---
title: Markin vs Optimizely: running tests vs deciding what to test
url: https://markin.ai/compare/markin-vs-optimizely
kind: vs
description: Optimizely runs the experiments you design. Markin decides which experiments are worth running, sizes them in revenue, and reads every one against a holdout.
updated: 2026-08-20
---

# Markin vs Optimizely: running tests vs deciding what to test

> 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.

## In short

- Optimizely: Which variant performs better on this surface? Output: Delivered variants, feature gates and statistical readouts.
- Markin: Which hypothesis is worth traffic, for whom, and what is it worth? Output: A prioritised experiment queue and a decision per customer, executed through your stack.
- Nothing sizes a test in revenue before it consumes traffic.
- Markin fills and orders the queue, then executes wherever the surface lives, including Optimizely, and reads every result against a control group.

## The short answer

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.

## The two options

**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

**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

| 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 each layer is

**Optimizely**, An experimentation and feature-management platform that delivers variants, allocates traffic and reports results.

Answers: Which variant performs better on this surface?

**Markin**, A growth-science layer that decides which hypotheses deserve to be tested, sizes them and proves them across the base.

Answers: Which hypothesis is worth traffic, for whom, and what is it worth?

## Side by side

|  | Optimizely | Markin |
| --- | --- | --- |
| Question it answers | Which variant performs better on this surface? | Which hypothesis is worth traffic, for whom, and what is it worth? |
| Primary input | Instrumented surfaces, targeting rules, variant definitions. | Warehouse, product, pricing and contact history; margins and constraints. |
| Primary output | Delivered variants, feature gates and statistical readouts. | A prioritised experiment queue and a decision per customer, executed through your stack. |
| Usual owner | Product engineering and CRO. | Growth, data science and revenue leadership. |
| How it's measured | Lift on the test metric for exposed traffic. | Incremental revenue and ARPU against a randomised holdout. |

## What Optimizely does better

- **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.

## Which one to 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.

## 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.

## Where Markin fits

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.

Next: [Growth Optimization](https://markin.ai/solutions/growth-optimization)

## When Markin is not the right answer

- 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.

## FAQ

**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.

Source: https://markin.ai/compare/markin-vs-optimizely