---
title: Markin vs Salesforce Einstein and Agentforce: decisions vs workflows
url: https://markin.ai/compare/markin-vs-salesforce-einstein-agentforce
kind: vs
description: Einstein scores and Agentforce automates work inside Salesforce. Markin decides which revenue hypothesis is worth testing and proves it against a holdout.
updated: 2026-08-20
---

# Markin vs Salesforce Einstein and Agentforce: decisions vs workflows

> Einstein predicts and scores inside Salesforce objects; Agentforce runs agentic workflows over that estate. Markin is upstream of both: it investigates why ARPU is stuck across a consumer base, forms and sizes hypotheses, chooses one action per customer, and measures it against a randomised control group.

## In short

- Salesforce Einstein / Agentforce: What is the next step on this record or in this process? Output: Scores, recommendations, drafted content and automated workflow steps.
- Markin: Which opportunity in this base is worth acting on, for whom, and what is it worth? Output: A ranked, sized action per customer, executed through the systems you run.
- No mechanism generates new revenue hypotheses from the data; humans configure what the platform then executes.
- Markin decides; Salesforce executes wherever the record is the right surface.

## The short answer

Salesforce is strongest where the record is the unit of work: a case, a lead, an opportunity, a service conversation. Markin is strongest where the customer base is the unit of work and the question is which of ten thousand possible actions is worth taking this week, and what it returned.

## The two options

**Salesforce Einstein and Agentforce**, Predictive scoring, generative assistance and agentic workflows across the Salesforce estate, grounded in Data Cloud and executed in Sales, Service and Marketing Cloud.

Choose it when the work you want automated already happens inside Salesforce records and processes.

- Record-centric
- Runs on Data Cloud
- Deep CRM automation

**Markin**, An autonomous growth-science team that works the base rather than the record: hypothesis generation, sizing, arbitration, execution through your stack and holdout measurement.

Choose it when growth depends on testing far more revenue hypotheses per quarter than a team can design by hand.

- Base-centric
- Holdout by default
- Executes through existing systems

## Line by line

| Dimension | Markin | Salesforce Einstein and Agentforce |
| --- | --- | --- |
| What it is | An autonomous growth-science team: it investigates why revenue per customer is stuck and acts on what it finds. | An AI layer and agent platform embedded across the Salesforce clouds. |
| What it decides | Which commercial opportunity deserves to exist for each customer this week, what it is worth, and when the right answer is to do nothing. | The next step on a record or in a workflow: score, route, draft, escalate, recommend. |
| Where hypotheses come from | Generated by Markin from customer, product, pricing and technical-health data, then sized before anyone builds anything. | Configured by admins and architects as flows, models and agent instructions. |
| Scope of action | Marketing, product, pricing and technical-health hypotheses, arbitrated against each other in one queue. | Sales, service and marketing processes inside the Salesforce estate. |
| How the work reaches the customer | Written back into the systems you already run, as attributes, events or API calls. Markin does not add a new customer-facing surface. | Native across Salesforce objects and channels, which is where its advantage is. |
| How impact is proven | A randomised holdout on every decision. The reported number is incremental revenue and ARPU, not attributed conversions. | Pipeline, case and campaign reporting inside Salesforce; holdouts are possible but not the default. |
| Where the data sits | Reads context where it already lives, warehouse, CDP, product and billing systems. No new system of record. | Data Cloud plus the Salesforce object model. |
| Governance and control | Every action carries its hypothesis, its expected value, its guardrails and its control group, reviewable before launch. | Mature enterprise permissions, audit and admin controls. |
| Time to a verified number | One revenue theme, one channel, one holdout: a defensible incremental number inside 90 days. | Depends on the implementation programme; the estate is deep and configuration is real work. |
| Best fit | Large B2C bases where the constraint is how many good hypotheses get tested, not how many messages get sent. | Organisations whose commercial process lives inside Salesforce. |

## What each layer is

**Salesforce Einstein / Agentforce**, Predictive scoring, generative assistance and agentic workflows across the Salesforce clouds, grounded in Data Cloud.

Answers: What is the next step on this record or in this process?

**Markin**, A growth-science layer over the whole consumer base that generates, sizes, launches and proves revenue hypotheses.

Answers: Which opportunity in this base is worth acting on, for whom, and what is it worth?

## Side by side

|  | Salesforce Einstein / Agentforce | Markin |
| --- | --- | --- |
| Question it answers | What is the next step on this record or in this process? | Which opportunity in this base is worth acting on, for whom, and what is it worth? |
| Primary input | Salesforce objects, Data Cloud profiles, flows and agent instructions. | Warehouse, product, billing and contact history; margins and constraints. |
| Primary output | Scores, recommendations, drafted content and automated workflow steps. | A ranked, sized action per customer, executed through the systems you run. |
| Usual owner | Salesforce architects, sales and service operations. | Growth, data science and revenue leadership. |
| How it's measured | Pipeline, case and campaign metrics inside Salesforce. | Incremental revenue and ARPU against a randomised holdout. |

## What Salesforce Einstein and Agentforce does better

- **Nothing beats it inside its own estate.** If the action is on a case, a lead or a service conversation, Agentforce acts natively where the record lives, with the permission model already in place. Markin would be routing an action back into Salesforce to do the same thing worse.
- **Sales and service coverage is far broader.** Markin has no opinion on quoting, case deflection, field service or seller productivity. Those are large, valuable problems and Salesforce owns them.
- **One vendor, one audit trail.** For regulated organisations already standardised on Salesforce, keeping governance in one place has real value that a second system has to earn.

## Which one to pick

**Choose Markin if**

- Your growth question is about millions of consumers, not thousands of records.
- You need hypotheses the organisation has not thought of yet, sized before anyone builds them.
- You want incremental ARPU against a control group as the standard reporting unit.
- Pricing, product friction and technical anomalies are as likely to be the answer as a campaign.
- You want the decision layer to be portable across the stack, not tied to one estate.

**Choose Salesforce alone if**

- The work happens on Salesforce records and should stay there.
- Your priority is seller and agent productivity.
- Consolidating on one vendor outweighs best-of-breed decisioning.
- Your consumer base is small enough to plan manually.
- You are mid-implementation and cannot absorb another system this year.

## What stays unanswered inside the estate

Salesforce answers what to do with the record in front of you. It does not investigate the base to find the records that should be in front of you, nor size what acting on them is worth.

- No mechanism generates new revenue hypotheses from the data; humans configure what the platform then executes.
- Cross-estate arbitration is missing: a Marketing Cloud journey and an in-product placement never compete on expected value.
- Reporting attributes outcomes to processes rather than isolating incrementality.
- Non-marketing causes of flat ARPU, pricing, friction, technical faults, have no owner.

## Where Markin fits

Markin decides; Salesforce executes wherever the record is the right surface. The decision arrives as an attribute or platform event and the existing flow takes it from there.

- **No rip and replace.** The Salesforce estate keeps every process it owns today.
- **Hypotheses at machine speed.** Markin proposes and sizes what nobody has time to analyse.
- **Control groups as standard.** Every action reports incremental value, not activity.

Next: [Revenue Discovery](https://markin.ai/solutions/revenue-discovery)

## When Markin is not the right answer

- Your commercial model is B2B enterprise sales with a small account list.
- You need CRM, case management or field service: Markin is not that.
- There is no reliable consumer-level data outside the CRM to reason over.

## FAQ

**Is Markin a Salesforce replacement?**

No. Salesforce remains the system of record and, in many cases, the execution surface. Markin decides which opportunity is worth acting on across the base and hands the action back to Salesforce or to whichever system owns that touchpoint.

**How is Markin different from Agentforce?**

Agentforce automates work that a human defined, inside the Salesforce estate. Markin decides what work should exist at all, across marketing, product, pricing and technical health, and proves each decision against a holdout.

**What does Salesforce do better?**

Everything record-centric: sales process, case management, service automation, seller productivity, and native action inside its own objects with an enterprise permission model already in place.

**Do we need Data Cloud for Markin to work?**

No. Markin reads context where it already lives, typically the warehouse. If Data Cloud is where your unified profile sits, it can be that instead.

**How is Markin different from the decisioning or AI already inside salesforce einstein / agentforce?**

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 einstein / agentforce 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 einstein / agentforce?**

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/markin-vs-salesforce-einstein-agentforce