SOLUTION/ARPU Expansion
Find and prove your next ARPU opportunity.
Markin finds expansion headroom across the base, ranks it against cost and cannibalization, then proves it with controlled experiments.
What arpu expansion looks like with Markin.
Potential, not averages
Expansion headroom estimated per customer and per cohort, so the number points at an action rather than at a dashboard.
Priced without indiscriminate discounting
Incentive depth is a variable to test. Uplift shows where a discount buys nothing and margin can be recovered.
Cannibalization aware
Expansion that shifts revenue between products is separated from expansion that adds it, before the program scales.
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.
Single-product households
Adjacent product fit evidenced by behaviour rather than by lookalike segments.
Reach
52.4K accounts
At stake
€1.9M ARR
Capacity-constrained users
Consistent usage above plan limits for 3+ cycles with no prompted upgrade path.
Reach
21.8K accounts
At stake
€1.4M ARR
Legacy pricing cohorts
Long-tenure accounts on discontinued terms, with retention risk modelled alongside.
Reach
14.3K accounts
At stake
€860K ARR
Bundle switchers
Apparent expansion that displaces existing spend rather than adding to it.
Reach
9.1K accounts
At stake
€400K at risk
How it works
Data. Intelligence. Action.
The same three-layer loop that powers every Markin motion, tuned for arpu expansion.
01
Map the ARPU opportunity tree
The base is decomposed into the drivers that can actually move: product holding, usage tier, price realisation, attach rate and retention.
02
Prioritize by expected incremental value
Each branch is sized on reachable customers, expected uplift, margin cost and the risk of cannibalizing existing revenue.
03
Run experiments, keep what compounds
Top branches become controlled experiments. Results feed back into the tree, so prioritization improves each cycle.
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.
Clara F.
Fintech · Single product
“Your deposits fit our Savings tier, here's the rate”
In-app
Post-activity
No discount
Óscar J.
Telco · Over-cap usage
“You've paid overage 3 months running”
Post-cycle
Plan change
Aina P.
Retail · High frequency
“Two more visits to your next loyalty tier”
Push
Post-purchase
Non-monetary
Lukas H.
Streaming · Bundle candidate
“Hold: cannibalization risk exceeds expected net new revenue”
,
,
None
Measured impact
The numbers arpu expansion teams see with Markin.
+19%
incremental ARPU on tested expansion branches
-24%
incentive depth removed with no loss in conversion
100%
of expansion tests run against control
Reporting ARPU was never the problem. Knowing which part of the base could move it, and by how much, was.
Chief Commercial Officer
European B2C group
Inside the product
ARPU Expansion, running 1:1.
Three concrete decisions Markin ships for arpu expansion, on the surfaces you already run.
Tier progress · Gold
3 more sessions this week to unlock a personal reward.
Personal challenge activeDiscovery
Build the opportunity tree
Decompose ARPU into the drivers that can be moved, and size each one.
Next best offer · scored per user
Cross-sell
Rank offers per account
Uplift ranks candidate offers and picks the one that adds revenue for this customer.
Plan match · per user
Basic
Recommended for this user
€9
Premium
€14
Annual
€119
Upgrade
Match the plan to the usage
Tier moves grounded in the customer's own consumption rather than in a campaign calendar.
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.
- 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.
- Markin + Hightouch: warehouse data, decisions and activationHightouch activates warehouse data into your tools. Markin decides which revenue opportunity is worth activating, on your composable stack.
- 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
What is ARPU expansion?
ARPU expansion is the deliberate discovery and prioritization of opportunities that raise average revenue per user, attach, tier fit, price realisation and retention, rather than the broad pushing of additional offers to the whole base.
How is this different from cross-sell campaigns?
Cross-sell is one execution path. The work here is deciding which expansion opportunity is worth a campaign at all, what it is worth net of margin cost, and whether it adds revenue or merely shifts it.
Can ARPU be a misleading metric?
Yes. An average can rise because low-value customers churned, which is not growth. ARPU is read alongside retention, base size and customer-base health so the number is interpreted correctly.
How do you avoid discounting away the gain?
Incentive depth is treated as a variable to test. Where uplift shows a discount buys no additional conversion, the discount is removed and the margin recovered.
How is cannibalization handled?
Expansion tests measure net new revenue against control, so revenue displaced from an existing product is separated from revenue genuinely added.
How does this relate to Revenue Discovery?
Revenue Discovery finds and sizes opportunities across the whole base. ARPU Expansion is the applied program for the subset of those opportunities whose objective is raising revenue per customer.
Research
Further reading
The research behind how Markin picks, launches and measures each decision.
- Read the guide
Playbooks
ARPU vs LTV vs CAC: which number answers which question
ARPU is observed, LTV is forecast, CAC is spend. Using one where another belongs is how a company posts rising LTV while revenue per customer quietly falls.
- 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
Playbooks
How to increase ARPU: a framework for B2C enterprises
How to increase ARPU in a large B2C business: the five levers that actually move average revenue per user, and the operating model behind them.
- 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.
- Expansion revenueExpansion revenue is additional revenue from customers a business already has: upgrades, add-ons, cross-sell, higher usage or a…
- Growth agentA growth agent is an autonomous worker that runs one stage of the growth-science loop without being prompted: reading signals…
- Opportunity feedAn opportunity feed is a continuously refreshed, ranked list of revenue opportunities detected in a customer base, each with its…
- Hypothesis provenanceHypothesis provenance is the complete, inspectable chain behind a decision: which signals raised it, which analysis sized it…
- Decision volumeDecision volume is the number of distinct, evidenced customer-level decisions a business makes in a period.
- Autonomous growth scienceAutonomous growth science is the practice of running the full scientific loop over a customer base without a human in every…
See arpu expansion run on your data.
A 30-minute demo, the same agents, the same decisions, the same 1:1 execution.