---
title: Markin + Salesforce: decisioning beyond journeys and campaigns
url: https://markin.ai/compare/markin-and-salesforce
kind: stack
description: Salesforce unifies customer records and runs journeys. Markin decides which revenue opportunity is worth acting on, and proves it against a holdout.
updated: 2026-09-03
---

# Markin + Salesforce: decisioning beyond journeys and campaigns

> Salesforce combines a system of record with unified profiles in Data Cloud and journey orchestration in Marketing Cloud. Markin adds the commercial decision on top: which opportunity exists per customer, what it is worth, and which treatment to run. Records, governance and execution stay in Salesforce.

## In short

- Salesforce: What is the record of this customer, and which journey are they in? Output: Segments, journey membership, delivered messages, pipeline and case records.
- Markin, the decision + execution layer: Which opportunity should this customer receive next, and is it worth the contact? Output: A ranked, sized decision per customer, written back to the record or the journey.
- Journey entry criteria encode decisions someone already made; they do not estimate the incremental effect of entering.
- It reads the context Salesforce maintains, decides what is commercially worth doing, and returns that decision so Salesforce can execute it through the journeys, tasks and agent surfaces you already run.

## What each layer is

**Salesforce**, A CRM and marketing estate: accounts, contacts and service history as the system of record, unified profiles and audiences in Data Cloud, and journey orchestration and personalisation in Marketing Cloud.

Answers: What is the record of this customer, and which journey are they in?

**Markin, the decision + execution layer**, A layer that turns that context into ranked commercial decisions: which opportunity is worth acting on for each customer, at what expected value, and proven against control.

Answers: Which opportunity should this customer receive next, and is it worth the contact?

## Side by side

|  | Salesforce | Markin, the decision + execution layer |
| --- | --- | --- |
| Question it answers | What is the record of this customer, and which journey are they in? | Which opportunity should this customer receive next, and is it worth the contact? |
| Primary input | CRM objects, service and sales activity, ingested events, unified profiles. | Customer context from Salesforce and the warehouse, outcomes, margin and constraints. |
| Primary output | Segments, journey membership, delivered messages, pipeline and case records. | A ranked, sized decision per customer, written back to the record or the journey. |
| Usual owner | Sales, service and marketing operations. | Growth, data science and revenue leadership. |
| How it's measured | Journey completion, campaign conversion, pipeline and case metrics. | Incremental revenue and ARPU against a holdout. |

## What a complete Salesforce estate still leaves open

Salesforce is excellent at knowing and recording. The commercial question,  of everything we could offer this customer this month, which one adds the most revenue, is normally answered by segmentation rules and campaign calendars written by people.

- Journey entry criteria encode decisions someone already made; they do not estimate the incremental effect of entering.
- Priority between concurrent journeys is resolved by suppression rules rather than by expected value.
- Uplift is rarely measured against a real holdout, so the reported number blends demand that would have happened anyway.
- The number of hypotheses tested per quarter is bounded by the operations team's capacity, not by the data.

## How the two run together

1. **Context in.** Markin reads unified customer context,  Salesforce records and Data Cloud profiles, plus warehouse, product and billing data, together with the outcome history needed to learn from.
2. **Decision.** Opportunities are generated, sized and ranked per customer. A treatment is selected, a control group is assigned, and the expected value is attached to the decision.
3. **Activation back into Salesforce.** The decision returns as a field on the record or an event that triggers the appropriate journey, task or next-best-action prompt for a service or sales agent.

## Salesforce's own decisioning layer

Products: Einstein Next Best Action, Agentforce, Marketing Cloud Next, Data Cloud

Salesforce has recommendation and agentic layers, but they are spread across products with different jobs: Einstein Next Best Action surfaces suggested actions, Agentforce executes autonomously, Marketing Cloud Next assembles campaigns, and Data Cloud has to be adopted for any of it to see the full customer.

**What it optimises**

- Einstein Next Best Action is described as the Salesforce recommendation engine that surfaces personalised action suggestions to users based on rules, data and AI-predicted scoring. [Vendor docs: Salesforce Trailhead, Get Started with Einstein Next Best Action](https://trailhead.salesforce.com/content/learn/modules/einstein-next-best-action/get-started-with-einstein-next-best-action)
- Marketing Cloud Next, announced June 2025, is billed as a full-funnel agentic marketing solution using autonomous agents for campaign assembly, performance optimisation and 1:1 personalisation at scale. [Vendor page: Salesforce, Marketing Cloud Next announcement](https://www.salesforce.com/news/stories/marketing-cloud-next-announcement/)

**Documented constraints**

- Next Best Action is largely an internal-facing recommendation tool: suggestions are surfaced to sales and service users rather than arbitrated autonomously across outbound channels. [Vendor docs: Salesforce Trailhead, Einstein Next Best Action](https://trailhead.salesforce.com/content/learn/modules/einstein-next-best-action/get-started-with-einstein-next-best-action)
- Data Cloud is presented as the foundational unification layer that fuels Agentforce, so decisioning quality is contingent on the customer estate being ingested into Salesforce's own data platform. [Vendor page: Salesforce, How Data Cloud powers Agentforce](https://www.salesforce.com/news/stories/how-data-cloud-powers-agentforce/)
- Salesforce itself documents an Agentforce-versus-Einstein distinction for marketers, meaning teams have to reconcile two overlapping AI layers inside one platform. [Vendor docs: Salesforce Trailhead, Explore AI in Marketing Cloud Next](https://trailhead.salesforce.com/content/learn/modules/ai-in-marketing-cloud-next/explore-ai-in-marketing-cloud-next)

**Evidence**

- No public uplift percentage for Einstein Next Best Action, Agentforce or Marketing Cloud Next decisioning was found on Salesforce's own product pages; the material is positioning-led rather than benchmarked. [Vendor page: Salesforce, Marketing Cloud Next announcement](https://www.salesforce.com/news/stories/marketing-cloud-next-announcement/)
- Salesforce was named a Leader in the 2026 Forrester Wave™ for Revenue Marketing Platforms for B2B, an independent report, but one covering B2B revenue marketing broadly rather than consumer decisioning. [Analyst report: Forrester Wave™, Revenue Marketing Platforms for B2B (Feb 2026)](https://www.salesforce.com/blog/salesforce-2026-forrester-wave-b2b/)

**Where Markin differs**

- **One decision layer, not three AI surfaces.** Markin produces a single ranked decision per customer that Salesforce clouds consume. There is no seam between a recommendation engine, an agent framework and a campaign assembler to reconcile.
- **Works on the warehouse you already have.** Markin reads context where it lives,  warehouse, CDP, billing, product, so decisioning does not wait on a full Data Cloud migration to see the customer.
- **Sized in revenue, arbitrated across channels.** Every opportunity carries expected incremental value, so a service action, a sales task and an outbound message compete on the same scale instead of running in parallel.

## What Markin does not replace

To be explicit about scope, because procurement will ask:

- Markin does not replace the CRM or become the system of record for customers, cases or pipeline.
- Markin does not replace Journey Builder or campaign execution.
- Markin does not manage consent, preferences or data governance.
- Markin does not require a Salesforce migration; it reads context and writes decisions back.

## Where Markin fits

Markin is not a CRM and has no ambition to be the system of record. It reads the context Salesforce maintains, decides what is commercially worth doing, and returns that decision so Salesforce can execute it through the journeys, tasks and agent surfaces you already run.

- **The record stays the record.** Ownership of contacts, consent, cases and pipeline does not move. Markin reads context in place and never becomes a second source of truth.
- **Decisions reach humans too.** The same ranked opportunity that triggers a journey can surface as a prioritised prompt for a retention agent or account manager, with the reason and the expected value attached.
- **Proof that survives finance review.** Every decision runs against control, so the number presented is incremental revenue rather than conversions credited to a campaign.

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

## When Markin is not the right answer

- Your commercial motion is a single product with no repeat purchase or upgrade path, so there is nothing to prioritise between.
- You need better reporting on existing journeys rather than a different way of choosing them.
- Customer data is fragmented to the point that no reliable outcome history exists yet.

## FAQ

**Doesn't Einstein Next Best Action already do decisioning?**

Einstein Next Best Action is Salesforce's recommendation engine: it surfaces personalised action suggestions from rules and AI-predicted scoring, largely to sales and service users. Agentforce adds autonomous execution and Marketing Cloud Next adds agentic campaign assembly, but the full picture depends on adopting Data Cloud as the unification layer. Markin produces one ranked, revenue-sized decision per customer that those clouds consume, without a Data Cloud migration first.

**Do we have to replace Marketing Cloud?**

No. Orchestration and delivery stay in Salesforce. Markin decides which opportunity is worth acting on and passes it back as the trigger for the right journey or agent task.

**We already have Data Cloud. Isn't the decision covered?**

Data Cloud unifies profiles and builds audiences. That answers what we know about a customer. Choosing which of several possible commercial actions has the highest incremental value, and proving it against control, is a different job.

**How does the decision get back into Salesforce?**

As a field on the record or an event, so existing journeys, flows and agent consoles can consume it without new interfaces for the team.

**Can service and sales teams use the same decisions?**

Yes. A ranked opportunity is channel-agnostic. The same decision can be delivered as a message, an in-product offer or a prompt for a human, and each route is measured against the same holdout.

**Is this a long integration project?**

The first deployment targets one revenue theme and one activation route. The requirement is reliable customer context and outcome history, not a re-platforming.

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

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 Salesforce 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 Salesforce?**

On the assumptions preloaded above, 5.0M customers at 30 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-salesforce