COMPARE/Markin + growth
Markin and your growth team
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
Markin runs on top of your growth team, not instead of it. Growth owns the offer catalogue, the brand rules, the contact policy and the commercial targets. Markin is the execution capacity underneath: it finds the opportunity, decides which customer gets which treatment and when, launches it in your own channels and product surfaces, and holds a randomised control group, so the team stops building lists and starts steering a system.
What is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
4.0M
customers at $19 ARPU / month
Addressable revenue
$501.6M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$85.3M – $175.6M
incremental revenue per year
Same headcount, same offers, same brand rules. The only variable changed is how many decisions get made and how many of them are verified.
Run it on your own numbersWhat your stack does today.
01
Your growth team
The people who own the commercial outcome: which offers exist, which segments matter, what the brand will and will not say, and what ships this quarter.
02
Markin, decision + execution
The system that runs the calendar out of the equation: sizing every opportunity, choosing one action per customer, and proving it against a holdout.
Side by side
The differences that change outcomes.
| Dimension | Your growth team | Markin, decision + execution |
|---|---|---|
| Question it answers | What should we launch next, and to whom? | For this customer, right now, what is the highest-value action? |
| Primary input | Scores, segments, commercial strategy, offer catalogue | Your offers, constraints, economics and outcome history |
| Primary output | Campaigns, journeys, offers, a roadmap | One sized, ranked decision per customer, with a control group |
| Usual owner | Growth / lifecycle / CRM | Steered by growth, run continuously |
| How it's measured | Campaign performance, quarterly targets | Incremental ARPU against a randomised holdout |
The unsolved part
The bottleneck was never ideas. It is the calendar.
A good growth team has more ideas than quarters. Each one costs a brief, a build, a list, an approval and a slot, so a base of millions gets served by a handful of campaigns a month, planned weeks ahead, aimed at segments. Everything that follows is a capacity gap, not a competence gap.
- The backlog grows faster than the roadmap can clear it, so most ideas are never tested, not rejected, just never reached.
- Prioritisation happens in a planning meeting, from intuition, because sizing every candidate offer by hand is not realistic.
- Campaigns are aimed at segments, so the same offer lands on customers with very different expected value.
- Contact pressure is managed by calendar and caps rather than by whether an action is worth sending at all.
- Most of what ships is read pre/post, so nobody can say what the programme actually added.
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.
Architecture
How growth and the decision layer run together
Growth sets the frame
Which offers exist, margin floors and contact economics, eligibility, brand and tone rules, frequency caps, and the commercial objective for the quarter.
Markin runs the volume
Inside that frame, Markin sizes the revenue behind each candidate action, ranks them per customer, chooses one or holds, and keeps a randomised control group back, continuously, across the whole base.
Evidence comes back to the team
Every decision returns a measured result: which offers beat control, by how much, for whom. Growth retires what does not work, invests in what does, and spends its time on the offers rather than the scheduling.
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 your growth team 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.
The split is simple: growth owns what may be said and what it is worth, Markin owns who gets it and when. Judgement stays human. The arithmetic of matching millions of customers to a catalogue of offers becomes a system that runs while the team sleeps.
The calendar stops being the unit of work
Instead of planning six campaigns a month, the team maintains a live catalogue of offers and constraints, and every customer is evaluated against all of them continuously.
Every offer is sized before it ships
Candidate actions carry an expected revenue figure, so prioritisation is an economic comparison rather than a debate about which idea sounds strongest.
Contact pressure is spent where it pays
When an action has no positive expected value, the system holds. Fewer, better-aimed messages usually beat more of them, and fatigue stops being a fixed cost of running growth.
Every result is defensible
A holdout by default means the number growth takes to the board survived a control group, rather than being a pre/post read with seasonality baked in.
Operating model
The constraint is not ideas. It is how many you can test.
| Today, with the team alone | With Markin on top | |
|---|---|---|
| Campaigns or decisions per month | A handful, whatever the calendar had room for | Millions of per-customer decisions, continuously |
| Unit of targeting | Segment, chosen in a planning meeting | One customer, chosen from expected value |
| Where the growth team's week goes | Briefs, list building, scheduling, approvals | Offers, economics, constraints, what to scale |
| Share of sends with a control group | The flagship programmes, when there is time | Every decision, by default |
| Contact pressure | Managed by frequency caps | Managed by expected value, hold when nothing pays |
| Cost of testing the 500th idea | Another slot, another quarter | Effectively zero |
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, because this is the question every growth leader asks first:
- Markin does not replace growth marketers. Offers, positioning, creative and commercial judgement stay with your team.
- Markin does not set strategy. Which markets, which products, which margin you are willing to give away are human decisions.
- Markin does not invent guardrails. Eligibility, brand, compliance and contact policy are configured by your team and enforced, not inferred.
- Markin does not replace your engagement platform. It decides; Braze, Salesforce or your CRM still deliver.
- Markin does not write your creative. It chooses among the actions you allow it to choose from.
- Markin does not sit beside your growth team 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 what your team could ship without the calendar
Preloaded for a large B2C business with a capable in-house growth function: a full roadmap, a real offer catalogue, and far more ideas than slots to run them in. The figure below is the incremental margin available from running the offers your team already has, across the whole base, continuously, measured against a randomised 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
$5.1M
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
$15.0M
What a before/after dashboard would claim, with no control group.
Verified uplift
$10.5M
What survives a holdout in the central case.
Return on programme cost
5.3×
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 1.8% of your addressable base, read over at least 8 weeks, so novelty is not mistaken for effect.
Addressable base
2.2M
Revenue at risk from churn
$230.6M
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
Growth encodes the frame
Offer catalogue, margin floors, eligibility, brand and contact rules. Markin connects to the engagement platform and data you already run, no migration, no replatforming.
Weeks 3–6
The backlog stops being a backlog
Ideas the team never had time to reach are sized and put in test in parallel, each with a randomised holdout, inside the guardrails growth defined.
Weeks 7–12
First verified incremental revenue
Results are read over a full measurement window. Offers that beat control scale; the rest are retired. The number growth presents is one that survived a holdout.
When you don’t need Markin.
- Your base is small enough that a person can reasonably reason about every customer segment in a meeting.
- You have no offer catalogue and no channel to act in, so there is nothing to decide between yet.
- The organisation is not willing to hold out a control group, in which case nothing here can be verified.
Questions buyers ask.
Does Markin replace my growth team?
No. It removes the operational half of the job, briefs, list building, scheduling, manual prioritisation, and leaves the commercial half, which is where growth adds value. The team moves from running a calendar to owning a decision system: which offers exist, what they are worth, and what the brand will allow.
What does the growth team do differently on day one?
It stops writing a campaign calendar and starts defining the frame: which offers exist, what the margin floor is, what the brand will not say, how often a customer can be contacted. Markin decides who gets what inside that frame.
Do we still need our engagement platform?
Yes. Markin decides; your engagement platform delivers. It sends the chosen action into Braze, Salesforce, your CRM or whichever channel you already run, so nothing about execution changes.
Will this increase how much we message customers?
Usually the opposite. Because every action carries an expected value and the system holds when nothing clears the bar, volume tends to fall while incremental revenue rises. Frequency caps still apply as a hard constraint on top.
How do we know the lift came from Markin and not from the team?
Because it is measured against a randomised holdout inside the same period, with the same team, the same offers and the same seasonality. The treated and held-out cohorts differ only in whether a decision was made per customer.
How is Markin different from the decisioning or AI already inside your growth team?
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 your growth team 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 your growth team?
On the assumptions preloaded above, 4.0M customers at 19 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.