Migrating from Google Optimize to Optimizely: A Complete Guide

When Google retired Google Optimize on September 30, 2023, it left a large group of teams with running experiments, a library of past learnings, and a testing habit — but no tool. Google's own guidance pointed users toward third-party experimentation platforms rather than a direct successor. If you are one of those stranded teams and Optimizely is on your shortlist, this guide walks through what actually moves over, what does not, and how to rebuild your program in Optimizely without losing momentum. It is written to be honest about the trade-offs: Optimizely is a paid, enterprise-grade platform, not a free drop-in clone, and treating the migration as a fresh setup rather than a lift-and-shift will save you time.

Why Google Optimize users need a new home

Google Optimize was free, ran client-side changes through a visual editor, and integrated tightly with Google Analytics — first Universal Analytics, later GA4. Your experiment objectives were Analytics goals, your audiences leaned on Analytics segments and rules, and your reporting lived inside Analytics. That tight coupling was the product's biggest strength and, after the sunset, its biggest liability: the experiments stopped, but the reporting dependency did not disappear.

There is no automated importer from Google Optimize into any third-party tool, Optimizely included. Google did not publish an export format for live experiment configurations, and the platforms differ enough that a mechanical translation would produce broken tests. The realistic path is a structured rebuild: inventory what you had, map each concept to its Optimizely equivalent, and recreate the experiments you still care about. The good news is that most Google Optimize concepts have a clear counterpart, so the rebuild is mechanical rather than creative.

How Optimizely maps to what you had in Google Optimize

Before touching the tool, translate your mental model. Almost every Google Optimize concept has a direct equivalent in Optimizely Web Experimentation, with one important split: anything you ran client-side maps to Web Experimentation, while anything you wished you could run on the server (pricing logic, search ranking, checkout flows) maps to Optimizely Feature Experimentation, which Google Optimize never supported.

The single biggest mindset shift is the analytics relationship. In Google Optimize, Analytics was the results system. In Optimizely, the Stats Engine computes significance natively on the Results page, and Google Analytics becomes an optional integration for teams that still want variation data inside GA4 reports. You are moving from "Analytics tells me who won" to "Optimizely tells me who won, and I can also send that to Analytics."

Choose your migration path: Web Experimentation vs Feature Experimentation

Google Optimize only did one thing: client-side changes rendered in the browser. Optimizely gives you two doors, and picking the right one up front prevents a painful re-platforming later. Optimizely Web Experimentation is the natural home for the marketing-page, copy, and layout tests you ran in Optimize. Optimizely Feature Experimentation is a server-side and full-stack product driven by SDKs and feature flags — the right choice when the change lives in application logic rather than the DOM.

flowchart TD
  A[Experiment you ran or want to run] --> B{Where does the change live?}
  B -->|Copy, layout, styling in the browser| C[Optimizely Web Experimentation]
  B -->|Pricing, search, checkout, backend logic| D[Optimizely Feature Experimentation]
  C --> E{Need it to persist as a permanent feature?}
  E -->|No, it is a marketing test| F[Web Experimentation only]
  E -->|Yes, ship the winner as a flag| D
  D --> G[Install an SDK and wrap the change in a feature flag]
  F --> H[Install the snippet and rebuild in the Visual Editor]

Most Google Optimize refugees start with Web Experimentation because it mirrors what they already knew. Teams with engineering resources often add Feature Experimentation for tests Optimize could never run — the ones that were previously off-limits because there was no server-side option. For a deeper treatment of that server-side path, see the guide on server-side A/B testing with Optimizely.

Step-by-step: migrating to Optimizely Web Experimentation

This is the path most former Optimize users take first. Each step maps to something you already did in Google Optimize, so treat it as translation, not invention.

Step 1: Create a project and install the snippet

An Optimizely project is the equivalent of your Optimize container — a workspace with its own snippet, experiments, and collaborators. After your project exists, install the one-line JavaScript snippet as high in the <head> as possible, before analytics or heatmapping tags. This replaces the Google Optimize snippet you previously deployed (often through Google Tag Manager).

<head>
  <meta charset="utf-8">
  <!-- Optimizely snippet: place before analytics tags -->
  <script src="https://cdn.optimizely.com/js/12345678.js"></script>
  <!-- Analytics, tag manager, and other scripts go after -->
</head>

Do not modify the snippet or add an id attribute to it — Optimizely requires it verbatim. If you separate staging and production, create a project per environment, exactly as many teams did with separate Optimize containers. See Optimizely's guide to implementing the one-line snippet for placement rules and the cross-origin considerations.

Step 2: Rebuild your experiments

In Optimizely, go to the Experiments dashboard, click Create New Experiment, and choose A/B Test (or Multivariate or Redirect to match what you ran in Optimize). Set where the experiment runs by targeting a URL or a saved page, then build your variations in the Visual Editor — the closest analog to Optimize's visual editor — or drop into custom code for changes the WYSIWYG editor cannot express.

Rather than porting every historical test, prioritize the experiments that were live or queued when Optimize shut down, plus any winners you never fully rolled out. Optimizely's six-step build flow (targeting, variations, audiences, metrics, traffic allocation, QA) is documented in Steps to create an experiment.

Step 3: Recreate audiences

Optimize targeting used a mix of Analytics audiences, URL rules, geo, device, behavior, and custom JavaScript variables. Optimizely's Audience Builder covers the same ground. Build audiences from standard conditions — Browser, Cookie, Device, Location, Language, Ad Campaign, and more — and combine them with AND and OR logic. For the logged-in-state and on-page-variable checks you likely expressed as Optimize JavaScript conditions, use the Custom JavaScript condition, which must evaluate to a Boolean and must reference variables defined above the Optimizely snippet on the page.

// Custom JavaScript audience condition: only logged-in visitors
// window.logged_in must be set above the Optimizely snippet in the page.
window.logged_in === "yes"

If you invested in GA4 audiences, you do not have to rebuild them by hand: Optimizely can import GA4 audiences directly for targeting. Full details are in Target audiences using the Audience Builder.

Step 4: Define metrics

This is where the analytics relationship changes most. In Optimize, your objectives were Google Analytics goals. In Optimizely, you define events (clicks, pageviews, or custom events) and then build metrics on those events. The first metric you add becomes the primary metric that decides whether the experiment wins or loses; add secondary metrics and monitoring goals for downstream effects. Click and pageview events are configured against a page; custom events are fired from your own code.

// Fire a custom conversion event from your site code
window["optimizely"] = window["optimizely"] || [];
window["optimizely"].push({
  type: "event",
  eventName: "signup_completed"
});

Because Optimizely computes significance itself, you are no longer waiting on Analytics goal completions to read a result. Configure events and metrics as described in Configure events.

Step 5: Reconnect Google Analytics (GA4)

Losing Optimize does not mean losing the GA4 relationship. Optimizely offers a native GA4 integration that shares each visitor's experiment assignment with GA4, so you can segment GA4 reports by experiment and variation — the analysis pattern many teams relied on in Optimize. Enable it under Settings > Integrations, then add it to individual experiments on their Integrations tab.

The integration sends assignments to GA4 either as a custom dimension (the optimizely_decision event) or as the pre-defined experience_impression event, and the Report Generation feature can push variations to GA4 as audiences automatically. Place your Google tag close to the Optimizely snippet in the <head> to avoid data discrepancies. Configuration steps are in Optimizely's GA4 integration guide, and if you prefer to keep GA4 as your conversion source, see the OptiPilot walkthrough on sending GA4 events to Optimizely Web.

Step 6: QA and launch

Optimize made it easy to preview a variant; Optimizely's Preview tool does the same, but an enterprise platform rewards a more disciplined pre-launch check. Verify the snippet is present on the target pages, confirm each variation renders, check that events fire, and confirm audience conditions qualify the right visitors. Optimizely also runs automatic sample ratio mismatch (SRM) detection to flag a broken traffic split early. Work through the experiment QA checklist tool before publishing, then click Start Experiment. Once results arrive, interpret them on the Optimizely Results page.

When to choose Feature Experimentation instead

Google Optimize could never test server-side logic, so if your backlog was full of "we can't test that in Optimize" ideas — pricing, search ranking, recommendation algorithms, API-level changes — the migration is your chance to unlock them. Optimizely Feature Experimentation runs those tests through SDKs and feature flags rather than a browser snippet.

The workflow is different from the visual model you knew: install an SDK, define a feature flag in the Optimizely app, and wrap the change in a decide call so the SDK buckets each user and returns a variation. Optimizely provides quickstarts for JavaScript/Node, Python, Java, Go, C#, and more.

const { createInstance } = require("@optimizely/optimizely-sdk");

const optimizely = createInstance({ sdkKey: "YOUR_SDK_KEY" });

optimizely.onReady().then(() => {
  const user = optimizely.createUserContext("user_123", { plan: "free" });
  const decision = user.decide("checkout_flow");

  if (decision.variationKey === "single_page") {
    renderSinglePageCheckout();
  } else {
    renderMultiStepCheckout();
  }
});

Because a flag can persist after the experiment ends, the winning variation ships as a controlled rollout rather than a one-off test — a capability Optimize never offered. Start with the Feature Experimentation quickstarts, and adopt naming and lifecycle conventions early using the guide on feature flag best practices.

What does not carry over

An honest migration plan names the gaps up front.

• Cost. Google Optimize was free; Optimizely is a paid, quote-based enterprise platform. If budget is the blocker, decide that before investing setup time.

• Historical experiment data. There is no importer. Past Optimize results live in your Analytics history and any reports you exported — treat them as a reference archive, not migratable data. Export what you can from Optimize and Analytics while you still have access.

• Analytics-native reporting by default. In Optimize, results appeared in Analytics automatically. In Optimizely, results appear on the Results page, and GA4 reporting is an optional integration you configure deliberately.

• Objectives as Analytics goals. Metrics are defined inside Optimizely against events you configure; they are not inherited from Analytics goals.

• Free-tier concurrency limits, in reverse. Optimize's free tier capped how many experiments you could run at once. Optimizely does not impose that particular ceiling, but plan tiers and account settings govern what you can run — confirm the specifics with your account team rather than assuming parity.

The statistical model also differs. Optimize used a Bayesian approach; Optimizely's Stats Engine uses sequential testing with always-valid significance, which changes how and when you should stop a test. Review your project's statistical significance threshold so your stopping rules match the new engine rather than Optimize's.

Migration checklist

Use this as the recap when you plan the move:

• Inventory live, queued, and unshipped-winner experiments from Google Optimize.

• Export any Optimize and Analytics reports you want to keep as a historical archive.

• Decide, per experiment, whether it belongs in Web Experimentation (client-side) or Feature Experimentation (server-side).

• Create the Optimizely project(s) and install the snippet or SDK.

• Rebuild the priority experiments, audiences, and metrics.

• Reconnect GA4 if you still want variation data in Analytics.

• QA against the checklist, launch, and read results on the Results page.

Frequently asked questions

Is Optimizely a free Google Optimize replacement?

No. Google Optimize was free; Optimizely is a paid, enterprise experimentation platform with quote-based pricing. What you gain for the cost is native statistical reporting through the Stats Engine, server-side testing through Feature Experimentation, and enterprise governance that Optimize did not offer. If a free replacement is your hard requirement, Optimizely is not the match — but if you need capabilities Optimize lacked, the upgrade is substantial.

Can I import my Google Optimize experiments into Optimizely?

There is no automated importer, from Optimizely or any other vendor. Google did not provide an export format for live experiment configurations, so the migration is a structured rebuild. In practice this is less painful than it sounds because most Optimize concepts map directly to an Optimizely equivalent — you are recreating a handful of priority experiments, not thousands.

Will my Google Analytics data still work?

Yes, in two ways. Optimizely computes experiment results natively on its Results page, so you no longer depend on Analytics to know who won. Separately, the native GA4 integration shares each visitor's experiment assignment with GA4, letting you segment GA4 reports by experiment and variation much as you did with Optimize. GA4 becomes an optional analysis layer rather than the mandatory reporting engine.

How long does migration take?

The snippet and project setup take under a day. The real work is rebuilding experiments, audiences, and metrics, which scales with how many tests you carry over. Teams that migrate only their live and highest-value experiments are typically running in Optimizely within a week or two; teams that try to recreate their entire Optimize history take much longer, which is why prioritizing is the first checklist item.

Should I use Web Experimentation or Feature Experimentation?

Use Web Experimentation for browser-rendered changes — copy, layout, and styling — which is nearly everything you ran in Optimize. Use Feature Experimentation when the change lives in server-side or application logic, such as pricing, search, or checkout, which Optimize could not test at all. Many teams run both: Web Experimentation for marketing tests and Feature Experimentation to finally test the backend ideas that were off-limits before.