COMPARE/Markin and your stack
+Markin + Iterable: deciding which journey is worth running
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
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.
Last updated: . Claims about other vendors link to the source they come from.
What is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
2.5M
customers at $16 ARPU / month
Addressable revenue
$288.0M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$49.0M – $100.8M
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
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.
02
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.
Side by side
The differences that change outcomes.
| Dimension | 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. |
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 Iterable 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. |
The unsolved part
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.
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 Iterable decides, and where it stops.
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.
Products referenced: Nova Intelligence, Nova Decisioning, Brand Affinity™
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 pageIterable, AI product pageBrand Affinity uses Iterable AI to label users by historical engagement, for use in segmentation, campaigns, journeys, data feeds and Catalog collections.
Vendor docsIterable Support, Brand Affinity
Documented boundaries
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 docsIterable Support, Brand AffinityThe framing is decision automation for marketer workflows, faster, better journey decisions, rather than generating and pricing commercial opportunities across the business.
Vendor pageIterable, AI product page
What the evidence actually says
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-commissionedForrester TEI of Iterable, commissioned by Iterable
Where Markin is different.
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.
Architecture
How the two run together
Context in
Markin reads customer context from the warehouse, billing and product systems, plus Iterable send and engagement history.
Decision
Opportunities are generated, sized and ranked per customer, a treatment is chosen and a control group assigned, with expected value attached.
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.
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 Iterable 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 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.
Why Markin wins
More powerful than anything on this page.
Every tool Markin is compared against was built for a job that stops before revenue moves: deliver the message, unify the data, score the lead. Markin was built for one outcome, growing ARPU, and it owns the full loop that gets there: investigate, hypothesise, launch, measure and scale, across marketing, product, pricing and technical health.
Ultra-fast learning, by design
Markin Growth Science runs the full cycle of observe, hypothesise, experiment and read in days, with hundreds of holdout-backed experiments in parallel. The system compounds learning at a pace no human team, and no campaign tool, can match.
Growth operations, made efficient
Sizing, segment design, build, launch and measurement used to be four teams and a sprint. In Markin they are one system, so the same growth operation ships more revenue action with a fraction of the coordination cost.
One outcome: ARPU
Every hypothesis is sized in expected revenue per customer, every action is judged against a randomised holdout, and everything that beats control scales across the base automatically. Nothing else on this page is measured that way.
If the goal is to grow ARPU through ultra-fast learning and run growth operations more efficiently, the choice is Markin.
Operating model
The constraint is not ideas. It is how many you can test.
| Today, with Iterable | 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 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.
- Markin does not sit beside Iterable 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 Iterable programme
Preloaded for a growth-led consumer business running Iterable journeys at scale. Iterable's own intelligence layer decides inside the journey. The figure below is the incremental margin available from deciding which opportunity deserves a journey, and what it is worth, measured against a holdout.
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
$3.3M
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
$8.6M
What a before/after dashboard would claim, with no control group.
Verified uplift
$6.0M
What survives a holdout in the central case.
Return on programme cost
4.5×
Payback
3 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 2.7% of your addressable base, read over at least 8 weeks, so novelty is not mistaken for effect.
Addressable base
1.5M
Revenue at risk from churn
$147.0M
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, Iterable 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.
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. |
When you don’t need Markin.
- 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.
See it in the product
Watch it decide, experiment and execute, before you talk to anyone.
A guided tour of the Markin workspace on a live demo customer, no sales call, no setup.
Questions buyers ask.
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.
Read next
Background reading on how next best action decisioning works, from the guides behind this comparison.
Next best action marketing: what it is and how it works in 2026
Next best action marketing explained: how it works, real B2C examples, how it differs from segment campaigns and marketing automation, and what it is worth on a 2M base.
How to increase ARPU: a framework for B2C enterprises
How to increase ARPU in a large B2C business: the five levers that actually move average revenue per user, and the operating model behind them.
Opportunity Feed, now with hypothesis provenance
Every candidate action in the Markin Opportunity Feed now carries the signal, segment and prior experiment it descends from. One click to audit or ship.