Feature flag data flow

Unlock feature-level insights with data

Split pairs feature flags with data. Analyze the impact of every feature on hundreds of business, product, and operational metrics.

Import data from your data pipeline to unlock alerting and experimentation. Export data to further analyze in a data warehouse or BI tool.

Quickly connect to existing data pipelines

Split ingests events from a wide variety of data sources and matches them to your feature flag data. Split automatically measures the impact of every feature that you release.

Import event data from your favorite tools. Split has integrations out of the box for Segment, mParticle, Sentry, and Google Analytics.

Want to connect other data sources? Track events from your app using our SDKs or send them via API.

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Defining a metric

Easily customize to your business objectives

Traffic types define the unique IDs you care about. Users? Accounts? Patients? Logged-in shoppers? We’ll map to what matters in your app.

Event properties

Measure once, match to any KPI

Metrics aggregate raw event streams. Define them to meet your exact definitions — sum, count, ratio, percent, average, per user, and more.

Metric properties give you even more control. Collect an event once that includes multiple attributes and then create precisely tailored metrics.

Count of checkout events where the purchase was a meal kit? Done.

Enrich external data sources

Our SDKs generate an impression each time a feature variant is exposed to a user, which you can use to run an email campaign or analyze customer cohorts.

Easily export impressions to destinations such as Segment, mParticle, and Google Analytics.

Want to use other systems? Our impression listener can forward data anywhere.

Create a culture of data-driven development

Imperfect Foods powers their feature release and experimentation strategy with Split and data pipeline integrations.

Connect data to your features in a click

Improve engineering efficiency and empower teams to solve customer problems by measuring feature-level impact.