---
title: Markin + Iterable: deciding which journey is worth running
url: https://markin.ai/compare/markin-and-iterable
kind: stack
description: Iterable builds and delivers cross-channel journeys. Markin decides which revenue opportunity deserves one, sizes it, and proves it against a randomised holdout.
updated: 2026-09-03
---

# Markin + Iterable: deciding which journey is worth running

> Iterable is a cross-channel engagement platform: journeys, segmentation and delivery across email, push, SMS and in-app, with Nova adding intelligence inside those journeys. Markin sits before it and decides which commercial opportunity is worth acting on per customer, and what it is worth. Iterable still builds and sends.

## In short

- Iterable: How do we build, personalise and deliver this journey? Output: Delivered messages, journey state and engagement reporting.
- Markin, the decision + execution layer: Which opportunity justifies contact for this customer, and what is it worth? Output: A ranked, sized decision per customer,  including hold, written into Iterable.
- Journey entry criteria encode a decision a person already made; they do not estimate its incremental effect.
- It decides which commercial opportunity is worth pursuing per customer and hands that decision to Iterable to execute in the journeys you already run.

## What each layer is

**Iterable**, A cross-channel engagement platform: journey building, segmentation, catalogues and delivery across email, push, SMS, in-app and web, with AI assistance inside those journeys.

Answers: How do we build, personalise and deliver this journey?

**Markin, the decision + execution layer**, A layer that generates and sizes revenue opportunities per customer, ranks them on expected incremental margin, selects a treatment and decides when to hold.

Answers: Which opportunity justifies contact for this customer, and what is it worth?

## Side by side

|  | Iterable | Markin, the decision + execution layer |
| --- | --- | --- |
| Question it answers | How do we build, personalise and deliver this journey? | Which opportunity justifies contact for this customer, and what is it worth? |
| Primary input | User fields and events, segments, journey logic, catalogues and templates. | Customer context, outcomes, margins, contact history, constraints, past experiment results. |
| Primary output | Delivered messages, journey state and engagement reporting. | A ranked, sized decision per customer,  including hold, written into Iterable. |
| Usual owner | Lifecycle and growth marketing. | Growth, data science and revenue leadership. |
| How it's measured | Deliverability, engagement and conversion attributed to the journey. | Incremental revenue and ARPU against a randomised holdout. |

## Top alternatives to Iterable

Buyers searching for Iterable alternatives are usually weighing two different moves: swapping the lifecycle platform, or adding a decision layer above it. Here is what each shortlisted option is genuinely good at.

| Alternative | What it is | Best for | Watch out |
| --- | --- | --- | --- |
| Braze | Cross-channel engagement platform with Canvas journeys and native decisioning features. | Mobile-heavy consumer products that need breadth of channels and integrations. | Optimisation is bounded by the journeys your team designs. |
| Klaviyo | Retail and ecommerce lifecycle platform with strong storefront data models. | Direct-to-consumer commerce brands where the store is the primary data source. | Less suited to subscription or telco-style ARPU programmes. |
| MoEngage | Insights-led engagement platform with Sherpa AI optimisation. | High-volume mobile bases across retail, fintech and media. | Optimisation is channel and timing led rather than margin led. |
| Optimove | CRM marketing platform built around customer-led orchestration and campaign arbitration. | Gaming, betting and retail teams running many concurrent campaigns. | Arbitrates between campaigns you already defined. |
| Markin | Agentic growth layer that generates, prices and tests revenue hypotheses on top of the platform you already send with. | Enterprise B2C teams whose constraint is learning speed and ARPU rather than delivery. | Not a messaging platform. Iterable stays as the activation route. |

## What stays unsolved when Iterable is running well

Iterable makes it fast to build and deliver journeys. The constraint moves upstream: which journeys deserve to exist, for whom, and what each one is actually worth in margin.

- Journey entry criteria encode a decision a person already made; they do not estimate its incremental effect.
- Engagement labels such as brand affinity rank relevance, not the revenue at stake behind a contact.
- A customer can qualify for several journeys at once; priority is resolved by caps and rules, not expected value.
- The number of hypotheses tested per quarter is bounded by how many briefs the lifecycle team can write.

## How the two run together

1. **Context in.** Markin reads customer context from the warehouse, billing and product systems, plus Iterable send and engagement history.
2. **Decision.** Opportunities are generated, sized and ranked per customer, a treatment is chosen and a control group assigned, with expected value attached.
3. **Activation back into Iterable.** The decision is written as a user field or a custom event, so an existing journey selects it and sends. Content, channels and consent remain in Iterable.

## Iterable's own decisioning layer

Products: Nova Intelligence, Nova Decisioning, Brand Affinity™

Iterable has an intelligence layer, Nova, that brings decisioning into journeys, plus AI labelling such as Brand Affinity. Those outputs are designed to be used inside Iterable's own segments, journeys and campaigns, which is precisely where they are strong, and where their scope ends.

**What it optimises**

- Iterable positions Nova as the intelligence layer of its platform, automating the decision loop so teams can turn strategic intent into action inside the product. [Vendor page: Iterable, AI product page](https://iterable.com/product/ai/)
- Brand Affinity uses Iterable AI to label users by historical engagement, for use in segmentation, campaigns, journeys, data feeds and Catalog collections. [Vendor docs: Iterable Support, Brand Affinity](https://support.iterable.com/hc/en-us/articles/360052990191-Brand-Affinity)

**Documented constraints**

- AI outputs are documented for use inside Iterable's own constructs,  segmentation, campaigns, journeys, data feeds and Catalog collections, so the decision surface is the Iterable estate. [Vendor docs: Iterable Support, Brand Affinity](https://support.iterable.com/hc/en-us/articles/360052990191-Brand-Affinity)
- The framing is decision automation for marketer workflows,  faster, better journey decisions, rather than generating and pricing commercial opportunities across the business. [Vendor page: Iterable, AI product page](https://iterable.com/product/ai/)

**Evidence**

- Iterable's published economic case is a Total Economic Impact™ study conducted by Forrester Consulting and commissioned by Iterable, not independent benchmarking of Nova. [Vendor-commissioned: Forrester TEI of Iterable, commissioned by Iterable](https://iterable.com/wp-content/uploads/2018/02/Iterable-TEI-Report_Final.pdf)

**Where Markin differs**

- **Decides before the journey exists.** Nova improves decisions inside a journey someone designed. Markin proposes and sizes the opportunity that justifies a journey,  or a pricing change, or a product fix, before any of it is built.
- **Ranked on incremental margin.** Engagement labels rank relevance. Markin ranks expected incremental revenue net of margin and contact cost, which is why not contacting can win.
- **Holdout on every decision.** Results are read against a randomised control group per decision, not as conversion attributed to a campaign.

## What Markin does not replace

To be explicit about scope, because procurement will ask:

- Markin does not replace Iterable journeys, templates, catalogues or delivery.
- Markin does not own consent, frequency caps or channel governance.
- Markin is not a customer data platform or a system of record.
- Markin does not take over creative production or content management.

## Where Markin fits

Markin does not build journeys and does not replace the send platform. It decides which commercial opportunity is worth pursuing per customer and hands that decision to Iterable to execute in the journeys you already run.

- **Upstream of the journey, not inside it.** The opportunity is generated and sized before a journey is briefed, so the roadmap stops being the limit on what can be tested.
- **Arbitrated across the estate.** An Iterable journey competes with a service contact, an in-product placement or silence, on one scale: expected incremental margin.
- **Verified against control.** Every decision carries a holdout, so what scales is what beat the control group over a full measurement window.

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

## When Markin is not the right answer

- You have no revenue or outcome data to read decisions against.
- Your programme is purely transactional messaging with no commercial choice to make.
- You are looking to replace your engagement platform rather than decide better inside it.

## FAQ

**Doesn't Iterable's Nova already do decisioning?**

Nova brings decisioning into Iterable journeys and its AI labels users for segmentation and campaigns. That is decisioning inside the send platform. Markin decides which commercial opportunity is worth a journey in the first place, prices it in margin terms, arbitrates it against non-Iterable touchpoints, and measures it against a randomised holdout.

**Do we have to replace Iterable?**

No. Iterable remains the journey and delivery layer. Markin supplies the decision that its journeys execute.

**How does the decision reach Iterable?**

As a user field carrying the chosen action and its expiry, or as a custom event that triggers journey entry.

**How is this different from Brand Affinity?**

Brand Affinity labels a customer by historical engagement. Markin estimates what a specific action is worth for that customer in incremental margin, and chooses between actions, including doing nothing.

**What does the first ninety days look like?**

One revenue theme, one activation route into Iterable, a real holdout, and a defensible incremental number by the end of the quarter.

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

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 Iterable 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 Iterable?**

On the assumptions preloaded above, 2.5M customers at 16 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.

Source: https://markin.ai/compare/markin-and-iterable