We solemnly promise that each bit of content you read here
is thoughtfully curated or crafted by a human, for a human. We spend hundreds of hours every month speaking to experts with decades of experience. Our blogs are more a journalistic report on the state of experimentation than a single perspective article. Enjoy!
An autonomous experimentation loop automatically identifies active tests, reads their results, and acts within limits you set in advance. Maintain security by granting the loop least-privilege credentials, such as read-only access, to limit risk. Ensure statistical rigor using sequential testing and enforcing minimum runtimes to prevent acting on random noise....
Feature flags let you deploy code hidden behind a toggle, then turn it on for specific users without a redeploy. Rollouts use the same flag to expand access step by step. Developers use flags for internal testing, percentage-based rollouts, and A/B testing against a control group. Feature flag and rollout...
Feature flags switch functionality on or off at runtime, without a deploy. Rollouts widen an already-approved feature to a growing percentage of users. Feature testing compares variations against a control to find which performs better, using A/B/n testing underneath. All three run on feature flags. Only feature testing holds back...
How often do your biggest product decisions go untested because full-stack feels too costly to spin up? Pricing logic, eligibility rules, feature access, rollout behavior. These decisions live deep in the stack, compound quickly, but they rarely get the same attention as surface-level changes. Meet Convert’s new full-stack testing templates....
QA in client- and server-side experiments ensures accurate, reliable results. 12 experts weigh in on how to streamline QA for flawless execution in a full-stack approach. Key points: A holistic approach to QA ensures every layer of your experiment, from the user interface to backend data tracking, functions as expected....
Feature testing enables you to optimize features by comparing variations, gathering user feedback, and making data-driven decisions on which versions to deploy. Key points: Feature experimentation, also called feature testing, compares variations of a feature with real users to find which performs best against a chosen metric. Convert Experiences is...
