"A/B testing is a side effect of controlled release. That’s what I love most about Split."

"The system is very reliable, it has a good number of integrations already in place and works nicely with CDPs. The support team is amazing and they answer questions in a very timely manner. Also very helpful are the governance tools. The python Admin API has also been very helpful …

"At the point engineers make deployments live, they want to understand how the features are performing, from a customer experience, user value, and system standpoint. We know that one of the big challenges developers face in understanding impact is how to get all of the data in the first place. …

"It’s inherently collaborative and transparent, a rigorous and sound process based on the hard data we know about."

"Our team uses Split feature flags to deploy quickly and experiment with new ideas. Feature Monitoring gives us a safe way to release features to our global user base with less risk."

“We had the intention to change the password field, but we didn’t have a way to know how good it was effectively, and to be able to control it in-house, within our product team. Split allowed us to do all of that and more.”

“Split has allowed Vida to test our mobile application in a way we wouldn’t be able to do otherwise.”

"Split’s full-stack experimentation platform has been built with engineering and product teams in mind. Its robust architecture and rich feature set integrates into our internal platforms and helps power experimentation across our entire engineering organization."

"Thanks to Split, we were able to learn that the risky version actually had the highest adoption by our customers while keeping overall signup conversion steady."

"One of our first experiments was a great learning experience. It prevented us from introducing something that we thought was good, but actually would have likely given us a negative impact. We’re now doing another iteration to get it right."

"We use feature flags and run experiments a lot more because it’s easier now with Split. We can target a random sample size. We can increment the size of that sample really easily, and we can toggle it off immediately if there’s an issue."

"Using Split with FullStory allowed us to humanize our experiments and empathize with our customers by validating the quantitative results with real user sessions that revealed how customers felt when interacting with our features."

“If we built this in-house, most engineers would look at this and say ‘feature rollout, it’s not that hard’. But when you think about having multiple environments, having a UI, conditional filters, an impression data pipeline, all that—it’s a whole team working for a very long time to be able …

“By decoupling experimentation decision-making from engineering Resources [with Dynamic Configuration], product teams become empowered to make fast and flexible changes to the product with little to no technical knowledge.”

“The integration with Amazon S3 helps us track all the events that happen offline. In our case, an example is someone investing money on our own bank account. That cannot be tracked through Segment, but we obviously aware of when someone sends us the money. An integration with Amazon S3 …