---
title: Customer 360 vs. a decision system
url: https://markin.ai/compare/customer-360-vs-decision-system
kind: category
description: A customer 360 shows everything you know about a customer. A decision system turns that into a ranked commercial action. Where visibility stops paying off.
updated: 2026-09-03
---

# Customer 360 vs. a decision system

> A customer 360 assembles every known attribute, interaction and transaction into one view. A decision system consumes that view and produces a ranked, sized commercial decision per customer, with an expected value and a control group. Visibility is a prerequisite; on its own it does not change revenue, because nothing in it chooses.

## In short

- Customer 360: What do we know about this customer? Output: A single readable view, dashboards and traits.
- Decision system: What should we do about this customer, and what is it worth? Output: A prioritised decision per customer, including hold.
- A complete view still requires a human to notice a pattern, form a hypothesis and act on it.
- It reads the view you already built, generates and sizes hypotheses across the full base, chooses treatments and validates them, so the investment in visibility starts producing revenue decisions instead of reports.

## What each layer is

**Customer 360**, A consolidated view of a customer across systems: profile, history, interactions, holdings, service events and consent.

Answers: What do we know about this customer?

**Decision system**, A layer that converts customer context into a ranked commercial decision, with an expected value, a treatment and an experiment attached.

Answers: What should we do about this customer, and what is it worth?

## Side by side

|  | Customer 360 | Decision system |
| --- | --- | --- |
| Question it answers | What do we know about this customer? | What should we do about this customer, and what is it worth? |
| Primary input | Source systems, integrations, identity resolution. | The 360 view, outcomes, costs, constraints and experiment history. |
| Primary output | A single readable view, dashboards and traits. | A prioritised decision per customer, including hold. |
| Usual owner | Data platform, CRM operations. | Growth, data science, revenue leadership. |
| How it's measured | Coverage, freshness, adoption of the view. | Incremental revenue and ARPU against control. |

## Where a 360 programme stalls

Most customer 360 initiatives succeed technically and disappoint commercially. The view is complete, adoption is reported, and the number of good decisions per week is unchanged, because deciding was never part of the scope.

- A complete view still requires a human to notice a pattern, form a hypothesis and act on it.
- Analyst capacity, not data availability, becomes the constraint on how many opportunities get worked.
- Dashboards report what happened; they do not estimate what a different action would have produced.
- Nothing in the view ranks two possible actions against each other for the same customer.

## Where Markin fits

Markin turns the 360 into decisions at machine scale. It reads the view you already built, generates and sizes hypotheses across the full base, chooses treatments and validates them, so the investment in visibility starts producing revenue decisions instead of reports.

- **Analysis without capacity limits.** Hypotheses are generated and tested continuously across the whole customer base, not on the subset an analyst has time for.
- **Every decision is explainable.** Each recommendation carries the signal that produced it, its size, its expected value and its experiment record.
- **No new source of truth.** The 360 stays the view. Markin reads it in place and writes decisions back to the systems that act.

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

## When Markin is not the right answer

- The 360 view is not yet trustworthy: fix data quality before layering decisions on top of it.
- Your commercial teams have no way to execute differentiated actions, so better decisions cannot be delivered.
- You need reporting and governance, not prioritisation.

## FAQ

**Is a decision system the same as analytics?**

No. Analytics explains what happened to a human who then decides. A decision system produces the decision itself, with an expected value and a control group, and learns from the outcome.

**Do we need a complete 360 before starting?**

No, but you need reliable context for the decisions you want to make. A decision layer can start on the subset of data that is trustworthy and expand as coverage improves.

**Who owns the decision system?**

Usually growth or revenue, with data science. It is a commercial system with analytical machinery, not a data platform project.

**How is Markin different from the decisioning or AI already inside customer 360?**

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 customer 360 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 customer 360?**

On a large B2C base, 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/customer-360-vs-decision-system