COMPARE/Markin vs Optimizely
vsMarkin vs Optimizely: running tests vs deciding what to test
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
Optimizely is an experimentation and feature-management platform: it delivers variants, assigns traffic and reports statistical results. Markin decides what should be tested in the first place, sizes each hypothesis in revenue, arbitrates them against each other, and proves the winners against a randomised holdout.
The short answerLast updated: August 2026
Experimentation platforms removed the technical cost of testing. The remaining constraint is human: someone has to invent the hypothesis, argue for it and design it. Markin removes that constraint, and Optimizely remains an excellent place to deliver the resulting test on web and product surfaces.
01
Optimizely
Experimentation and feature management across web, app and server, with traffic allocation, targeting, stats engine and rollout controls.
Choose it when engineers and product teams need a reliable way to ship and measure variants safely.
- Runs the test
- Feature flags
- Stats engine
02
Markin
An autonomous growth-science team that decides which hypotheses deserve traffic, sizes them, launches across channels and reports incremental ARPU.
Choose it when the test queue is short on ideas worth testing, not short on delivery capacity.
- Writes the queue
- Sizes in revenue
- Per-customer decisions
Line by line
The same ten questions, answered for both.
| Dimension | Markin | Optimizely |
|---|---|---|
| What it is | An autonomous growth-science team: it investigates why revenue per customer is stuck and acts on what it finds. | An experimentation and feature-management platform. |
| 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. | Which variant a visitor sees, and when a feature rolls out. |
| Where hypotheses come from | Generated by Markin from customer, product, pricing and technical-health data, then sized before anyone builds anything. | Written by product managers, engineers and CRO specialists. |
| Scope of action | Marketing, product, pricing and technical-health hypotheses, arbitrated against each other in one queue. | Web, app and server-side surfaces the team instruments. |
| 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. | Delivers variants and gates features directly, which it does very well. |
| How impact is proven | A randomised holdout on every decision. The reported number is incremental revenue and ARPU, not attributed conversions. | Rigorous statistics on the tests you choose to run. |
| Where the data sits | Reads context where it already lives, warehouse, CDP, product and billing systems. No new system of record. | Experiment exposure and outcome events, plus warehouse export. |
| Governance and control | Every action carries its hypothesis, its expected value, its guardrails and its control group, reviewable before launch. | Rollout safety, targeting rules and flag lifecycle management. |
| Time to a verified number | One revenue theme, one channel, one holdout: a defensible incremental number inside 90 days. | Fast per test; total throughput is bounded by hypothesis supply and design time. |
| Best fit | Large B2C bases where the constraint is how many good hypotheses get tested, not how many messages get sent. | Teams with the ideas and the engineering capacity to test them. |
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
The test queue is the bottleneck
Most organisations can run far more experiments than they can design. The backlog is not full of sized, credible hypotheses; it is full of opinions ordered by who asked loudest.
- Nothing sizes a test in revenue before it consumes traffic.
- On-surface tests cannot arbitrate against a message, a price or a service action.
- Wins are reported as lift on a metric, rarely as incremental revenue per customer.
- Retiring a winner that stopped working is a manual, easily forgotten step.
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 optimizely 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 Optimizely 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.
Delivery and safety are its own discipline
Feature flags, progressive rollout, kill switches and SDK-level targeting are hard engineering problems. Markin does not do them and should not.
Statistical rigour on-surface
Sequential testing, sample ratio mismatch detection and variance reduction on web traffic are mature in Optimizely. It is a good place to read a test.
Engineering trust
If your teams already gate every release behind flags, that workflow is worth protecting. Markin should feed it, not fight it.
Where Markin fits
Not a replacement. A growth-science team on top.
Markin fills and orders the queue, then executes wherever the surface lives, including Optimizely, and reads every result against a control group.
Hypotheses with a price tag
Expected value decides what gets traffic.
Cross-channel by default
A web test competes with a message and a pricing change.
Scale or retire
Every winner is re-read, and decayed winners are retired.
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, optimizely 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 test velocity is limited by ideas, not by infrastructure.
- You want each hypothesis sized in revenue before it takes traffic.
- Tests need to span channels, not just on-site surfaces.
- You want a per-customer decision, not a variant per visitor.
- Board reporting needs incremental ARPU, not lift on a page.
Choose Optimizely alone if
- You need feature flags and safe rollout above all.
- Testing is confined to web and product surfaces.
- Your team already produces more good hypotheses than it can run.
- Engineering owns the experimentation workflow end to end.
- Traffic volumes make on-site testing the fastest route to answers.
When you don’t need Markin.
- You need feature flags and rollout tooling: Markin does not provide them.
- Testing is confined to a single page and a single metric.
- Traffic is too low for controlled reads at any level.
Questions buyers ask.
Does Markin replace Optimizely?
No. Optimizely remains a good place to deliver and read on-surface tests. Markin decides which tests deserve to exist, sizes them, and extends the same discipline to channels Optimizely does not touch.
How is this different from an experimentation roadmap?
A roadmap is a human artefact refreshed quarterly. Markin regenerates and re-sizes the queue continuously from data, including hypotheses nobody proposed.
What does Optimizely do better?
Variant delivery, feature flags, safe rollout and on-surface statistical rigour.
Can both run together?
Yes. Markin can hand a chosen treatment to Optimizely for delivery, and read the outcome alongside every other action the customer received.
How is Markin different from the decisioning or AI already inside optimizely?
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 optimizely 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 optimizely?
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.