COMPARE/Markin and your stack
Markin + Adobe: prioritising the opportunity before personalisation
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
Adobe Experience Platform unifies profiles in Real-Time CDP and orchestrates personalised experiences through Journey Optimizer. Markin sits before the moment of personalisation and decides which commercial opportunity deserves it for each customer, and what it is worth. Adobe still owns the profile, the journey and the experience.
What is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
8.0M
customers at $32 ARPU / month
Addressable revenue
$1.69B
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$287.2M – $591.4M
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
Adobe Experience Platform
An enterprise experience stack: Real-Time CDP for unified profiles and audiences, and Journey Optimizer for orchestrating and personalising journeys and in-the-moment experiences across channels.
02
Markin, the decision + execution layer
A layer that ranks commercial opportunities per customer, sizes the revenue at stake, chooses the treatment expected to move it, and holds when nothing has positive expected value.
Side by side
The differences that change outcomes.
| Dimension | Adobe Experience Platform | Markin, the decision + execution layer |
|---|---|---|
| Question it answers | How do we deliver a personalised experience to this profile, in this moment? | Which opportunity is worth this customer's attention, and what is it worth to the business? |
| Primary input | Ingested events and records, unified profiles, audiences, journey and offer configuration. | Unified context, outcome history, margin, contact cost, constraints, experiment results. |
| Primary output | Personalised experiences, journey state, audience activation and reporting. | A ranked, sized decision per customer, returned as the input to the experience. |
| Usual owner | Marketing operations and experience teams. | Growth, data science and revenue leadership. |
| How it's measured | Engagement, journey performance, personalisation lift within the experience. | Incremental revenue and ARPU against a holdout. |
The unsolved part
What a full Adobe estate still leaves open
Personalisation answers how an experience should be tailored once you have decided to deliver it. The prior question, of everything we could put in front of this customer, which one is commercially worth the moment, is usually settled by audience rules and a campaign calendar.
- A better-personalised version of the wrong offer is still the wrong offer.
- Audience qualification is rule-based; concurrent qualification is resolved by suppression and priority ordering, not by expected value.
- Experience-level lift is measured within the channel, rarely as incremental revenue across everything the customer received.
- The rate of new commercial hypotheses is bounded by the operations backlog, not by the data.
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 Adobe decides — and where it stops.
Adobe has the most complete decisioning product of the group: real-time ranking, journey arbitration, frequency capping, experimentation and global control groups. It is also the only vendor here that publishes the hard limits of that engine, and those limits describe an offer-catalogue architecture rather than an opportunity-generation one.
Products referenced: Journey Optimizer AI Decisioning, Offer Decisioning, Real-Time CDP, Adobe AI Assistant
What it optimises
AJO AI decisioning combines a real-time decision engine, AI ranking of options by likelihood of engagement, eligibility constraints, frequency capping and AI model insights reporting conversion or revenue lift.
Vendor pageAdobe, Journey Optimizer AI decisioningAdobe ships experimentation with global control groups, an automatically held-out subset used to measure true impact, plus send-time optimisation, with AI journey path, channel and arbitration optimisation flagged as coming soon.
Vendor pageAdobe, Journey Optimizer AI decisioning
Documented boundaries
Adobe publishes decisioning guardrails: a maximum of 10,000 decision items, item size capped at 1KB with 30 attributes, 500 items per collection and 30 decision items returned per policy.
Vendor docsAdobe Experience League, Decisioning guardrailsFurther documented ceilings: a maximum of 5 AI ranking models, 1,000 placements, 10 decision policies per email, and 1,500 to 5,000 decision requests per code-based experience API call depending on Edge segmentation.
Vendor docsAdobe Experience League, Decisioning guardrailsFull value depends on Adobe Experience Platform and Real-Time CDP being the customer data foundation underneath Journey Optimizer.
Vendor pageAdobe, Journey Optimizer AI decisioning
What the evidence actually says
Adobe was named a Leader in the 2026 Gartner® Magic Quadrant™ for Personalization Engines (published 3 February 2026), a genuine independent report, though the promotional framing is Adobe's selection from it.
Analyst reportGartner® Magic Quadrant™ for Personalization Engines (2026)Adobe's AI model insights surface conversion and revenue lift for your own programmes, but no aggregate customer uplift benchmark is published.
Vendor pageAdobe, Journey Optimizer AI decisioning
Where Markin is different.
Opportunities are generated, not catalogued
Adobe ranks items from a decision catalogue someone has to author and maintain within published limits. Markin generates and sizes the opportunity from customer context, so the space of things worth doing is not capped by a catalogue.
Above the platform, not inside it
Markin runs on the warehouse alongside AEP rather than requiring the full Experience Platform footprint before decisions can be made, and writes decisions back into AJO for delivery.
Ranked on money, not engagement likelihood
AI ranking orders options by likelihood of engagement. Markin orders them by expected incremental revenue net of cost, which is why not contacting can win.
Architecture
How the two run together
Context in
Markin reads unified customer context, Real-Time CDP profiles alongside warehouse, product and billing data, plus the outcome history required to learn from past treatments.
Decision
Opportunities are generated, sized and ranked per customer. A treatment is chosen, a control group assigned and an expected value attached before anything is delivered.
Activation back into Adobe
The decision returns as a profile attribute or event, so Journey Optimizer selects and personalises the experience. Content, channel and experience design stay with Adobe.
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 Adobe 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 experiences. It decides which opportunity deserves one, then hands that decision to Adobe so the journey and personalisation machinery does what it is good at. The enterprise governance you have already implemented does not move.
The decision precedes the experience
Journey entry becomes a ranked opportunity with an expected value rather than an audience rule, so personalisation is applied to the offer worth making.
Governance stays in place
Profiles, consent, data governance and brand controls remain Adobe's. Markin reads context in place and writes decisions back.
Revenue-level proof
Each decision carries a holdout, so the number reported to the business is incremental revenue rather than in-channel lift.
Operating model
The constraint is not ideas. It is how many you can test.
| Today, with Adobe | 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 deliver experiences, emails or on-site personalisation.
- Markin does not replace Real-Time CDP profiles or the identity graph.
- Markin does not manage consent, data governance or brand controls.
- Markin does not replace Journey Optimizer; it supplies the decision that starts the right journey.
- Markin does not sit beside Adobe 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
What a decision layer adds on top of Adobe Journey Optimizer
Preloaded for a large enterprise base already running AJO: high reach, mature margin discipline and a well-funded programme. Adobe's decisioning ranks a maintained catalogue within its published ceilings; the figure below is the incremental margin on opportunities generated outside that catalogue, after the haircut independent research says to apply.
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
$17.1M
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
$42.2M
What a before/after dashboard would claim, with no control group.
Verified uplift
$29.6M
What survives a holdout in the central case.
Return on programme cost
8.1×
Payback
2 mo
If 20–40% of it does nothing
What it takes to prove it
To detect a 2.5% lift on revenue per customer you need roughly 58K customers in the control arm, about 1.3% of your addressable base, read over at least 8 weeks, so novelty is not mistaken for effect.
Addressable base
4.4M
Revenue at risk from churn
$601.6M
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, Adobe 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.
- Your priority is improving creative and experience quality rather than choosing between commercial opportunities.
- You lack the outcome history required to size opportunities or evaluate treatments.
- You want to consolidate vendors. This layer adds a decision capability; it does not remove an experience platform.
Questions buyers ask.
Adobe Journey Optimizer already has AI decisioning. Why add a layer?
Adobe's decisioning is the most complete in the category: real-time ranking, eligibility rules, frequency capping, experimentation with global control groups, and a 2026 Gartner Magic Quadrant Leader placement for Personalization Engines. It is also catalogue-based, and Adobe publishes the ceilings, 10,000 decision items, 30 items returned per policy, 5 AI ranking models, 1,000 placements. Markin generates and sizes opportunities from customer context instead of ranking a maintained catalogue, then writes the chosen decision into AJO.
Do we have to replace Adobe?
No. Adobe remains the profile and experience layer. Markin decides which opportunity deserves the moment and returns that decision so Journey Optimizer can execute and personalise it.
Real-Time CDP already has audiences and offers. Why add a decision layer?
Audiences describe who qualifies and offer management describes what can be shown. Neither estimates the incremental revenue of acting, ranks competing opportunities by expected value, or enforces a holdout by default.
How does the decision reach Journey Optimizer?
As a profile attribute or event that journeys can key off, so the experience team works with the interfaces they already use.
Does this create a governance problem?
It should not. Consent and eligibility rules are treated as hard constraints on what Markin may decide, and every decision is auditable with the reason and expected value attached.
How is Markin different from the decisioning or AI already inside Adobe?
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 Adobe 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 Adobe?
On the assumptions preloaded above, 8.0M customers at 32 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.