---
title: Markin and your data science team
url: https://markin.ai/compare/markin-and-your-data-science-team
kind: stack
description: Markin multiplies data science: your team owns models, economics and causal design, Markin runs millions of decisions against a holdout, continuously.
updated: 2026-09-03
---

# Markin and your data science team

> Markin runs on top of your data science team, not instead of it. The team owns features, models, economics and causal design. Markin behaves like an extra bench of scientists that never sleeps: it writes its own hypotheses,  marketing, product, pricing or a technical anomaly holding growth back, sizes them, launches them in the systems you already run, and reads every one against a randomised control group, at a volume no analyst team can sustain by hand.

## In short

- Your data science team: What is likely to happen, and what actually caused it? Output: Models, scores, experiment designs, readouts
- Markin, decision + execution: For this customer, right now, what is the highest-value action? Output: One sized, ranked decision per customer, with a control group
- Customer-level scores get collapsed into segments because that is the only unit downstream tooling can act on.
- The split is simple: your team owns the science, Markin owns the arithmetic repeated millions of times.

## What each layer is

**Your data science team**, The people who understand your customers quantitatively: features, propensity and uplift models, causal design, the economics behind every number.

Answers: What is likely to happen, and what actually caused it?

**Markin, decision + execution**, The system that consumes those models at machine cadence: generating and sizing opportunities, choosing one action per customer, and proving it against a holdout.

Answers: For this customer, right now, what is the highest-value action?

## Side by side

|  | Your data science team | Markin, decision + execution |
| --- | --- | --- |
| Question it answers | What is likely to happen, and what actually caused it? | For this customer, right now, what is the highest-value action? |
| Primary input | Warehouse data, event streams, domain knowledge | Your models, constraints, economics and outcome history |
| Primary output | Models, scores, experiment designs, readouts | One sized, ranked decision per customer, with a control group |
| Usual owner | Data science / analytics | Steered by data science, run continuously |
| How it's measured | Model quality, validity of the causal read | Incremental ARPU against a randomised holdout |

## The bottleneck was never modelling. It is what happens to the score.

A strong data science team can model anything you put in front of it. What it cannot do is spend a customer-level score at customer level across millions of people, every week, with a control group on each decision. So excellent models end their life in a monthly segment export, and the causal work is reserved for the flagship programmes. That is a throughput gap, not a competence gap.

- Customer-level scores get collapsed into segments because that is the only unit downstream tooling can act on.
- Sizing every candidate opportunity by hand costs more analyst time than most opportunities are worth, so prioritisation defaults to intuition.
- Holdouts are wired manually, so most of what ships is never attributed to anything.
- Senior analysts spend the week pulling data, building lists and reconciling reports instead of on causal judgement.
- Model retraining is scheduled, not driven by what the last decisions actually proved.

## How data science and the decision layer run together

1. **The team sets the frame.** Features, models worth trusting, margin and contact economics, eligibility rules, and how a causal read must be designed to count. This stays with the people who know your business.
2. **Markin runs the volume.** Inside that frame, Markin generates candidate actions, sizes the revenue behind each, ranks them per customer, chooses one, and holds a randomised control group back, continuously, across the whole base, without a ticket.
3. **Evidence comes back to the team.** Every decision returns a measured result: what beat control, by how much, for whom. Data science reads the evidence, retrains on it, adjusts the frame and audits the system. The loop compounds instead of resetting each quarter.

## What Markin does not replace

To be explicit, because this is the question every analytics leader asks first:

- Markin does not replace data scientists. Feature engineering, domain models and causal judgement stay with the people who own them.
- Markin does not hide its reasoning. Every decision is auditable back to the inputs and the model outputs that produced it.
- Markin does not force you off your models. Bring your own propensity and uplift models, or override any model in the loop.
- Markin does not own your warehouse. It reads from where your data already lives; no migration, no new source of truth.
- Markin is not a reporting tool. It produces decisions and the evidence that they worked; your analytics stack stays where it is.

## Where Markin fits

The split is simple: your team owns the science, Markin owns the arithmetic repeated millions of times. Modelling, economics and causal design stay human. Sizing, ranking, choosing, holding out and measuring stop being a roadmap item and become a system that runs while the team sleeps.

- **Your models finally get used at customer level.** A propensity or uplift model built by your team feeds a per-customer decision that also knows margin, contact cost and eligibility. The score stops ending its life in a segment export.
- **Causal design becomes the default, not the exception.** A randomised holdout on every decision means the model you shipped has a measured incremental effect, not a correlation you have to defend in a readout.
- **Analysts become owners of a decision system.** Instead of servicing campaign requests, data science defines and audits the system that makes millions of decisions, a far larger surface of influence with the same headcount.
- **Retraining driven by outcomes, not by calendar.** Each decision returns a labelled result under a known treatment assignment, which is the cleanest training data your models will ever get.

Next: [Customer Decisioning](https://markin.ai/solutions/customer-decisioning)

## When Markin is not the right answer

- You have no outcome history and no channel to act in, so there is nothing to learn from or decide about yet.
- The organisation is not willing to hold out a control group, in which case nothing here can be verified.
- Your base is small enough that a person can reasonably reason about every customer segment in a meeting.

## FAQ

**Does Markin replace my data science team?**

No, and a team that tried to run Markin without data scientists would get less out of it. Markin removes the manual decisioning work: sizing candidates by hand, exporting segments, wiring holdouts, reconciling readouts. The scarce skill,  knowing what to optimise, what to constrain and what a causal read actually proves, becomes more valuable, not less.

**Who owns the models?**

You do. Markin uses the features, propensity and uplift models your team already maintains, alongside its own opportunity sizing, and every decision is auditable back to the inputs that produced it. Your team can inspect, override or replace any model in the loop.

**Can we bring our own uplift models?**

Yes. Bring scores from the warehouse or serve them live; Markin combines them with margin, contact cost and eligibility to choose one action per customer. Where you have no model, Markin's own sizing fills the gap until you do.

**How is this different from deploying our models to a campaign tool?**

A campaign tool consumes a score to build a list. A decision layer compares every candidate action for a customer on expected value, applies constraints, chooses one or holds, and attaches a control group. The output is a decision with evidence, not an audience.

**How do we know the lift came from the decision layer and not the models?**

You do not have to separate them, and the holdout does not care: treated and held-out cohorts differ only in whether a per-customer decision was made, in the same period, with the same models and the same seasonality. That is why the verified range we quote is post-holdout rather than pre/post.

**How is Markin different from the decisioning or AI already inside your data science team?**

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 your data science team 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 your data science team?**

On the assumptions preloaded above, 4.0M 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.

Source: https://markin.ai/compare/markin-and-your-data-science-team