Experimentation that Scales to Every Product and Engineering Team

With Split by Harness, feature flags become an experiment without added toil or competition for scarce data science resources, so every team is freed up to innovate. Scale is only limited by your imagination, not your experimentation specialist headcount.

Measure Every Release with Precision

Harness taps into data from SDKs, APIs, and integrations like Segment, mParticle, and Sentry, offering continuous, real-time insights. It’s likely you will use Split by Harness’s patented attribution engine first to monitor each rollout closely and empower your team to make informed, impactful decisions effortlessly. It’s the first step towards being data driven at the feature level.

Diagram showing two user groups feeding into blue and pink systems labeled A and B, which connect via SDK and API to services including Amazon S3, Google Analytics, mParticle, Twilio Segment, and Sentry, leading to a graph titled Feature Observability tracking sign ups.
Side-by-side mobile login screens showing A/B test variants with different button layouts and welcome messages.

A/B Tests Without Extra Setup

Once you are using Split by Harness for release monitoring, the groundwork for experimentation is already laid.  From there, you can conduct A/B tests using the same self-service tool without the need for extra resources or added complexity.

Centralize Experiments to a Single Source of Truth

Achieve seamless consistency and visibility with a unified source for all metric definitions and result calculations. Eliminate fragmented islands with competing methodologies and levels of rigor. Harness centralizes notifications for metric impacts, review periods, and change requests, empowering your team with streamlined, efficient collaboration.

Dashboard showing 'My Work' section with experiments ready for review and one outstanding approval, alongside a 'Metrics' panel listing account-related key performance indicators and error responses.
Bar chart comparing fixed horizon testing and sequential testing timelines, showing sequential testing finds statistical impact sooner at day 11, while fixed horizon testing review ends at day 30.

Swift Decision-Making With Sequential Testing

Sequential Testing empowers engineering teams to quickly finalize release decisions upon detecting significant impacts. This reliable statistical method ensures consistently valid results, allowing you to assess progress at any point and expedite development processes.

Detect Small Movements With Fixed Horizon Testing

Split by Harness enables you to detect even the slightest movements in your experimentation results. For key business metrics, like conversion or revenue per transaction, these small changes can have a major impact on business. 

Real estate webpage split in two, left side with search filters and blurred apartment background, right side with search filters and illustration of a house, both showing statistics below and a central graph titled 'Conversion Rate'.
Chart titled Select a dimension with a dropdown for Devices, showing impact percentages with error bars for Overall, Android, Desktop/Chrome, Desktop/Edge, and iOS; a tooltip highlights iOS impact at 24.36% with a range between 23.85% and 24.88% as of August 2023, 11:13 AM.

Unlock Clarity With Dimensional Analysis

Transform ambiguity into actionable insights. With Dimensional Analysis, assign dimensions to your metrics for deeper context and clarity. Break down data by properties like country or device type, ensuring every experiment fuels future innovation. No effort is wasted—just repurposed for greater success.

Share Insights, Align Stakeholders

Instantly grasp your results with Harness’s Metric Impact Cards for a quick overview or dive deeper using the Metric Details Chart. Share insights in seconds to keep your team and stakeholders on the same page, ensuring everyone is ready for the next strategic move. Empower collaboration effortlessly!

Line graph showing total booking dollars per user from April 11 to April 17 with treatments On and Off; On treatment increases from around 150 to over 225 while Off remains near 150. Impact increase is shown as 25.3%.
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“Split makes it possible to really understand how our users respond to the changes we make. Now it’s easy to know the best change for our users and how we can help them satisfy their creative journey.”

Jean Steiner, Ph.D., VP of Data Science
Feature Management & Experimentation