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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.
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.
- Customer decisioning vs. a CDPA CDP unifies customer data. Customer decisioning chooses which revenue opportunity is worth acting on. What each layer owns, and where they meet.
- Customer decisioning vs. a customer engagement platformEngagement platforms execute journeys across channels. Customer decisioning chooses which opportunity deserves a message at all. How the two layers divide the work.
- 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.
- Customer 360 vs. a decision systemA customer 360 shows everything you know about a customer. A decision system turns that into a ranked commercial action. Where visibility stops paying off.
- 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.
- 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.
- 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.
- Retention analytics vs. retention decisioningRetention analytics explains cohorts and churn drivers. Retention decisioning chooses interventions and proves retained margin. Where the handover should happen.
- Recommendation engine vs. next-best actionA recommendation engine surfaces the right content. Next-best action chooses the right commercial treatment. Why relevance is not revenue, and where they connect.
- 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.
Markin and your stack
How a decision layer works alongside the data, engagement and activation tools you already run.
- Markin + Braze: from customer data to prioritised retention actionsBraze orchestrates and delivers cross-channel journeys. Markin decides which revenue opportunity deserves one. How a decision layer works alongside Braze.
- Markin + Salesforce: decisioning beyond journeys and campaignsSalesforce unifies customer records and runs journeys. Markin decides which revenue opportunity is worth acting on. How a decision layer works alongside Salesforce.
- Markin + Hightouch: warehouse data, decisions and activationHightouch activates warehouse data into your tools. Markin decides which revenue opportunity is worth activating. How the decision layer sits on a composable stack.
- Markin + Segment or Tealium: turning customer context into revenue decisionsSegment and Tealium collect, resolve and govern customer data. Markin decides which revenue opportunity is worth acting on. How the layers divide the work.
- Markin + Adobe: prioritising the opportunity before personalisationAdobe Real-Time CDP and Journey Optimizer unify profiles and personalise in the moment. Markin decides which opportunity deserves that moment.
- Markin + Optimizely: from winning experiments to deciding what is worth testingOptimizely proves which variation wins. Markin decides which revenue opportunity is worth testing and acts on every customer continuously, against a holdout.
- Markin + Amplitude: from behavioural insight to revenue decisionsAmplitude observes behaviour and tests features. Markin turns that into sized revenue decisions per customer, across channels, measured against a holdout.
- Markin and your data science teamMarkin multiplies data science: your team owns models, economics and causal design, Markin runs millions of decisions against a holdout, continuously.
- Markin and your growth teamMarkin removes the calendar: your team owns offers, brand and economics, Markin chooses who gets what, launches it in your tools, and reads it against a holdout.