SOLUTION/Next-Best Action
Next-best action, one layer higher.
Markin chooses the treatment per customer and decides which revenue opportunity across the entire base deserves action first.
What next-best action looks like with Markin.
Action value, not engagement
Each candidate action is scored on expected incremental value net of cost, not on predicted opens, clicks or response.
Propensity, uplift, eligibility
Propensity says who is likely. Uplift says who is persuadable. Eligibility and suitability say who may be contacted, and with what.
Contact policy as a constraint
Frequency, fatigue and channel caps are constraints on the optimization, not an afterthought applied by a separate team.
Opportunity intelligence
The segments your team should be acting on today.
Markin surfaces live opportunities, scored, sized and priced in ARR, so growth work starts on the accounts that actually move the number.
Multiple eligible offers
Two programs target the same customers in the same week with conflicting incentives.
Reach
24.8K users
At stake
€890K ARR
Sure things
Propensity is high but uplift is near zero: the action buys conversion that was coming anyway.
Reach
17.3K users
At stake
€520K margin
Wrong-moment contacts
Response concentrates in a two-hour window the current calendar never uses.
Reach
38.1K users
At stake
€740K ARR
Channel mismatch
Push response is 3.4x email for this cohort, which sits on a weekly email drip.
Reach
15.9K users
At stake
€360K ARR
How it works
Data. Intelligence. Action.
The same three-layer loop that powers every Markin motion, tuned for next-best action.
01
Assemble the action inventory
Every treatment your teams can actually deliver, offers, saves, upgrades, service interventions, with its cost, channel and policy constraints.
02
Score value per customer, per action
Propensity, uplift, survival and sequence models are combined into an expected incremental value for each eligible action.
03
Arbitrate, deliver, learn
One layer arbitrates across all active programs so customers are not double-contacted, then measures what the action 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.
1:1 execution
One decision per customer. One message. One moment.
Every row is a live decision Markin ships into your CRM, app or contact center, no segment builders, no rule trees.
Nuria B.
Retail · Lapsing
“The category you buy most just restocked”
Push
Sat 10:05 local
No discount
Pau S.
Telco · Data-capped
“You went over your data 3 months running, here's the fit”
In-app
Pre-renewal
Plan change
Elena V.
Fintech · Payroll active
“Your idle balance could earn 3.6% in Savings”
In-app
Payday +2
3.6% APY
Jonas W.
Streaming · Heavy viewer
“Hold: contact policy exhausted this cycle”
,
,
None
Measured impact
The numbers next-best action teams see with Markin.
+21%
incremental value per contacted customer
-38%
duplicated touches across competing programs
100%
of actions run against a control group
The scores were never the hard part. Deciding which action to take, for whom, without three teams overwriting each other, that was.
Director of CRM
Enterprise B2C operator
Inside the product
Next-Best Action, running 1:1.
Three concrete decisions Markin ships for next-best action, on the surfaces you already run.
Match ranking · per user
Arbitration
One decision across all programs
Competing campaigns resolved into a single action per customer per moment.
Next best offer · scored per user
Uplift
Spend where you change the outcome
Uplift separates persuadable customers from those who convert regardless.
Plan match · per user
Basic
Recommended for this user
€9
Premium
€14
Annual
€119
Policy
Respect eligibility and suitability
Regulatory and brand constraints applied as hard limits inside the optimization.
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.
Compared to
How this differs from what you already run.
- Customer decisioning vs. a customer engagement platformEngagement platforms execute journeys. Customer decisioning chooses which opportunity deserves a message at all. How the two layers divide the work.
- Next-best action vs. next-best opportunityNext-best opportunity sizes what is at stake for a customer. Next-best action chooses the treatment. Why the order matters and how the two connect.
- Markin + Adobe: prioritising the opportunity before personalisationAdobe Real-Time CDP and Journey Optimizer unify profiles and personalise in the moment. Markin decides which opportunity deserves that moment.
- Markin vs Amplitude: analytics that explain vs a team that actsAmplitude explains what happened in the product. Markin forms hypotheses on why revenue per customer is stuck, acts, and proves the result.
- Markin vs Optimizely: running tests vs deciding what to testOptimizely runs the experiments you design. Markin decides which experiments are worth running, sizes them in revenue, and reads every one against a holdout.
- Markin vs building it in-house: what a team can realistically shipBuilding a decision layer in-house is possible and sometimes right. An honest comparison of throughput, cost, ownership and time to a verified number.
FAQ
What is next-best action?
Next-best action is the practice of selecting, for each individual customer, the single most valuable action to take next across all available offers, messages and channels, rather than executing campaigns segment by segment.
What is the difference between propensity and uplift?
Propensity estimates how likely a customer is to take an action. Uplift estimates how much the action itself changes that outcome. Optimising on propensity alone spends margin on customers who would have converted anyway.
What do eligibility, suitability and contact policy mean here?
Eligibility is whether a customer may receive an action at all. Suitability is whether it is appropriate for them. Contact policy caps how often and through which channels they may be reached. All three are constraints on the decision, not filters applied afterwards.
How is this different from a rules engine?
You do not author segments or decision trees. Actions are scored per customer on expected incremental value, and the ranking updates as behaviour and results change.
How is impact measured?
Against control. Impact is reported as incremental revenue and margin, not opens, clicks or engagement proxies.
Can the system decide to do nothing?
Yes. When no eligible action has positive expected value, holding is the decision, and it is recorded as one.
Research
Further reading
The research behind how Markin picks, launches and measures each decision.
- Read the guide
Guides
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.
- Read the guide
Guides
Einstein Next Best Action: how it works, limits and the decision layer
How Strategy Builder ranks recommendations in Salesforce, where the pattern stops, and how to decide in the warehouse and deliver inside the CRM.
- Read the guide
Guides
What is ARPU? Definition, formula and how to calculate it
ARPU calculation explained: the formula, a step-by-step method, variants by business model and benchmark levels from 147 B2C operators.
Vocabulary
The terms behind this page.
The vocabulary this solution runs on, defined term by term.
- Next best actionNext best action is the single intervention that maximises expected value for a specific customer at a specific moment, chosen…
- Feature storeA feature store computes, versions and serves the model inputs used in training and in production, guaranteeing that both see…
- SignalA signal is an observed change in customer behaviour, product state, payment health or market context that carries information…
- Revenue opportunityA revenue opportunity is a named, sized and addressable gap between what a customer segment is worth today and what it could be…
- HypothesisA hypothesis is a falsifiable statement linking an action to a revenue outcome for a defined population: if we do X for segment…
- Candidate actionA candidate action is one specific intervention eligible for one specific customer at one moment: an offer, a message, a price…
See next-best action run on your data.
A 30-minute demo, the same agents, the same decisions, the same 1:1 execution.