SOLUTION/Retention Decisioning
Churn prevention that proves retained revenue.
Churn prevention software for enterprise B2C: Markin matches customer value, churn reason and intervention cost to prove which response actually retains incremental revenue.
What retention decisioning looks like with Markin.
Prediction is an input
Risk scores enter the decision alongside customer value, churn reason and what it would cost to intervene.
Save what is worth saving
Retained revenue net of intervention cost, not raw save rate. Some at-risk customers are not economically retainable.
Reason-matched interventions
Price, product friction, service failure and competitive loss demand different responses. A single save offer answers none of them well.
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.
Service-failure churn
Discount offered where the driver was an unresolved service issue, so the save does not hold.
Reach
11.4K accounts
At stake
€1.1M ARR
Low-value at-risk base
Intervention cost exceeds the revenue it would retain across the whole band.
Reach
42.6K accounts
At stake
€680K margin
Late-cycle contacts
Intervention lands after the decision to leave, response drops 4x versus T-30d.
Reach
19.2K accounts
At stake
€950K ARR
Disengaged but active
Usage decay precedes cancellation by ~90 days and is not in the current trigger set.
Reach
26.7K accounts
At stake
€1.3M ARR
How it works
Data. Intelligence. Action.
The same three-layer loop that powers every Markin motion, tuned for retention decisioning.
01
Separate risk from reason
Survival and sequence models estimate when, while diagnosis attributes why: price, friction, service, competition or natural end of need.
02
Match against the intervention inventory
Each available intervention carries a cost, a channel, a policy constraint and evidence of where it has worked. The matrix is explicit.
03
Test, then scale what retains revenue
Interventions are trialled against control by value band and reason, and only the economically positive ones scale.
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.
Marta L.
Telco · High value, price-driven
“Keep your number, lock a 12-month price”
Care
T-30d
Value-matched
Hugo D.
Streaming · Friction-driven
“Playback issue resolved before any retention offer”
Support
On detection
None
Rita N.
Fintech · Low value
“Hold: save cost exceeds the revenue retained”
,
,
None
Andreu C.
Retail · Lapsing loyalty
“Your size is back in the range you actually buy”
Post-decay
No discount
Measured impact
The numbers retention decisioning teams see with Markin.
+13%
incremental retained revenue net of save cost
-29%
save budget spent on non-retainable accounts
100%
of interventions run against control
We were very good at predicting churn and very bad at deciding what to do with the prediction. Those are different problems.
Head of Retention
Subscription operator, EU
Inside the product
Retention Decisioning, running 1:1.
Three concrete decisions Markin ships for retention decisioning, on the surfaces you already run.
Churn risk · 30d
0.80
Diagnosis
Attribute the churn reason
Price, friction, service and competition separated before an intervention is chosen.
Plan match · per user
Basic
Recommended for this user
€9
Premium
€14
Annual
€119
Economics
Size the save
Retained revenue net of intervention cost, per value band.
Match ranking · per user
Inventory
Map the interventions
Every available save action with its cost, channel and evidence base.
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.
- 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.
- Retention analytics vs. retention decisioningRetention analytics explains cohorts and churn drivers. Retention decisioning chooses interventions and proves retained margin. Where the handover sits.
- 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.
- 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 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.
- 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.
FAQ
What is churn prevention software?
Churn prevention software identifies customers at risk of leaving and decides what to do about it. The useful version does more than score risk: it attributes the churn reason, prices each available intervention against the revenue it would retain, and measures every save against a control group so retained revenue is provable rather than assumed.
How is Markin different from churn prediction tools?
Churn prediction tools output a risk score and leave the decision to a human or to a fixed rule. Markin treats the score as one input, adds customer value, churn reason and intervention cost, chooses the specific response, runs it inside your existing CRM or care flows, and reads incremental retained revenue against a holdout.
Is this a churn prediction model?
Prediction is one input. Retention decisioning is the layer that turns a risk score into a specific, costed intervention choice, or into a deliberate decision not to intervene.
Why is save rate the wrong metric?
Save rate can be raised by discounting customers who were never going to leave. The economically meaningful measure is revenue retained net of intervention cost, measured against a control group.
How do you decide the churn reason?
Behavioural, billing, service and product signals are attributed to reason classes. Interventions are then matched to the reason rather than to the score alone.
Do you replace our retention campaigns?
No. Existing campaigns and care flows remain the execution surface. Markin decides which customers enter them, with which intervention, and measures the result.
What if no intervention is worth making?
That is a valid outcome. When expected retained revenue does not cover the cost of intervening, holding is the recorded decision.
How is impact measured?
Against control, as incremental retained revenue over the measurement window rather than immediate cancellation avoidance.
Research
Further reading
The research behind how Markin picks, launches and measures each decision.
- Read the guide
Playbooks
Machine learning churn prediction: labels, leakage and the wrong test
Most production churn models fail on label definition, feature leakage, or an evaluation that rewards AUC instead of decisions. The three fixes, in order.
- Read the guide
Playbooks
Customer winback strategies that pay for themselves
Most win-back programmes mail the whole churned base and count natural returners as wins. How to segment, price the offer and prove causation.
- Read the guide
Research
Telecom churn rate benchmarks, 2026
Median monthly churn is 1.1 percent postpaid, 4.4 percent prepaid and 1.6 percent on converged bundles. What separates the top decile from the rest.
- Read the guide
Playbooks
Churn deflection: the cancellation flow as a decision
Between 30 and 45 percent of accepted save offers go to customers who were staying anyway. How to spend margin only where it changes the outcome.
- Read the guide
Playbooks
Customer churn prediction: models, features and incrementality
A practical guide to customer churn prediction: which models actually work, which features move the needle, and how to convert prediction into incremental save rate.
- Read the guide
Guides
Voluntary vs involuntary churn: two problems, two fixes
Payment-failure churn is a dunning problem. Intent churn is a decisioning problem. How to split them, who owns each, and how to measure both correctly.
Vocabulary
The terms behind this page.
The vocabulary this solution runs on, defined term by term.
- Net revenue retentionNet revenue retention measures revenue from an existing cohort at the end of a period against its revenue at the start…
- Gross revenue retentionGross revenue retention measures how much starting cohort revenue survives a period counting only losses: churn and downgrades…
- Churn preventionChurn prevention is the practice of identifying customers likely to leave and choosing the intervention, if any, that retains…
- Voluntary vs involuntary churnVoluntary churn is a customer deciding to leave.
- Retention curveA retention curve plots the share of a cohort still active against time since acquisition.
- Win-backWin-back is the practice of returning a lapsed or cancelled customer to paying status.
See retention decisioning run on your data.
A 30-minute demo, the same agents, the same decisions, the same 1:1 execution.