Experimentation

Experimentation, as we see it.

When powered by feature flags, experiments and A/B tests can be easier for any team to run, interpret, and act upon.

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Why traditional experimentation initiatives fail

Docs: Experimentation

Wildly successful companies practice large-scale experimentation. But they’re the exception. Most organizations struggle to run enough experiments for it to matter. And they seldom get much value from the few experiments they do run. The promise of experimentation eludes the majority. Why?

Traditional experimentation tools are isolated from the development workflow. What’s more, they often require advanced knowledge of statistics to operate. Lastly, many tools take too long to yield meaningful results. All of this leads to bottlenecks, fewer experiments, and less actionable data.

Improve business value

Actionable experimentation

LaunchDarkly Experimentation empowers more teams to run more experiments and get more out of them.

Run more experiments

In tying experiments to feature flags, LaunchDarkly lets you run experiments in any environment and any layer of your stack: front-end, back-end, mobile. Easily turn flagged features into experiments.

Get better answers in less time

Through our Bayesian approach to experimentation, LaunchDarkly delivers useful, digestible data on every single experiment. No more waiting for statistical significance or input from an expert.

Allow any team to run valid experiments

We believe anyone should be able to set up and run valid experiments. LaunchDarkly offers an intuitive experiment builder, user-friendly dashboards, and guardrails for testing in production.

Unite feature flags and experiments

When you identify a winning feature from an experiment, roll it out with one click. No extra engineering work required. LaunchDarkly supports feature flags and experiments in a single implementation.

Case study

CCP Games creates self-serve experimentation.

CCP Games delivers and controls features in production while running experiments in one seamless workflow.

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LaunchDarkly has enabled self-serve experimentation. You don’t have to be a data scientist to run valid, actionable experiments. This is unbelievably powerful.

Nick Herring

Technical Director of Infrastructure, CCP Games

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Case study

Ritual delivers customer experiences with experimentation.

With feature flags and experiments in one place, the product delivery team is more agile, data-driven, and collaborative.

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The main benefit of using LaunchDarkly experimentation is that it’s directly tied to the feature flag implementation. It’s not a separate implementation every time we want to test a feature.

Daniel Archer

VP of Engineering, Ritual

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Case study

Loom runs more experiments, increases product engagement.

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Our old approach to experimentation was too complicated from an engineering perspective. It ate up developer time that would have been better spent on building actual features, not writing custom targeting rules.