SOLUTION/Product & Customer Diagnosis
Find what is quietly breaking ARPU.
Markin connects behavioural shifts to product, payment and service friction, then routes each finding with the ARPU at stake.
What product & customer diagnosis looks like with Markin.
Behaviour to root cause
When a cohort's behaviour shifts, agents investigate the product, technical, payment and support systems behind it, not only the campaign that touched them.
Marketing vs product vs ops
Markin distinguishes when the correct answer is a message, a human callback, a product change, a technical fix, an operational change or no action at all.
Sized in revenue at stake
Every diagnosed issue is quantified in ARPU and margin impact, then routed to the owning team with the evidence needed to act.
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.
Card declines on renewal, specific BIN range
3.1% of premium renewals silently failing on one issuer. No support ticket generated.
Reach
7.4K users
At stake
€1.4M ARR
New users stuck on step 4, Android 14
Drop-off spike on a specific device × OS combination. Never surfaced in generic funnel view.
Reach
11.9K users
At stake
€680K ARR
Unresolved complaints, high-ARPU cohort
Open tickets >7 days on top-decile customers, currently receiving upsell campaigns.
Reach
3.2K users
At stake
€910K ARR
Feature confusion after redesign
Usage of the primary flow down 22% post-release for one segment. Not a marketing problem.
Reach
18.6K users
At stake
€1.2M ARR
How it works
Data. Intelligence. Action.
The same three-layer loop that powers every Markin motion, tuned for product & customer diagnosis.
01
Correlate behaviour with systems
Agents unify product events, payment attempts, support tickets, operational logs and customer behaviour to spot where value delivery breaks down.
02
Diagnose and classify
Each finding is classified: product friction, technical error, service issue, operational bottleneck, or a genuine growth opportunity. Promotional actions are suppressed on customers with unresolved issues.
03
Route to the right owner
Findings ship to product, engineering, support, operations or commercial with sized impact and evidence. Post-fix impact is measured against baseline.
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.
Finding · Card decline BIN
7.4K users · +€1.4M ARR
“Route to Payments team + retry logic proposal”
Product / Engineering
Now
No customer touch
Finding · Onboarding step 4
11.9K users · +€680K ARR
“Ship to Product with device × OS evidence”
Product
This sprint
Suppress promo
Cohort · Open tickets
3.2K users · +€910K ARR
“Hold all promotional actions, route to Care”
Support
Now
Human callback
Segment · Redesign confusion
18.6K users · +€1.2M ARR
“In-product guide + Product review”
Product + In-app
This week
In-context help
Measured impact
The numbers product & customer diagnosis teams see with Markin.
€6.1M
ARR unlocked by non-marketing fixes / yr
3.8x
faster detection vs. monitoring
100%
findings routed with owner + evidence
Half of what limited our ARPU was never a marketing problem. Markin surfaced it, sized it, and sent it to the right team with the evidence to act.
Chief Product Officer
European incumbent telco
Inside the product
Product & Customer Diagnosis, running 1:1.
Three concrete decisions Markin ships for product & customer diagnosis, on the surfaces you already run.
Churn risk · 30d
0.80
Payments
Silent revenue leaks
Detect declines, retry failures and dunning gaps invisible in the standard funnel.
User journey · scored live
Product
Where users stop reaching value
Identify the exact step, device and cohort where activation breaks.
Match ranking · per user
Support
Suppress promos on open tickets
Never upsell a customer with an unresolved complaint. Route to Care instead.
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 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.
- 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.
- Churn prediction vs. retention decisioningA churn model tells you who is at risk. Retention decisioning chooses who to save, with what, and at what cost. Why prediction alone rarely moves retention.
- 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.
FAQ
How is product & customer diagnosis 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
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
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
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.
- Monetization rateMonetization rate is the share of active customers who pay anything in a period.
- ARPUARPU, average revenue per user, is total revenue in a period divided by the average number of active users in that period.
- ARPAARPA, average revenue per account, is revenue divided by the number of accounts rather than individual users.
- ARPPUARPPU, average revenue per paying user, divides revenue only by users who paid in the period.
- Lifetime valueLifetime value is the discounted margin a business expects from a customer over the whole relationship.
- LTV:CAC ratioThe LTV:CAC ratio divides expected customer lifetime value by fully loaded customer acquisition cost.
See product & customer diagnosis run on your data.
A 30-minute demo, the same agents, the same decisions, the same 1:1 execution.