Sophie Harpur

Data Hub combines our impressions view with new capabilities like data export and a single dashboard to view, query, and export feature flag and event data.

Become more data-driven around feature releases by using impression data from Split in mParticle and mParticle event data to evaluate treatments in Split.

Use measures of impact, absolute value, and error margins to analyze whether an A/B test was a success.

Discover how to respond swiftly to metric degradations with alert policies. Learn how Split.io’s feature flagging and experimentation platform enables businesses to set up proactive alerts for timely issue detection and resolution. Uncover the benefits of data-driven insights and efficient monitoring in optimizing your software performance. Read our blog now to implement alert policies and ensure your metrics remain at peak performance for a seamless user experience.

With today’s release, you can now create a range of metrics from a single event, providing a deeper analysis of the results of your experiments. These additional insights into knowing how people are using your application is vital to shaping… Read more

Split’s goal is to power the world’s product decisions, so we are always looking for new ways to enable our customers to be more data-driven. We believe in the power of metrics and strive to make sure our users have… Read more

In this post, we will talk about key experimentation concepts including how to choose your Overall Evaluation Criteria (OEC) for your experiments and how to increase the sensitivity of those metrics through metric filtering and metric capping.

At Split, we are always improving how we can help our customers make these decisions more efficiently across the full application stack. In this blog, I will discuss best practices to achieve statistically significant results in your experiments and how Split can help you accomplish this.