SOLUTION/Customer Decisioning
The decisioning layer for compounding ARPU.
Markin turns signals into ranked revenue opportunities, then launches and measures candidate actions through the systems you already run.
What customer decisioning looks like with Markin.
A layer, not a replacement
Data foundation (warehouse or CDP) → Markin decides and launches → your CRM, lifecycle, product and channel systems. Existing investment stays in place.
Opportunity before treatment
Most decisioning starts once the campaign is chosen. Markin decides which opportunity deserves a campaign at all, and what it is worth.
Governed and auditable
Eligibility, suitability, contact policy and control-group design are explicit inputs, and every decision carries its provenance.
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.
High-value contracts near renewal
Risk concentrated in the top two value bands, where an intervention is economically defensible.
Reach
28.9K accounts
At stake
€2.1M ARR
Single-product households
Adjacent product fit evidenced by behaviour rather than by segment membership.
Reach
46.3K accounts
At stake
€1.6M ARR
Discount-insensitive buyers
Uplift is flat between 10% and 25% off: the extra incentive depth buys no additional conversion.
Reach
12.7K accounts
At stake
€610K margin
Over-contacted base
Next touch carries negative expected value once all active programs are counted together.
Reach
31.5K accounts
At stake
€430K margin
How it works
Data. Intelligence. Action.
The same three-layer loop that powers every Markin motion, tuned for customer decisioning.
01
Read the signals already in your stack
Warehouse tables, product events, CRM history, billing and care records are read in place. No migration, no copy of the customer base.
02
Generate and size candidate opportunities
Agents form hypotheses about retention, expansion, loyalty and offer policy, then size each one in customers reachable and revenue at stake.
03
Prioritize, test, route, measure
The portfolio is ranked by expected incremental value against cost and contact budget. Winners are routed to the executing system; every one carries a control group.
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.
Laura M.
Retail · Loyalty tier 2
“Your tier renews in 3 weeks, here's what keeps it”
Push
Sun 11:20 local
Non-monetary
Iván T.
Telco · Contract T-30d
“Keep your line, move to the plan your usage fits”
Call
Weekday 7pm
Value-matched
Sofia R.
Fintech · Single product
“Your balance pattern fits our Savings product”
In-app
Post-deposit
No discount
Daniel K.
Streaming · Payment risk
“Hold: no intervention has positive expected value”
,
,
None
Measured impact
The numbers customer decisioning teams see with Markin.
+16%
incremental ARPU on prioritized opportunities
-41%
programs built that never had a positive case
100%
of decisions measured against control
We had no shortage of things we could do. What we lacked was a defensible way to decide which of them was worth doing first.
VP Growth
European subscription business
Inside the product
Customer Decisioning, running 1:1.
Three concrete decisions Markin ships for customer decisioning, on the surfaces you already run.
Next best offer · scored per user
Prioritization
Rank the opportunity portfolio
Every candidate opportunity scored on reachable base, expected incremental value and cost to serve.
Match ranking · per user
Governance
Make policy explicit
Eligibility, suitability and contact policy expressed as inputs to the decision, not buried in campaign rules.
User journey · scored live
Routing
Execute where you already execute
Decisions land in the CRM, lifecycle tool or product surface that owns the touch.
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 CDPA CDP unifies customer data. Customer decisioning chooses which revenue opportunity is worth acting on. What each layer owns, and where they meet.
- Customer 360 vs. a decision systemA customer 360 shows everything you know about a customer. A decision system turns that into a ranked commercial action. Where visibility stops paying off.
- Data warehouse vs. CDP vs. decision layerThree layers, three jobs: storage and modelling, identity and activation, and commercial decisions. What each one owns and where the boundaries sit.
- Personalization vs. revenue decisioningPersonalization tailors the experience. Revenue decisioning chooses which commercial outcome to pursue and proves it in euros. How the two differ in practice.
- Retention analytics vs. retention decisioningRetention analytics explains cohorts and churn drivers. Retention decisioning chooses interventions and proves retained margin. Where the handover sits.
- Recommendation engine vs. next-best actionA recommendation engine surfaces the right content. Next-best action chooses the right commercial treatment. Why relevance is not revenue.
FAQ
Is Markin a CDP, a CRM or a customer engagement platform?
None of them. A CDP unifies customer data, a CRM stores the relationship and an engagement platform delivers the message. Markin is the growth-science layer between them: it decides which revenue opportunity to pursue, for whom and what it is worth, and then launches the action inside those systems, against a holdout.
Do we have to replace Braze, Salesforce, Adobe or Pega to use it?
No. Markin complements an existing stack. It reads from the data foundation you already have and drives the systems that already own execution, launching each treatment there rather than handing over a recommendation.
What is the difference between decisioning and next-best action?
Next-best action usually selects a treatment for an individual once the program exists. Decisioning as Markin defines it starts one step earlier: selecting and prioritizing the revenue opportunity that justifies a program in the first place.
How do you avoid a black-box decision?
Every decision carries its provenance: the signals used, the hypothesis, the model class, the policy constraints applied and the control-group design. Decisions are auditable after the fact.
Do we need to move our data?
No. Markin reads from your warehouse, product events, CRM and operational systems in place.
What about data security and compliance?
SOC 2, ISO 27001, ISO 42001 and GDPR ready. Least-privilege access, per-tenant encryption, audit logs on every decision and finding.
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.
- Candidate actionA candidate action is one specific intervention eligible for one specific customer at one moment: an offer, a message, a price…
- Time to churnTime to churn is the expected remaining tenure of a customer, derived from a survival model rather than a binary risk score.
- Eligibility rulesEligibility rules define which actions a customer may legally, commercially and contractually receive: consent status, market…
- Contact policyContact policy governs how often, through which channels and with what spacing a customer may be contacted.
- Real-time decisioningReal-time decisioning evaluates a customer's current context and returns an action within the latency budget of the moment…
- Decision volumeDecision volume is the number of distinct, evidenced customer-level decisions a business makes in a period.
See customer decisioning run on your data.
A 30-minute demo, the same agents, the same decisions, the same 1:1 execution.