3 minute read
We are thrilled to announce that Twilio has chosen Split as a key component of their experimentation capability to drive faster product decisions.
Developers and businesses use Twilio to make communications relevant and contextual by embedding messaging, voice, and video capabilities directly into their software applications. To be an innovator at the forefront of building API services for developers, Twilio invested in an engineering team that builds many tools in-house, often before a product even exists to address that need within their stack.
Due to rapid growth in 2016, Twilio saw the need for richer product experimentation with accurate and valuable feedback. Twilio started using experimentation as a way to quantitatively improve product decision-making, to make decisions quickly as well as drive key metrics like sales opportunities and product adoption. They were hyper-focused on building out the infrastructure needed to run experiments at scale, yet still searching for a flexible and scalable solution.
As with many internal engineering needs, Twilio evaluated building a targeting engine in-house and reviewed a range of both open source and off-the-shelf tools. As part of this process, they discovered Split. Twilio had some unique requirements but found that Split had the capabilities and flexibility to meet their needs.
As Twilio looked to expand experimentation enterprise-wide, and to more complex use cases, they turned to Split to solve granular targeting and providing necessary data around what variation of a feature a user experienced. Split seamlessly plugged into Twilio’s internal employee dashboard, making it easy to adopt experimentation across the engineering team.
Split was built from the ground up for an engineering team use case. Split’s Feature Experimentation Platform comes with SDKs for eleven different languages. However, what was more important, was that Twilio needed different teams to organize their experiments in Splits within one environment.
Split serves as a key component of Twilio’s experimentation platform, which is used across the engineering organization to run full stack experiments. With Split, Twilio gains an out-of-the-box service for serving experiment treatments, ensuring proper randomization and consistency of user experience, and collecting data on what treatment a user experienced. Split enables Twilio to test features in production, assess the treatment experimentation teams were testing and support experiments on some of Twilio’s toughest internal debates.
Twilio has achieved the following benefits from using Split:
- Ability to run experiments six months faster than if they needed to build the entire platform in-house;
- Ability to utilize multiple teams to organize their experiments within one environment; and
- Flexibility to build a third party feature flagging engine into Twilio’s own internal dashboards and tools to give employees a single user experience and insulation from future changes in underlying platforms.
Read the case study to learn more about how Twilio is using Split.