Savage X Fenty keeps shoppers engaged with rapid, reliable experiments.
LaunchDarkly gave the business teams the confidence that experiments could be run reliably and the data could be trusted.
Data science & experimentation
Run experiments you can actually trust.
Built for teams that treat experimentation as infrastructure, not a side process—with the statistical methods, metric governance, and scale that serious programs depend on.
Experiment confidently
Build an experimentation program your team and leadership can depend on.
Trust your results, not just your instincts. Statistical methods that match what top-tier experimentation teams use are built into the platform, so your team doesn't have to implement them from scratch.
Get better supported results faster with variance reduction.
Use CUPED to significantly cut experiment runtime without sacrificing statistical power.
Don’t worry about peeking.
Sequential testing lets you monitor results in real time.
Catch your experiment health issues automatically.
Sample ratio mismatch (SRM) detection flags assignment problems before they may impact your results.
Be the authority on what gets measured.
Define primary, secondary, and guardrail metrics centrally—and know that every team running experiments is measuring against the same definitions you set, not improvising their own.
Connect directly to your source-of-truth data.
Native integrations with Snowflake, BigQuery, Redshift, and Databricks mean experiment results reflect the exact numbers your data team and leadership already trust.
Reduce metric drift.
Avoid duplicate definitions, shadow metrics, or "which number is right" conversations after results come in. One definition, used consistently across every experiment.
Define metrics
Own your metrics from definition to decision.
Define what good looks like. LaunchDarkly helps make sure those definitions are the ones actually used in every experiment, and when configured, pulled directly from your warehouse, rather than approximated by a vendor pipeline.
Scale experimentation
Scale the program without sacrificing rigor.
Give product and engineering teams the ability to run their own experiments with the guardrails, governance, and methodology standards your team sets.
Demonstrate the program's business value, not just individual experiment results.
Cross-experiment analysis and holdout groups let you show leadership what the experimentation practice itself is delivering—not just whether a single feature worked.
Run experiments in parallel with controls to reduce interference.
Mutual exclusion and holdout groups let you scale the program without experiment-to-experiment contamination.
Establish true baselines.
Holdout groups measure long-term impact and separate experiment effects from underlying trends, so your recommendations hold up to scrutiny.
Govern experiments
Catch bad experiments
before they cost you a cycle.
Gate experiments with a formal review workflow, with built-in approval workflows purpose-built for experimentation governance.
Review and approve experiments before they run.
Experiment Approvals give data scientists a gate on metric selection, experiment configuration, and targeting rules, so there’s less risk that experiments measure the wrong thing.
Prevent bad experiments from producing misleading results.
Catch underpowered experiments, misconfigured metrics, and overlapping populations before launch, not after analysis.
Extend governance to AI features.
Apply the same approval workflow to AI prompt and model experiments, so agentic behavior changes get the same rigor as any other experiment.
Resources to get started.
Get started today
