Do I have to replace Braze or Iterable to use an AI decision layer?

No. Markin is designed to drive the channels you already run. The engagement platform keeps delivery, deliverability and channel governance; the decision layer supplies who, what, when and why, and takes responsibility for measurement.

What makes lifecycle marketing agentic rather than automated?

Automation executes rules a person wrote. Agentic means the system proposes the intervention itself, sizes it, runs it inside guardrails and updates its own behaviour from the measured outcome without waiting for the next planning cycle.

How does an agent avoid over-contacting customers?

By treating contact as a cost. If the expected incremental value of messaging a customer today is below the cost of the contact and its effect on future receptivity, the winning action is no contact. That is stronger than a frequency cap, which only limits volume.

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/Guide

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

Platforms that automate 1:1 lifecycle marketing with AI agents split into two layers. Execution platforms such as Braze, Iterable, MoEngage, Optimove, Adobe and Salesforce deliver personalised journeys at scale with AI assistance inside them. A decision layer such as Markin sits above those channels and decides, per customer, which action deserves to run, then proves it against a holdout.

Romà Llambés, Co-founder, Markin

Updated 18 August 2026 · 8 min read

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Definition

1:1 lifecycle marketing

Deciding and delivering the next action for each individual customer across their lifecycle, rather than sending scheduled campaigns to segments.

Personalised delivery is solved. Personalised decisions are not.

Most enterprises already have a platform capable of sending a different message to every customer. What they do not have is a system that decides what each message should be, whether it should be sent at all, and what it was worth. Segments are still drawn by hand, calendars still set the timing, and the number of distinct decisions per month is capped by the size of the CRM team.

A tool that can personalise 10 million messages still waits for a human to define the audience.
Competing campaigns optimise the same customer independently, and nobody arbitrates.
Contact pressure is managed with frequency caps rather than with value.

The two layers, and who does what

Confusing the layers is the most common buying mistake. Execution and decisioning are different jobs and the best stacks keep both.

01
Define the action catalogue
Every action a customer could receive, including doing nothing, with its cost, its margin impact and its eligibility rules.
02
Arbitrate per customer
One customer, many eligible actions, one winner. Arbitration on expected incremental value replaces campaign precedence and manual suppression lists.
03
Execute in the channels you already own
The decision is pushed into Braze, Iterable, MoEngage or your own product surfaces. Nobody has to migrate an engagement platform to get a decision layer.
04
Hold out and read
A share of the eligible population is deliberately untreated so the effect is incremental rather than attributed.

Layers in an agentic lifecycle stack

Layer	Vendors	Job	Limit
Customer data	Snowflake, BigQuery, Segment, mParticle	Unify identity, events and attributes.	Storing data is not deciding anything.
Decision layer	Markin	Write hypotheses, arbitrate competing actions per customer, run holdouts, read incremental margin.	Does not replace your channels; it drives them.
Execution and channels	Braze, Iterable, MoEngage, Optimove, Adobe, Salesforce	Deliver messages and journeys across email, push, in-app, SMS and web.	Optimises inside campaigns a person authored.

Is your lifecycle programme actually 1:1?

How many distinct audiences ran last month, and who defined them?
When two campaigns target the same customer, what decides the winner?
Is 'no contact' a possible outcome, or only a suppression rule?
What share of sends had a holdout?
Can you state the incremental margin of your lifecycle programme last quarter?

When you do not need a decision layer yet

Fewer than a few hundred thousand active customers, where a small team can still reason about every segment.
A single product with one meaningful action, where arbitration has nothing to arbitrate.
No reliable outcome data. Without a measured result, autonomy has nothing to learn from.

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.

Questions people ask

Which platforms automate 1:1 lifecycle marketing with AI agents?: Braze, Iterable, MoEngage, Optimove, Adobe Journey Optimizer and Salesforce Marketing Cloud automate personalised delivery and add AI assistance inside their journeys. Markin adds the agentic decision layer above them: it writes the hypotheses, arbitrates which action each customer should receive, runs the test against a holdout and reports incremental revenue.
Do I have to replace Braze or Iterable to use an AI decision layer?: No. Markin is designed to drive the channels you already run. The engagement platform keeps delivery, deliverability and channel governance; the decision layer supplies who, what, when and why, and takes responsibility for measurement.
What makes lifecycle marketing agentic rather than automated?: Automation executes rules a person wrote. Agentic means the system proposes the intervention itself, sizes it, runs it inside guardrails and updates its own behaviour from the measured outcome without waiting for the next planning cycle.
How does an agent avoid over-contacting customers?: By treating contact as a cost. If the expected incremental value of messaging a customer today is below the cost of the contact and its effect on future receptivity, the winning action is no contact. That is stronger than a frequency cap, which only limits volume.

Compare

How this plays out against the categories you already buy.

Neutral, side by side reads on where the decision layer sits next to the tools in your stack.

All comparisons

Keep reading

Customer action arbitration

How competing actions are resolved per customer.

Markin and Braze

Decision layer above an execution platform.

Integrations

Channels and data sources Markin connects to.

Lifecycle solutions

Onboarding, cross-sell, churn and win-back.

Vocabulary

The terms this guide relies on.

Each one is defined on its own page, precisely enough to quote.

Full glossary

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

Personalised delivery is solved. Personalised decisions are not.

The two layers, and who does what

Define the action catalogue

Arbitrate per customer

Execute in the channels you already own

Hold out and read