9 Best Practices for Experimenting Using Feature Flags

Experimentation holds a world of possibilities from uncovering user behaviors to validating ideas, and so much more. Managing experiments at scale, however, can quickly become overwhelming, which can impact the quality of the data you receive. Learn how to configure an experiment from any feature flag, the best practices of making a good experiment, and how experiments bridge the gaps between technical and business teams in our latest whitepaper.

• Creating a culture of experimentation
  How do you instill a culture of asking questions, soliciting ideas from all team members, and making data-driven decisions part of the organization’s makeup?
• Defining what experimentation success looks like as a team
  How do you define what “good enough” is and who is involved in creating the definition? Experiments can involve a cross-functional team of people or only a handful, depending on the focus of the experiment
• Defining and using proper segmentation
  How will you segment your users, and second, how will you segment the data?

You'll learn how to improve experiments:

More powerful experiments start here

Companies across the globe and all industries are turning to experimentation to give them compelling data and immediate, actionable insights to guide their development cycles. But setting up, running, and interpreting good experiments can be challenging—and poor experiments can directly impact your bottom line. We've put together nine best practices in our latest whitepaper to help your team get the most out of the experiments they run.

LaunchDarkly lets us make data-driven product decisions, and we’re seeing higher levels of engagement with new features we ship.

Janie Lee

Director of Product Management, Loom

Better feature management
starts today.

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