COMPARE/Category comparison
Experimentation vs. continuous decisioning
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
An experimentation platform runs controlled A/B/n tests on predefined variations and reports which wins on a chosen metric with valid statistics. Continuous decisioning decides the right action per customer, every cycle, with a holdout by default, across the whole base, including customers no experiment reaches. Experiments prove what works; decisioning decides who gets it, who is held, and what it is worth.
In short
- Experimentation platform: Which variation performs better on this metric, and how confidently? Output: A statistical read per variation: lift, confidence interval, significance.
- Continuous decisioning: Which action should this customer receive now, and is it worth it? Output: One action or hold per customer, ongoing, with expected value attached.
- An experiment only covers customers who hit the flagged surface; the rest of the base receives no decision at all.
- The intelligence layer sizes the revenue opportunity, the action layer chooses the treatment for every customer, and a control group is attached by default, so learning and acting share one record and the loop improves on proven numbers.
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
3.0M
customers at $20 ARPU / month
Addressable revenue
$360.0M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$61.2M – $126.0M
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 each one actually does.
01
Experimentation platform
A platform that runs controlled A/B/n tests and bandits on predefined variations, on a defined success metric, and reports lift with significance and confidence intervals.
02
Continuous decisioning
A layer that decides the right action per customer, every cycle, across the whole base, selecting treatments, sizing revenue and assigning a control group by default.
Side by side
The differences that change outcomes.
| Dimension | Experimentation platform | Continuous decisioning |
|---|---|---|
| Question it answers | Which variation performs better on this metric, and how confidently? | Which action should this customer receive now, and is it worth it? |
| Primary input | Variations, a success metric, traffic allocation, audience targeting. | Opportunities, treatments, uplift models, margins, costs, past experiment results. |
| Primary output | A statistical read per variation: lift, confidence interval, significance. | One action or hold per customer, ongoing, with expected value attached. |
| Usual owner | Product, engineering, experimentation. | Growth, data science, revenue leadership. |
| How it's measured | Lift on the chosen metric with significance and confidence interval. | Incremental ARPU and revenue against a 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 experimentation platform 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
Why a strong experimentation program is not yet a decisioning system
Experimentation tests a few hypotheses at the cadence a team can ship, on the surfaces that carry a flag. It proves what works; it does not decide who should receive it, act on customers a flag never reaches, or hold when nothing has positive value.
- An experiment only covers customers who hit the flagged surface; the rest of the base receives no decision at all.
- Each experiment reports on one metric; nothing sums the effect of everything a customer saw into one revenue number.
- A holdout is a traffic split within one experiment, not a control group attached to every commercial decision across the estate.
- The hypothesis backlog arrives at the speed the team can write specs, not at the speed the data changes.
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.
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 treats experiment results as one of the inputs to the decision. The intelligence layer sizes the revenue opportunity, the action layer chooses the treatment for every customer, and a control group is attached by default, so learning and acting share one record and the loop improves on proven numbers.
Experiment results feed the decision, not a backlog
What a test proved becomes an input to the next cycle of decisions, so the program compounds rather than producing isolated learnings.
Every customer gets a decision, every cycle
Customers a flag never reaches still receive an action, a save, an attach, a hold, measured against control in the channels they do use.
A holdout on every decision, not every experiment
The control group is attached to each commercial action, so the reported number is incremental revenue per decision, not a program-level lift.
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 experimentation platform | 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.
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 your experimentation program is worth once it decides continuously
Preloaded for a consumer business running an experimentation program at scale: tests run on flagged surfaces and report significance on chosen metrics. The figure below is the incremental margin available from acting on every customer every cycle, not only where a flag is wired in, with a control group by default, rather than a per-experiment significance read.
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.
Estimated annual impact
$3.5M
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
$10.8M
What a before/after dashboard would claim, with no control group.
Estimated incremental uplift
$7.6M
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
$207.9M
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, experimentation platform 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.
- You run a few experiments a quarter and the team can still reason about who should receive the winner.
- Every customer decision already flows through a flagged surface, so there is no unaddressed base to act on.
- Your success metric cannot be tied to revenue, so neither experiments nor decisions can be valued.
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.
Is continuous decisioning just running A/B tests all the time?
No. Running tests continuously still tests a few hypotheses at human cadence on flagged surfaces. Continuous decisioning acts on every customer, every cycle, with a control group by default, including customers no experiment reaches. Experiments prove what works; decisioning decides who gets it and who is held.
Do experimentation and decisioning conflict?
No. They are complementary. Experimentation proves which treatments work; decisioning uses those results as an input, chooses who receives the proven treatment, and holds the rest against a control group. Learning and acting share one record.
What about multi-armed bandits, don't they decide?
Bandits allocate traffic between variations inside one experiment to maximise reward during the test. They optimise the test you already wrote; they do not decide which opportunity is worth testing, act on customers outside the flagged surface, or hold when no action has positive value.
How is Markin different from the decisioning or AI already inside experimentation platform?
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 experimentation platform 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 experimentation platform?
On the assumptions preloaded above, 3.0M customers at 20 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.
CMOs on the shift from creative volume to decision volume
Four CMO conversations on what changes when the constraint moves from producing creative assets to routing the right asset to the right customer.
Continuous decisioning: from campaign calendars to always-on 1:1
Continuous decisioning replaces the weekly promo calendar with a loop that ships hundreds of small, causal experiments per week against a preserved holdout. A 90-day migration.