COMPARE/Category comparison
Churn prediction vs. retention decisioning
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
Churn prediction estimates the probability that a customer leaves. Retention decisioning uses that estimate plus expected uplift, margin and cost to decide who to intervene with, which treatment to use and who to leave alone. Risk is an input; the decision is what changes revenue, and only the decision can be measured against control.
What is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
2.0M
customers at $19 ARPU / month
Addressable revenue
$205.2M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$34.9M – $71.8M
incremental revenue per year
Measured on treated cohorts against a randomised holdout, read over a full measurement window rather than the first weeks. Anonymised range across Markin deployments in large B2C bases; your own holdout is the number that decides. The figures above apply that range to the reachable share of the base on this page's assumptions; they are arithmetic, not a forecast for your business.
Run it on your own numbersWhat each one actually does.
01
Churn prediction
A model that scores each customer on the likelihood of cancelling, lapsing or not renewing within a horizon.
02
Retention decisioning
A layer that converts risk into an intervention decision by modelling incremental save probability, margin impact and cost per customer.
Side by side
The differences that change outcomes.
| Dimension | Churn prediction | Retention decisioning |
|---|---|---|
| Question it answers | Who is likely to leave? | Who is worth saving, with what, and who should we leave alone? |
| Primary input | Usage, tenure, service events, payment and engagement history. | Risk, uplift models, offer costs, margin, contact history, constraints. |
| Primary output | A risk score and a risk-ranked list. | An intervention decision per customer, including deliberate no-contact. |
| Usual owner | Data science, analytics. | Growth, retention, finance. |
| How it's measured | AUC, precision and recall of the model. | Incremental retained revenue and margin against control. |
The unsolved part
Why an accurate churn model can lose money
A perfect risk score still says nothing about whether an intervention helps. Treat the highest-risk decile with a discount and a large share of the spend lands on customers who were leaving anyway, or on customers who would have stayed without the discount.
- High risk is not the same as high savability: some at-risk customers are unreachable at any sensible cost.
- Discounting customers who would have stayed converts margin into a giveaway and looks like a saved customer in the report.
- Cost per save is rarely modelled, so retention programmes are judged on retention rate rather than retained margin.
- Risk scoring can even raise churn when the intervention itself reminds a passive customer that cancelling is an option.
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.
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.
Where Markin fits
Not a replacement. A growth-science team on top.
Markin models incremental save probability rather than risk alone, prices each treatment against the margin it protects, and holds where intervention has negative expected value. Retention becomes a budget allocation problem with an auditable answer.
Uplift, not risk ranking
Customers are selected on how much the intervention changes their outcome, which is a different population from the highest-risk decile.
Cost-aware treatment choice
The cheapest treatment that achieves the save is preferred, and discounts are reserved for the cases that need them.
Deliberate non-contact
Sleeping-dog segments are identified and excluded, which is often the single largest margin gain in a retention programme.
Operating model
The constraint is not ideas. It is how many you can test.
| Today, with churn prediction | With Markin on top | |
|---|---|---|
| Revenue hypotheses tested per quarter | 4 to 8, whatever the roadmap had room for | Hundreds, generated and run in parallel |
| What can be hypothesised about | Messages, offers and audiences, the campaign surface | Marketing, product, pricing and technical health alike |
| From decision to live in the channel | A ticket, a build queue, a release window | Markin launches it in your existing platforms itself |
| Time from idea to a result you trust | 6 to 10 weeks of analysis, build and readout | Days, because sizing and design are automated |
| Share of decisions with a control group | The flagship programmes, when there is time | Every decision, by default |
| Coverage of the base | Top segments and the customers a rule caught | One decision per customer, across the whole base |
| Cost of testing the 500th hypothesis | Another analyst, another quarter | Effectively zero |
| What the team spends its time on | Pulling data, building lists, reconciling reports | Judgement: constraints, economics, what to scale |
Markin does not replace your data science team. It removes the ceiling on how much of the base that team can act on, and how fast it finds out whether it worked.
Evidence standard
Most of this category reports its own lift.
None of the major engagement, CDP or personalisation vendors publishes an independently verified uplift figure for its decisioning product. Where numbers exist, they come from vendor-commissioned studies or single-customer case studies with no disclosed holdout methodology. The most rigorous public research in the category is not flattering to anyone, including us, which is exactly why we build against it.
BCG reports that when organisations adopt rigorous incrementality testing, they typically find 20% to 40% of their active next-best-action programmes deliver marginal to negative lift.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)The same research flags novelty effects, new programmes show inflated early results, and recommends 8 to 12 weeks before drawing conclusions.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)Global-holdout, programme-level ROI measurements often overstate impact through halo effects, pull-forward effects and experiment contamination.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)
How Markin holds itself to it
- Every decision Markin makes carries a control group. Uplift is reported against that holdout, not against the customers who did not qualify.
- Results are read over a full measurement window rather than in the first weeks, so novelty is not mistaken for effect.
- Programmes that fail to beat control are retired automatically. Killing decisions that do not pay is part of the loop, not an annual review.
- The one figure we quote about ourselves is a range, not an average: +17% to +35% ARPU on treated cohorts against a randomised holdout, across Markin deployments in large B2C bases. We publish no industry benchmark, because we could not source one we would be willing to defend. Your holdout is the number that matters.
Size it yourself
What the churn model is worth once you act on it
Preloaded for a subscription base with churn scoring already running. A score tells you who is likely to leave; it does not tell you which save is worth making, or whether the customer would have stayed anyway. The revenue-at-risk figure below is the size of the problem; the net margin is what a decision layer can defend against control.
Your base
Accounts that generated revenue in the last 30 days. Not registered users.
Recurring plus non-recurring revenue divided by active customers.
Margin on the next unit sold, not blended company margin.
Your programme today
Consented, non-fatigued, reachable on at least one channel.
Revenue lost to cancellations each month, as a share of the base.
The bet
Licences, data, incentives and the people running it.
Before any incrementality haircut. 2–4% is a defensible planning assumption.
Verified annual impact
$2.0M
Net incremental gross margin in the central case, after the programme cost and after the share of decisioning programmes that independent research finds deliver no real lift.
Reported uplift
$6.2M
What a before/after dashboard would claim, with no control group.
Verified uplift
$4.3M
What survives a holdout in the central case.
Return on programme cost
3.6×
Payback
4 mo
If 20–40% of it does nothing
What it takes to prove it
To detect a 3% lift on revenue per customer you need roughly 40K customers in the control arm, about 4.4% of your addressable base, read over at least 8 weeks, so novelty is not mistaken for effect.
Addressable base
900K
Revenue at risk from churn
$158.6M
Annualised, at the current monthly rate.
Time to value
90 days to a number that survived a holdout.
No replatform, no data migration, no rebuild of the channels you already run. If the first cohorts do not beat control, nothing scales and you have lost a quarter, not a roadmap.
Weeks 0–2
Read the context you already have
Markin connects to the data and the channels you run today, churn prediction included. No migration, no replatform, no new source of truth.
Weeks 3–6
First sized opportunities in test
Opportunities are ranked by expected value, treatments are chosen per customer, and the first cohorts go live with a randomised holdout attached.
Weeks 7–12
First verified incremental revenue
Results are read over a full measurement window. What beats control scales; what does not is retired. Nothing scales on a number that has not survived a holdout.
When you don’t need Markin.
- You have no churn model and no outcome history yet: build prediction and measurement first.
- Retention is contractually fixed for the period, so no intervention can change the outcome.
- Your only available treatment is a single fixed discount with no room to vary cost or channel.
Questions buyers ask.
Do we still need a churn model?
Yes. Risk remains an input. Retention decisioning adds the layer above it: expected uplift, cost and margin, which is where the money is actually won or lost.
What is a sleeping-dog segment?
Customers whose probability of leaving increases when you contact them about staying. Uplift modelling identifies them so they can be excluded, which pure risk ranking cannot do.
How is retention impact measured properly?
Against a holdout of comparable at-risk customers who receive no intervention, reported as incremental retained margin rather than retention rate.
How is Markin different from the decisioning or AI already inside churn prediction?
A decisioning engine ranks actions a human already defined, inside the campaign surface it was given. Markin forms the hypotheses itself, marketing, product, pricing or a technical anomaly holding growth back, sizes them, executes them inside churn prediction and your product surfaces, and reads each one against a randomised holdout. It behaves like a data science and growth team, not like an optimiser.
Does Markin only test messages and offers?
No. Anything a human growth scientist would investigate is in scope: onboarding friction, feature adoption, pricing and packaging, dunning, and technical health issues such as a checkout error rate or a broken deeplink quietly killing conversion. Marketing is one of four hypothesis domains, not the boundary.
What is the business case for adding Markin on top of churn prediction?
On the assumptions preloaded above, 2.0M customers at 19 a month, a small move in ARPU is a large number in absolute terms, because it applies to the whole installed base every month rather than to a campaign. Across Markin deployments the verified range on treated cohorts is +17% to +35% ARPU against a randomised holdout. The point is not more messages: it is finding the highest-value action per customer, launching it, and proving it against control before it scales.
How long before it pays for itself?
First sized opportunities are in test within six weeks and the first holdout-verified result lands inside 90 days. Payback depends on your base, margin and programme cost, the calculator on this page computes it from your own numbers, after applying the 20% to 40% haircut BCG finds when next-best-action programmes are incrementality-tested.