SOLUTION/Revenue Discovery
Find the ARPU your team has not found.
Markin investigates every customer signal, sizes each revenue opportunity and turns the strongest hypotheses into measurable experiments.
What revenue discovery looks like with Markin.
Search the whole base
Agents scan every customer, transaction and product event for anomalies and unexplained behaviour, not only cohorts an analyst has already defined.
Hypotheses, not dashboards
For every signal, agents generate multiple explanations, investigate them with internal data and external context, and rank them by expected ARPU.
Prove it against control
Candidate interventions are sized on margin, tested against control groups, and only scaled once the causal effect is confirmed.
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.
eSIM buyers with mismatched trip length
Cluster buying 7-day plans while booking flights >14 days out. Hypothesis: unaware of long-stay plan or competitor benchmark. Uplift on tailored counteroffer: +€1.9M ARR.
Reach
12.3K users
At stake
€1.9M ARR
Micro-cohort of premium annuals silent 21d
No lifecycle stage flagged them. Investigation points to a specific device × content combination underperforming.
Reach
6.4K users
At stake
€820K ARR
Family accounts on solo plans
Usage pattern matches multi-line household. External benchmark: competitor offering family bundle at parity price.
Reach
9.1K users
At stake
€1.1M ARR
High-LTV shoppers browsing competitor-heavy categories
External signal + on-site behaviour: probable switch in the next 30 days if unaddressed.
Reach
4.8K users
At stake
€540K ARR
How it works
Data. Intelligence. Action.
The same three-layer loop that powers every Markin motion, tuned for revenue discovery.
01
Detect the signal
Agents surface anomalies and unexplained behaviour across the base: a duration that doesn't fit the customer, a drop in a specific micro-cohort, a pattern no journey covers.
02
Generate and investigate hypotheses
Multiple explanations are proposed and tested against transactional, behavioural, support and operational data. Agents can pull external context: competitor offers, local pricing, market conditions, product alternatives.
03
Size, act, learn
Each opportunity is sized on incremental margin. Candidate interventions (some outside the current CRM) are tested against control. Validated discoveries become reusable growth motions.
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.
Cluster · eSIM long-stay
12.3K users · +€1.9M ARR
“Extend to 30 days for €4 more, matched to your trip”
In-app + Email
T+1 after 7d activation
Trip-matched plan
Cohort · Silent premium
6.4K users · +€820K ARR
“New releases picked for your device, first episode unlocked”
Push
Prime-time
Content unlock
Segment · Family fit
9.1K users · +€1.1M ARR
“Add a line for €5, keep your data pooled”
SMS
Weekend
Family bundle
Cohort · Substitution risk
4.8K users · +€540K ARR
“Reserved for you, price-matched for 72h”
Now
Price match
Measured impact
The numbers revenue discovery teams see with Markin.
€8.4M
ARR opportunities surfaced / quarter
60%+
outside existing campaigns
24/7
investigation loop
Markin found revenue we didn't know we had, patterns no analyst had queried, sized in ARR, tested against control. That is the part we couldn't build ourselves.
Chief Growth Officer
Global consumer subscription business
Inside the product
Revenue Discovery, running 1:1.
Three concrete decisions Markin ships for revenue discovery, on the surfaces you already run.
Churn risk · 30d
0.80
Discovery
Anomaly to hypothesis
Every unexplained pattern becomes a set of ranked, sized hypotheses agents can test.
Match ranking · per user
External context
Read the market
Agents pull competitor offers, local pricing and product alternatives to explain what internal data alone cannot.
Next best offer · scored per user
Unit economics
Size before you act
Every candidate intervention priced against margin and incentive cost before any customer sees it.
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.
- Markin vs Salesforce Einstein and Agentforce: decisions vs workflowsEinstein scores and Agentforce automates work inside Salesforce. Markin decides which revenue hypothesis is worth testing and proves it 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.
- vs an LLM with MCP: asking questions is not running growthConnecting an LLM to your warehouse over MCP answers questions well. Compare cost, skills, hypothesis evaluation and model choice against Markin.
- Experimentation vs. continuous decisioningA/B testing proves which variation wins on one metric. Continuous decisioning acts on every customer every cycle, with a holdout. Why testing is not deciding.
- 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 + Optimove: deciding what deserves a campaign at allOptimove orchestrates and prioritises retention campaigns. Markin decides which revenue opportunity deserves one, sizes it, and proves it against a holdout.
FAQ
How is revenue discovery 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
Dunning best practices: recovering failed payments before they become churn
Failed payments are 20 to 40 percent of all churn and the cheapest to fix. Retry by decline code and refresh cards before they fail.
- 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
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.
- 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.
Vocabulary
The terms behind this page.
The vocabulary this solution runs on, defined term by term.
- 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…
- Revenue per available customerRevenue per available customer spreads revenue across everyone reachable, including dormant and non-paying users, rather than…
- IncrementalityIncrementality is the portion of an outcome that would not have happened without the action.
- Statistical powerStatistical power is the probability that a test detects an effect of a given size when that effect is real.
- Sequential testingSequential testing allows results to be monitored continuously and stopped early without inflating false positives, using…
See revenue discovery run on your data.
A 30-minute demo, the same agents, the same decisions, the same 1:1 execution.