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Where the decision layer sits in your stack.

Data platforms organise context. Engagement platforms execute. What usually has no owner is the commercial decision in between: which opportunity deserves to be acted on, for whom, and what it is worth. These pages describe each category on its own terms and say plainly where Markin fits.

One distinction runs through all of them. A decisioning engine optimises a list of actions a human wrote, inside one channel. Markin writes the list, hypotheses across marketing, product, pricing and technical health, launches them in the systems you already run, and reads every one against a holdout. It behaves like a data science and growth team, not like an optimiser.

Recently published

The newest comparisons.

Fresh reads on the categories and tools buyers evaluate against a decision layer.

All comparisons

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.

Category comparisons

What each category in the customer stack actually owns, and which question stays unanswered when you only have one of them.

Markin and your stack

How a decision layer works alongside the data, engagement and activation tools you already run.

Direct comparisons

Where propositions genuinely overlap: what each is built for, and when combining them makes more sense.