2025 Optimizely Feature Experimentation release notes
- Optimizely Feature Experimentation
Follow this article to receive email notifications when new Optimizely Feature Experimentation content is added for 2025.
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 AgentOptimizely 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 templateCloudflare 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 metricsratio 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 payloaddecision 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 referenceJavaScript 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.Initialize the JavaScript SDK.
- Disabled VUID tracking by default. See VUID manager.VUID manager.
- Added support for async user profile serviceasync user profile service and async decide method callsasync decide method calls.
- See the full release notes on GitHub.
- Updated the Experimentation Usage & Billing dashboardsUsage & Billing dashboards to include monthly active users (MAUs) by experiment and project.
- Released Optimizely Opal ChatOptimizely Opal Chat for Experimentation. Opal automates tasks, surfaces insights, and guides decision-making.
- Released the Optimizely Opal results summaryOptimizely Opal results summary, which automatically summarizes your A/B test results in plain language.
- Added the ability to ideate with Opal to get test ideasget test ideas.
- Released the @ExperimentPlan @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 featuresOptimizely 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 featuresOptimizely Opal and AI features.
March
- Released granular roles and permissions for audience roles audience roles.
- Released teamsteams to manage granular permissions for entities in bulk.
- Added new API endpoints to support granular roles and permissions. See API changelogAPI 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 scorecardscorecard for more information.
- Specify key user interactions to assess engagement and evaluate impact using custom eventscustom events.
- Create specific experiment-focused metrics (such as conversionconversion, numeric aggregationnumeric aggregation, ratioratio, and more).
- Segment users by common behaviors into cohortscohorts for precise analysis and targeted insights.
- Create custom metricsmetrics and derived columnsderived columns to transform data to gain deeper insights.
- Use the Stats EngineStats Engine to ensure reliable results and advanced analysis capabilities.
- Use CUPEDCUPED 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 Experimentation Analytics > Explore.
- Uses the Opti ID Admin CenterOpti ID Admin Center for user management, giving you a single login point to switch among your Optimizely products. See the Opti ID documentationOpti 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.Learn more about Warehouse-Native Experimentation Analytics.