COMPARE/Category comparison

Campaign calendar vs. continuous decisioning

+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.

A campaign calendar schedules planned sends against a fixed timetable, with audiences defined per campaign. Continuous decisioning evaluates every customer on every cycle and acts only where expected incremental value is positive. The calendar allocates attention by date; continuous decisioning allocates it by opportunity, and can decide to do nothing.

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In short

Campaign calendar: What are we sending this month, and to whom? Output: Scheduled campaigns and audience briefs.
Continuous decisioning: For each customer today, is there something worth doing? Output: Per-customer decisions, continuously refreshed, including hold.
Customers reach decisive moments continuously; a monthly plan only meets them by coincidence.
Opportunities are re-sized as behaviour changes, treatments are chosen per customer and per moment, and the calendar can keep its brand and seasonal moments while the always-on layer handles everything driven by customer state.

Last updated: September 2026. 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 $22 ARPU / month

Addressable revenue

$264.0M

per year, reachable base

Verified ARPU uplift

+17% to +35% ARPU

on treated cohorts, against holdout

What that is worth

$44.9M – $92.4M

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 numbers

What each one actually does.

Campaign calendar

A planned schedule of campaigns and sends, with audiences, creative and timing agreed in advance.

Continuous decisioning

An always-on loop that re-evaluates each customer's opportunities and treatments as context changes, and acts only when expected value is positive.

Side by side

The differences that change outcomes.

Dimension	Campaign calendar	Continuous decisioning
Question it answers	What are we sending this month, and to whom?	For each customer today, is there something worth doing?
Primary input	Commercial plan, seasonality, creative capacity.	Live context, opportunity sizing, uplift models, constraints.
Primary output	Scheduled campaigns and audience briefs.	Per-customer decisions, continuously refreshed, including hold.
Usual owner	CRM, lifecycle, brand marketing.	Growth and data science.
How it's measured	Sends, engagement, campaign-attributed revenue.	Incremental revenue and ARPU against control.

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, compared between With campaign calendar alone and With Markin
Job to be done	With campaign calendar 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 the calendar structurally cannot do

A calendar is a capacity plan disguised as a strategy. The number of decisions per quarter equals the number of campaign slots the team can produce, and the timing is driven by the plan rather than by the customer.

Customers reach decisive moments continuously; a monthly plan only meets them by coincidence.
Every campaign must fill its slot, so low-value sends happen because the date arrived.
Learning is organised per campaign, so results rarely accumulate into a model of what works.
Adding decisions means adding headcount, because each one is authored by hand.

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 runs the loop continuously. Opportunities are re-sized as behaviour changes, treatments are chosen per customer and per moment, and the calendar can keep its brand and seasonal moments while the always-on layer handles everything driven by customer state.

Volume of decisions, not volume of campaigns

Throughput stops being a function of how many briefs the team can write.

Timing follows the customer

Contract dates, usage shifts and lifecycle events trigger evaluation instead of waiting for the next slot.

The calendar keeps what it is good at

Seasonal and brand moments stay planned. Only the customer-state-driven work moves to the always-on loop.

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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.

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Operating model

The constraint is not ideas. It is how many you can test.

	Today, with campaign calendar	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 the calendar is costing you, in margin

Preloaded for a business whose revenue programme is pre-committed a quarter at a time. Every slot on the calendar is an action chosen before the customer's context was known. Set your own figures to see the margin that continuous decisioning can defend against control, and the holdout you would need to prove it.

Your base

Active paying customers2.5M

Accounts that generated revenue in the last 30 days. Not registered users.

Monthly ARPU$22

Recurring plus non-recurring revenue divided by active customers.

Gross margin on incremental revenue63%

Margin on the next unit sold, not blended company margin.

Your programme today

Share of base contactable per month40%

Consented, non-fatigued, reachable on at least one channel.

Monthly revenue churn2.5%

Revenue lost to cancellations each month, as a share of the base.

The bet

Fully loaded annual programme cost$900K

Licences, data, incentives and the people running it.

Assumed lift on addressable revenue3%

Before any incrementality haircut. 2–4% is a defensible planning assumption.

Estimated annual impact

$2.6M

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

$7.9M

What a before/after dashboard would claim, with no control group.

Estimated incremental uplift

$5.5M

What survives a holdout in the central case.

Return on programme cost

3.9×

Payback

4 mo

If 20–40% of it does nothing

Best case · 20% no lift$3.1M

Central case · 30% no lift$2.6M

Worst case · 40% no lift$2.1M

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 4.0% of your addressable base, read over at least 8 weeks, so novelty is not mistaken for effect.

Addressable base

1.0M

Revenue at risk from churn

$172.9M

Annualised, at the current monthly rate.

Open the full calculator, with the method behind it

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, campaign calendar 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 business is genuinely seasonal end to end, with almost no customer-state-driven revenue.
You cannot deliver messages outside a fixed weekly batch process.
Your team wants more control over creative sequencing than an always-on loop can accommodate today.

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.

Explore demo Or book a demo with the team

Questions buyers ask.

Does continuous decisioning mean sending more?

Usually the opposite. When every contact must beat the option of silence on expected value, low-value sends disappear and total contact volume falls while revenue per contact rises.

Do we abandon the campaign calendar?

No. Seasonal, brand and launch moments stay on the calendar. What moves is the always-on lifecycle work that should be triggered by customer state rather than by a date.

How quickly do results appear?

As fast as your outcome cycle allows. Short-cycle behaviours produce readable results in weeks; contract-driven ones take a full renewal window before incrementality is trustworthy.

How is Markin different from the decisioning or AI already inside campaign calendar?

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 campaign calendar 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 campaign calendar?

On the assumptions preloaded above, 2.5M customers at 22 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.