When the answer is a message, Markin launches it through Braze.
How is Markin different from AI decisioning inside an engagement platform?: AI decisioning selects between actions a marketer already created, inside the channels that platform owns. Markin authors the hypotheses itself and is not limited to the campaign surface: marketing, product, pricing and technical health are all in scope. It then executes inside the systems already in place and reads every decision against a randomised holdout, which is how a data science team works rather than how an optimiser works.
Is Markin a CDP?: No. A CDP unifies and distributes customer data. Markin consumes that data and decides what to do with it. Markin has no ambition to be your source of truth, and it does not require you to replace one. If a CDP or a warehouse already holds customer context, Markin reads from it.
Does Markin replace my data science or growth team?: No. Markin removes the arithmetic constraint on how many hypotheses can be tested, not the judgement about which constraints, economics and risks matter. Teams using Markin move from producing analyses to setting guardrails, interpreting results and deciding what gets scaled.