SOLUTION/Growth Optimization
The Next Best Action engine for ARPU growth.
Markin chooses and launches the right treatment for every customer, continuously measured against control as incremental ARPU.
What growth optimization looks like with Markin.
Eligibility, not segments
Models score every customer for every candidate action in real time, replacing fixed segments and rule trees with live 1:1 eligibility.
Timing, channel, offer, pressure
Every lever tuned per person: the moment, the surface, the incentive depth and the total contact pressure across all campaigns.
Coordinated across campaigns
One decision layer arbitrates all active journeys, so no customer is hit by conflicting or duplicated actions.
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.
Users receiving offers they'd take anyway
Uplift model shows zero incremental impact from current cross-sell push, spending margin on natural buyers.
Reach
34.7K users
At stake
€1.2M ARR
Fatigued high-LTV base
Cohort touched by 4+ campaigns per week, next-touch has negative expected ARPU.
Reach
22.1K users
At stake
€780K ARR
Push-first users on email calendar
4x higher response in push, currently on weekly email drip.
Reach
18.4K users
At stake
€520K ARR
Over-discounted winback cohort
Save rate identical at 10% vs 20% off, current program defaults to 20%.
Reach
9.6K users
At stake
€340K ARR
How it works
Data. Intelligence. Action.
The same three-layer loop that powers every Markin motion, tuned for growth optimization.
01
Read the programs you already run
Markin ingests active campaigns, journeys and treatments, then measures who actually responded, who was going to convert anyway and who was over-contacted.
02
Decide per person, per moment
For each customer, agents pick the eligible action with the highest expected incremental ARPU (or decide to hold), using propensity, uplift, survival or sequence models depending on the decision.
03
Experiment, scale, retire
Every decision runs against control. Wins are scaled, losses are stopped, learnings from one motion feed the next.
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.
Ana G.
Retail · Loyalty Gold
“The pieces you saved just came back in your size”
Push
Sat 10:14 local
Held 48h
Tom R.
Fintech · Payroll active
“Your surplus could earn 3.8% in Savings”
In-app
Payday +2
3.8% APY
Marc P.
Telco · Contract T-18d
“Keep your number, lock a better plan for 12 months”
Call
Weekday 6pm
Loyalty plan -10%
Priya S.
Travel · MAD→LHR
“Pick your seat, upgrade to Comfort for €12”
T-72h
Comfort seat
Measured impact
The numbers growth optimization teams see with Markin.
+18%
incremental ARPU on optimized motions
-32%
contact pressure at higher revenue
100%
of decisions run against control
Every campaign we already run is now measured, optimized and coordinated by Markin. Same channels, same team, materially higher ARPU.
VP CRM
Top-5 European retailer
Inside the product
Growth Optimization, running 1:1.
Three concrete decisions Markin ships for growth optimization, on the surfaces you already run.
User journey · scored live
Onboarding
Move new users to first value
Personal 7-day paths tuned per signup, replacing the static welcome drip.
Next best offer · scored per user
Cross-sell
Rank offers per account
Uplift ranks every candidate offer per person and picks the one that actually moves this customer.
Churn risk · 30d
0.80
Churn
Save only what's worth saving
Agents trigger the exact intervention (or silence) that maximises retained ARPU, not raw save rate.
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.
- Campaign calendar vs. continuous decisioningA calendar plans what everyone gets and when. Continuous decisioning evaluates every customer every day. What changes operationally, and what it is worth.
- Markin vs Adobe Journey Optimizer: orchestration vs growth scienceJourney Optimizer orchestrates journeys on Adobe Experience Platform. Markin decides which revenue hypothesis deserves a journey, and proves it.
- 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 + Airship: deciding what deserves the notificationAirship delivers mobile-first journeys across push, in-app, SMS and wallet. Markin decides which opportunity is worth the interruption, and proves it against a holdout.
- vs ChatGPT with MCP: from a good answer to a proven numberChatGPT with MCP connectors reads your data and answers well. Markin sizes, tests and proves growth hypotheses at population scale. An honest comparison.
- vs Claude with MCP: strong reasoning, no control groupClaude with MCP is excellent at long-context analysis over your data. Markin sizes, tests and proves growth hypotheses per customer. An honest comparison.
FAQ
How is growth optimization different from a campaign tool or CRM?
Markin is not a channel and not a rule engine, and it is not a decisioning engine that ranks actions you already wrote. It does the work of a growth data science team: it forms its own hypotheses, marketing, product, pricing or a technical anomaly holding growth back, sizes them, launches them inside your existing CRM, product surfaces or contact center, and reads each one against a control group.
What models are behind Markin?
A stack of proprietary deep-learning and causal models (propensity, uplift, survival, sequence, embeddings) orchestrated by reasoning agents. The right method is picked per decision, retrained continuously on your data.
Do we need to move our data?
No. Markin reads from your warehouse, product events, CRM and operational systems in place. Nothing is copied, nothing gets locked in.
How is impact measured?
Every decision runs against a control group. Impact is reported as incremental ARPU, revenue and margin, not opens, clicks or engagement proxies.
Can Markin decide to do nothing?
Yes. When no intervention has positive expected value, Markin holds. Silence is a valid, auditable decision.
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
Playbooks
How to increase ARPU in telecom
Median postpaid operators grow ARPU 1.9 percent a year, the top quintile 6.7 percent. The five levers behind the gap, and how to measure each one.
- 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.
- Read the guide
Research
ARPU benchmarks by industry 2026: median vs top-quintile
ARPU benchmarks by industry for 2026: median and top-quintile monthly ARPU levels and growth rates across nine B2C categories, with the method.
Vocabulary
The terms behind this page.
The vocabulary this solution runs on, defined term by term.
- ExperimentAn experiment is a controlled release of a candidate action against a randomised holdout, sized in advance so the result can…
- Contribution margin per userContribution margin per user is revenue per user minus the variable costs of serving that user: delivery, payment fees, support…
- Uplift modelAn uplift model estimates the change in outcome caused by treating a customer, rather than the outcome itself.
- A/B testAn A/B test randomly assigns customers to two or more variants and compares a pre-declared metric between them.
- Holdout groupA holdout is a randomly selected set of customers deliberately excluded from an action, kept as the counterfactual.
- Sequential testingSequential testing allows results to be monitored continuously and stopped early without inflating false positives, using…
See growth optimization run on your data.
A 30-minute demo, the same agents, the same decisions, the same 1:1 execution.