Markin + Optimizely integration
Product-side hypotheses executed as flags, read as revenue.
What is a Optimizely AI agent?
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 Markin reads
- Experiment definitions and assignment logs
- Feature flag exposure
What Markin writes back
- Proposed variants with expected value
- Targeting decisions per user
How the connection works
| Pattern | What it means here |
|---|---|
| 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 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
- 01Read experiment definitions, variations and results.
- 02Rank candidate tests by revenue at stake and time to power.
- 03Refuse tests that cannot reach power in a reasonable window.
- 04Launch the chosen variation through Optimizely.
- 05Maintain a randomised holdout across concurrent tests.
- 06Read outcomes back keyed to the decision ID.
- 07Report incremental revenue with confidence intervals.
- 08Retire variations that lose and promote the ones that win.
Scope of work
The growth work behind Optimizely
The list above is what Markin touches in Optimizely. A connector is only the surface. Below is the work itself: what an analyst, a lifecycle manager, a data scientist and an experimentation lead would do between them, running continuously against your own data.
Read the estate
Normally a data engineer, once, then never refreshed.
- 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
Normally a quarterly analyst deep dive.
- Detect involuntary churn from failed payments, card expiry and retry behaviour.
- Spot onboarding steps where activation falls off and revenue never starts.
Explain why
Normally a two-week investigation pulled off the roadmap.
- 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
Normally a workshop, limited to the ideas in the room.
- Write hypotheses continuously across marketing, product, pricing and technical health.
- Attach the expected revenue effect and the population it applies to.
Decide per customer
Normally segment rules refreshed when someone has time.
- 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
Normally a ticket, then a slot in next month's calendar.
- 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
Normally argued about, rarely measured.
- Read uplift in revenue per customer against that control.
- Report retention, ARPU, margin and contact pressure side by side.
Retire, govern and hand over
Normally nobody's job, so nothing is ever switched off.
- Show the whole decision trail when legal, finance or an auditor asks.
- Hand the team a portfolio they can read, question and overrule.
Teams use Optimizely together with
Optimizely questions
- 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.