COMPARE/Markin vs Salesforce
vsMarkin vs Salesforce Einstein and Agentforce: decisions vs workflows
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
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.
The short answerLast updated: August 2026
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.
01
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
02
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
The same ten questions, answered for both.
| 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 is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
2.0M
customers at $24 ARPU / month
Addressable revenue
$259.2M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$44.1M – $90.7M
incremental revenue per year
Measured on treated cohorts against a randomised holdout, read over a full measurement window rather than the first weeks. Anonymised range across Markin deployments in large B2C bases; your own holdout is the number that decides. The figures above apply that range to the reachable share of the base on this page's assumptions; they are arithmetic, not a forecast for your business.
Run it on your own numbersThe unsolved part
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.
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.
Job to be done
The same work, at a different throughput.
Nothing below needs a tool that does not exist. It needs the work to happen continuously instead of once a quarter, and to be proven against a holdout instead of argued about.
| Job to be done | With salesforce einstein / agentforce alone | With Markin |
|---|---|---|
| Notice that revenue per customer is drifting in a segment | Someone spots it in a dashboard review, weeks after it started. | Detected as a signal the day the drift clears noise, with the segment already sized. |
| Explain why it is happening | An analyst is pulled off the roadmap for a two-week investigation. | An investigation runs automatically and returns the drivers with their evidence. |
| Come up with hypotheses worth testing | A workshop produces the handful of ideas the room happened to think of. | Hypotheses are written continuously across marketing, product, pricing and technical health. |
| Decide which hypotheses deserve budget | Prioritised by seniority and gut feel, with no size attached. | Each one is sized in revenue and ranked before anything is built. |
| Choose the next best action for one customer | Segment rules and campaign calendars decide, refreshed when someone has time. | Chosen per customer, per moment, against everything else competing for that customer. |
| Actually launch it | A ticket to the lifecycle team, then a slot in next month's calendar. | Executed inside the systems you already run, with no new channel to adopt. |
| Prove it caused the revenue | Reported against non-qualifiers or a global holdout, if at all. | Every decision carries a randomised control group; uplift is read against it. |
| Kill what does not work | Programmes survive because nobody owns retiring them. | Failing to beat control retires the programme automatically. |
| Do all of it again next week | Capacity-bound: four to eight tests a quarter. | Hundreds of hypotheses in flight in parallel, continuously. |
Honest take
What Salesforce Einstein and Agentforce does better.
A comparison that only flatters one side is not worth reading. These are the cases where we would tell you to stay where you are.
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.
Where Markin fits
Not a replacement. A growth-science team on top.
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.
Evidence standard
Most of this category reports its own lift.
None of the major engagement, CDP or personalisation vendors publishes an independently verified uplift figure for its decisioning product. Where numbers exist, they come from vendor-commissioned studies or single-customer case studies with no disclosed holdout methodology. The most rigorous public research in the category is not flattering to anyone, including us, which is exactly why we build against it.
BCG reports that when organisations adopt rigorous incrementality testing, they typically find 20% to 40% of their active next-best-action programmes deliver marginal to negative lift.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)The same research flags novelty effects, new programmes show inflated early results, and recommends 8 to 12 weeks before drawing conclusions.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)Global-holdout, programme-level ROI measurements often overstate impact through halo effects, pull-forward effects and experiment contamination.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)
How Markin holds itself to it
- Every decision Markin makes carries a control group. Uplift is reported against that holdout, not against the customers who did not qualify.
- Results are read over a full measurement window rather than in the first weeks, so novelty is not mistaken for effect.
- Programmes that fail to beat control are retired automatically. Killing decisions that do not pay is part of the loop, not an annual review.
- The one figure we quote about ourselves is a range, not an average: +17% to +35% ARPU on treated cohorts against a randomised holdout, across Markin deployments in large B2C bases. We publish no industry benchmark, because we could not source one we would be willing to defend. Your holdout is the number that matters.
Time to value
90 days to a number that survived a holdout.
No replatform, no data migration, no rebuild of the channels you already run. If the first cohorts do not beat control, nothing scales and you have lost a quarter, not a roadmap.
Weeks 0–2
Read the context you already have
Markin connects to the data and the channels you run today, salesforce einstein / agentforce included. No migration, no replatform, no new source of truth.
Weeks 3–6
First sized opportunities in test
Opportunities are ranked by expected value, treatments are chosen per customer, and the first cohorts go live with a randomised holdout attached.
Weeks 7–12
First verified incremental revenue
Results are read over a full measurement window. What beats control scales; what does not is retired. Nothing scales on a number that has not survived a holdout.
Which one you should 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.
When you don’t need Markin.
- 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.
Questions buyers ask.
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.