2025 Optimizely Feature Experimentation release notes

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December

• Released the Experiment Review agent, which lets you Run a pre-launch experiment review with Opal. The agent reviews your experiment configuration and recommends changes to maximize your odds of reaching statistical significance.
• Released the Optimizely Edge Agent, which integrates edge computing and serverless architecture to let you seamlessly conduct targeted deliveries and experiments across various platforms and architectures.

October

• Released version 1.0.0 of the Cloudflare Worker template. See the release on Github for a complete list of updates. 

September

• Added the new Optimizely Reporting Metric Impact Report dashboard for Experimentation, which aggregates data on the impact of your metrics.

July

• Released ratio metrics for A/B and multivariate tests, which let you select different events for the numerator and the denominator to reflect business-specific key performance indicators, such as revenue per add-to-cart click or feature use per account. See Create a ratio metric in the metric builder for instructions.
• Added experiment ID and variation ID to the decision notification listener payload.

May

• Released v6.0.0 of the JavaScript SDK. This update includes various changes, including the following:
  • Unified the JavaScript (Browser) and JavaScript (Node) developer documentation into one unified reference. See JavaScript SDK reference for versions 6.0.0 and later.
  • Split the createInstance call into multiple factory functions for greater flexibility and control. See Initialize the JavaScript SDK.
  • Disabled VUID tracking by default. See VUID manager.
  • Added support for async user profile service and async decide method calls. 
  • See the full release notes on GitHub.
• Updated the Experimentation Usage & Billing dashboards to include monthly active users (MAUs) by experiment and project.
• Released Optimizely Opal Chat for Experimentation. Opal automates tasks, surfaces insights, and guides decision-making. 
• Released the Optimizely Opal results summary, which automatically summarizes your A/B test results in plain language.
• Added the ability to ideate with Opal to get test ideas.
• Released the @ExperimentPlan prebuilt instruction agent to get feedback on a test plan from Opal. 

Usage and billing update

Effective May 7, 2025, access to Optimizely Opal features across Content Marketing Platform, Web Experimentation, Feature Experimentation, Personalization, Content Management System (SaaS), Collaboration, and Optimizely Data Platform will transition to a credit-based usage and billing model.

For a full list of Optimizely Opal features, see Optimizely Opal and AI features.

April

Usage and billing update

Effective May 7, 2025, access to Optimizely Opal features across Content Marketing Platform, Web Experimentation, Feature Experimentation, Personalization, Content Management System (SaaS), Collaboration, and Optimizely Data Platform will transition to a credit-based usage and billing model.

For a full list of Optimizely Opal features, see Optimizely Opal and AI features.

March

• Released granular roles and permissions for audience roles.
• Released teams to manage granular permissions for entities in bulk.
• Added new API endpoints to support granular roles and permissions. See API changelog.

Warehouse-Native Experimentation Analytics

Warehouse-Native Experimentation Analytics is now generally available. The integration brings the elements of warehouse-native Optimizely Analytics to Feature Experimentation and Web Experimentation. Teams can analyze experiment performance, identify winning variations, and conduct deeper analyses on experiments that ensure data security and privacy and avoid data duplication or movement.

• Enhance experimentation results by integrating Optimizely Experimentation data with additional insights from the data warehouse. See scorecard for more information.
• Specify key user interactions to assess engagement and evaluate impact using custom events.
• Create specific experiment-focused metrics (such as conversion, numeric aggregation, ratio, and more).
• Segment users by common behaviors into cohorts for precise analysis and targeted insights.
• Create custom metrics and derived columns to transform data to gain deeper insights.
• Use the Stats Engine to ensure reliable results and advanced analysis capabilities.
• Use CUPED to reduce the impact of random variation and surface insights quicker.
• Switch effortlessly between configuring experiments and conducting deep experimentation analysis from both Feature Experimentation and Web Experimentation.
• Filter results by user segments, analyze trends over time, and track variation performance through designated funnels via Experimentation Analytics > Explore.
• Uses the Opti ID Admin Center for user management, giving you a single login point to switch among your Optimizely products. See the Opti ID documentation to learn more about how to use it.
• Updated the Analytics UI to match Optimizely styling.

Learn more about Warehouse-Native Experimentation Analytics.