Integrations
An Optimizely Feature Experimentation decision happens wherever your code runs — in a browser bundle, in a Node or Python service, on the edge — and the SDK that made it is the only thing that knows about it. Getting that decision into the analytics platform, the warehouse or the monitoring tool your team already reads is what turns a flag rollout into a result somebody can act on.
The directory below is every integration this site has written a guide for — one card per article, grouped by what the tool does. Each card says what its guide actually covers, so two articles about the same platform never read as the same thing, and where a tool is covered under Web Experimentation as well the card links across to it.
Most of these guides are built on the same piece: a decision notification listener that fires when the SDK assigns a variation, forwarding the flag key, the variation and the rule to whatever you are instrumenting. Search by product name, by an abbreviation, or by the job you are trying to do, and start with the stack you already run.
Browse 28 Feature Experimentation integrations
Web Experimentation integrations28 integrations
Product & Web Analytics
Adobe Analytics
SDK notification listeners
Integrate Adobe Analytics with Optimizely Feature Experimentation using SDK notification listeners, with client-side and server-side code examples.
Also on Web Experimentation
Amplitude
Amplitude events as conversions
Forward Amplitude events into Optimizely Feature Experimentation as conversions, either from your own application code or with an Amplitude webhook streaming sync.
Also on Web ExperimentationAmplitude
Decision listeners across SDKs
Integrate Amplitude with Optimizely Feature Experimentation via SDK decision listeners in JavaScript, Node.js, and Python, plus Amplitude Audience Sync.
Also on Web Experimentation
Firebase
User properties and events
Forward Optimizely Feature Experimentation decisions into Firebase as user properties and events, on the web and through the Measurement Protocol.
Google Analytics 4
GA4 conversions as experiment metrics
Forward Google Analytics 4 events into Optimizely Feature Experimentation so your existing GA4 conversions become experiment metrics without rebuilding them.
Also on Web ExperimentationGoogle Analytics 4
Notification listeners and GTM
Track Feature Experimentation server-side experiments in Google Analytics 4 with notification listeners and GTM
Also on Web Experimentation
Heap
HTTP Track API from listeners
Integrate Heap with Optimizely Feature Experimentation using SDK decision listeners and the Heap HTTP Track API, with JavaScript, Node.js, and Python code.
Also on Web ExperimentationJune
User and company group traits
Forward Optimizely Feature Experimentation decisions into June as user traits, company group traits and events for account-level B2B analysis.
Localytics
iOS and Android decision tagging
Send Optimizely Feature Experimentation flag decisions to Localytics on iOS and Android to segment mobile analytics and funnels by experiment variation.
Matomo
Visit-scoped custom dimensions
Segment self-hosted Matomo reports by Optimizely variation using visit-scoped custom dimensions and the HTTP Tracking API, with code examples.
Mixpanel
People properties from listeners
Integrate Mixpanel with Optimizely Feature Experimentation using SDK decision listeners in JavaScript, Node.js, and Python, plus user profiles and Cohort Sync.
Also on Web ExperimentationPendo
Visitor metadata from listeners
Integrate Pendo with Optimizely Feature Experimentation using SDK decision listeners in JavaScript, Node.js, and Python to segment Pendo analytics by variation.
Also on Web Experimentation
PostHog
Person properties from listeners
Integrate PostHog with Optimizely Feature Experimentation using SDK decision listeners in JavaScript, Node.js, and Python to segment funnels by variation.
Also on Web ExperimentationSession Replay & Heatmaps
Contentsquare
Dynamic Variables from SDK listeners
Integrate Contentsquare with Optimizely Feature Experimentation using SDK listeners and Dynamic Variables to segment heatmaps and session replays by variation.
Also on Web ExperimentationFullStory
Server SDKs and the V2 Browser API
Integrate FullStory with Optimizely Feature Experimentation using SDK decision listeners, the V2 Browser API, and the Server API for Node.js and Python.
Also on Web Experimentation
Hotjar
Identify and Events APIs
Segment Hotjar heatmaps, recordings and surveys by Optimizely variation using the Identify and Events APIs, with browser and server examples.
Also on Web ExperimentationLogRocket
Replays filtered by variation
Filter LogRocket session replays by Optimizely variation using decision listener traits and events, with browser, Node.js and Python examples.
Tag Management & CDP
Google Tag Manager
dataLayer from decision listeners
Integrate Google Tag Manager with Optimizely Feature Experimentation using SDK decision listeners in JavaScript, Node.js, and Python to route variations to GA4.
Also on Web Experimentation
mParticle
DECISION listeners on mobile and web
Integrate mParticle with Optimizely Feature Experimentation using DECISION listeners across Android, iOS, JavaScript, React, and React Native.
Also on Web Experimentation
Segment
Listeners in JavaScript, Node and Python
Integrate Segment with Optimizely Feature Experimentation using SDK notification listeners in JavaScript, Node.js, and Python to route decisions downstream.
Also on Web Experimentation
Tealium
Server and client SDK listeners
Integrate Tealium with Optimizely Feature Experimentation via SDK decision listeners in JavaScript, Node.js, and Python so variations travel with events.
Also on Web ExperimentationData Warehouse & Pipelines
Amazon Redshift
Firehose and COPY ingestion
Land Optimizely Feature Experimentation decisions in Amazon Redshift with Firehose or COPY, and query revenue per variation from first exposure.
Snowflake or BigQuery
Data sharing, Parquet export and ELT
Analyze Optimizely Feature Experimentation data in Snowflake or BigQuery via native data sharing, S3 Parquet export, or Warehouse-Native Analytics.
Snowplow
Decisions as a validated entity
Attach Optimizely Feature Experimentation decisions to every Snowplow event as a validated entity, with schemas and browser, Node and Python code.
Marketing & CRM
Braze
Custom attributes and events
Send Optimizely Feature Experimentation decisions to Braze as custom attributes and events, using the Web SDK or the users/track REST API.
Monitoring & Error Tracking
Datadog or New Relic
Tag APM traces with flag decisions
Tag Datadog or New Relic APM traces with Optimizely Feature Experimentation flag decisions to monitor latency, errors, and throughput by variation.
Sentry
Decisions as tags and breadcrumbs
Forward Optimizely Feature Experimentation decisions into Sentry as tags, context and breadcrumbs, with browser, Node.js and Python listeners.
Other Integrations
Zapier
Webhooks without burning task quota
Send Optimizely Feature Experimentation decisions to a Zapier webhook without burning your task quota, with Node.js and Python listeners.
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