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Optimizely has been innovating in Experimentation since 2010 - and we haven’t slowed down, with some significant acquisitions in the customer data and digital asset management space in the last 24 months, and a huge amount of activity behind the scenes to bring key features across the ecosystem to Feature Experimentation customers.

We are very excited to announce deeper integrations – leveraging the advanced workflow and collaboration properties of Content Marketing Platform (CMP) for enhanced experimentation collaboration, and the powerful personalization and real-time segmentation capabilities of our customer data platform for more targeting and personalization capabilities with less developer overhead in Feature Experimentation. 

These advanced personalization and collaboration tools are a step change in the scope of what can be achieved with experimentation – leading to better optimized personalization and more streamlined workflows for growing experimentation teams. 

Advanced audience targeting  

A step change in personalization optimization.

  • Customer data platform (CDP) connector - for customers with an existing third-party CDP who wish to align their existing audience segmentation with Feature Experimentation.
  • Full customer data plaform (CDP) – This feature add-on brings Optimizely’s native Data Platform into Feature Experimentation in a seamless integration designed to provide all the tools you need to experiment and release to highly customizable audiences and real-time segments. 

Advanced audience targeting is currently in beta and scheduled for general release at the end of June. If you're interested, speak to your Customer Success Manager to get access to the beta. 

What you can use advanced audience targeting for

Release features or experiments to targeted groups i.e beta testing, geo-targeted, transaction criteria such as spent per period and frequency of spend.   

Real-time segments to feature personalization – Personalise specific features to specific user groups, automatically identified even if the user is not in a logged-in state. A powerful tool for customers looking to leverage personalization dynamically, with the power to experiment on the fly. Examples include automatically turning certain features on/off per geo-location or customer profile group such as motorbike owner/car owner, male/female, SINK/DINK/Family. 

When advanced audience targeting will be available

  • Private beta – until April 23’ – Speak to your Customer Success Manager for access to the beta.
  • General Availability – June 1, 2023 

Editing audiences in Feature Experimentation using Data Platform audiences

Using multiple experiments on a single feature flag 

With multiple experiments and deliveries per flag, you can seamlessly run experiments, gain valuable insights, and roll out winning variants – all without needing a developer after the flag is implemented and tested. 

Setting rules for development

  • Run more than one experiment and one targeted delivery at a time
  • Release multiple versions of a feature to different audiences at the time same, all using the same flag. 
  • Supercharge your ability to personalize features at scale

Schedule and plan feature flags

Scheduling a feature flag

With multiple teams involved from marketing, product and engineering – orchestrating new releases can be a challenge to execute and often leaves people responsible for making manual updates and inconvenient times.  

  • Plan and schedule features to be rolled out (and back) over time
  • Time when a feature should launch
  • Schedule traffic allocation percentages
  • Create rules for access to features only within set times

Disable public access to data files with secure environments 

We’ve long offered authenticated datafile access via our REST API, and we now offer additional security with the ability to disable public access to datafiles on our CDN.  

Secure environments enable Feature Experimentation customers to protect their datafiles from unauthorized access. This feature enables server-side SDK implementations to make end-to-end authenticated API calls to fetch data files securely and disables public access to datafiles for added privacy.  

How to set up secure environments for Feature Experimentation.