---
title: Markin + Optimizely
url: https://markin.ai/integrations/optimizely
category: Experimentation and feature flags
description: Connect Markin to Optimizely: Markin proposes and sizes product hypotheses, Optimizely executes them, and impact is read in revenue.
---

# Markin + Optimizely

> Product-side hypotheses executed as flags, read as revenue.

**Job:** Decide what to test next and read the verdict honestly.

A Markin and Optimizely setup keeps Optimizely as the delivery and targeting layer for experiments while Markin decides what should be tested next, sizes it, and reads the verdict against a randomised holdout it maintains.

Experimentation platforms answer the question you brought them. Markin's job is to produce a continuous supply of questions worth asking, and to keep the ones that pay.

## What can you ask Markin to do in Optimizely?

- How do I decide what to A/B test next?
- How is continuous decisioning different from experimentation?
- How do I run always-on experiments without a big analyst team?
- How do I stop shipping tests that never reach significance?

## What Markin does in Optimizely

- Read experiment definitions, variations and results.
- Rank candidate tests by revenue at stake and time to power.
- Refuse tests that cannot reach power in a reasonable window.
- Launch the chosen variation through Optimizely.
- Maintain a randomised holdout across concurrent tests.
- Read outcomes back keyed to the decision ID.
- Report incremental revenue with confidence intervals.
- Retire variations that lose and promote the ones that win.

## The growth work behind Optimizely

### Read the estate

- Reconcile the same customer across billing, CRM, product and support identities.
- Read consent, subscription and channel eligibility state before anything else.

### Find where revenue is leaking

- Detect involuntary churn from failed payments, card expiry and retry behaviour.
- Spot onboarding steps where activation falls off and revenue never starts.

### Explain why

- Keep the query trail so an analyst can reproduce every number.
- Run the investigation automatically and return the drivers with their evidence.

### Write hypotheses worth funding

- Write hypotheses continuously across marketing, product, pricing and technical health.
- Attach the expected revenue effect and the population it applies to.

### Decide per customer

- Suppress an action rather than send a weak one, and log why.
- Choose the next best action for each customer, for each moment.

### Execute in the tools you already run

- Roll a decision batch back cleanly when something looks wrong.
- Leave the estate exactly as it was if Markin stops writing.

### Prove it caused the revenue

- Read uplift in revenue per customer against that control.
- Report retention, ARPU, margin and contact pressure side by side.

### Retire, govern and hand over

- Show the whole decision trail when legal, finance or an auditor asks.
- Hand the team a portfolio they can read, question and overrule.

Full catalogue: [Everything a growth team does, running every day.](https://markin.ai/growth-work)

## Activation patterns

- **Decision API.** The surface asks Markin for a decision at render time and receives one action plus its reason, with a defined fallback.
- **Triggered event.** Markin emits an event that starts or advances a journey. Near-immediate, and the channel keeps its own governance.

## What Markin reads from Optimizely

- Experiment definitions and assignment logs
- Feature flag exposure

## What Markin writes back

- Proposed variants with expected value
- Targeting decisions per user

## Used together with

- [amplitude](https://markin.ai/integrations/amplitude)
- [web-and-mobile-sdk](https://markin.ai/integrations/web-and-mobile-sdk)
- [snowflake](https://markin.ai/integrations/snowflake)
- [launchdarkly](https://markin.ai/integrations/launchdarkly)
- [stripe](https://markin.ai/integrations/stripe)
- [segment](https://markin.ai/integrations/segment)

## Related

- [Solution: revenue-expansion](https://markin.ai/solutions/revenue-expansion)
- [Comparison](https://markin.ai/compare/markin-and-optimizely)

## FAQ

**How does Markin connect to Optimizely?**

Decision API, Triggered event. The surface asks Markin for a decision at render time and receives one action plus its reason, with a defined fallback.

**What does Markin read from Optimizely?**

Experiment definitions and assignment logs; Feature flag exposure.

**What does Markin write back into Optimizely?**

Proposed variants with expected value Targeting decisions per user

**Do we have to move our data to Markin?**

No. Markin reads from your warehouse, product events and operational systems in place, on your compute, under the access rules your data team already set. Nothing is copied into a separate customer base and there is no vendor-side profile store to migrate off later.

**Does Markin replace our engagement platform or CDP?**

No, and it should not. Your engagement platform keeps the channel, the templates, the deliverability and the governance. Your CDP keeps identity and consent. Markin adds the layer neither has: deciding which action deserves to exist for each customer, and proving it against a holdout.

**What if the system we use is not listed?**

The four activation patterns cover almost everything: attribute write-back, triggered event, decision API and direct surface rendering. Any system that exposes an API, accepts a table, or can read a warehouse column can receive decisions. New connectors are built during deployment, typically in days.

Source: https://markin.ai/integrations/optimizely