100s of teams are constantly building 1000s of features at any given time — and even at that scale, Deltoid would diligently track statistical movements between test and control groups, against the company’s core metrics (like DAU, MAU, user retention, engagement, time-spent, transactions, revenue), a team’s most relevant metrics (page views, shares, interactions, time-spent), and key hypothesis and guardrail metrics.
Every engineer, data scientist, product manager, designer, researcher, and even business folks have at some point stared at a Deltoid chart and made important product decisions.
Today with Pulse, we’re bringing the same power of data-driven decision-making to everyone.
Today with Pulse, we’re bringing the same power of data-driven decision-making to everyone. For every feature you’re building behind a Feature Gate, you can now see how it is performing along with how it is affecting your company’s critical metrics.
For instance, in the picture above, a new feature was opened to 10% of user traffic. The horizontal bars in the graph indicate confidence intervals. They start out gray, but once a particular metric gathers statistical significance, it turns either red or green depending on if it’s on the negative or positive side of the axis.
In a single glance you can tell whether or not a feature is ship-worthy. In this example, the new feature is negatively affecting the product_view and product_details metrics while no other metrics show a statistically clear lift; it isn’t wise to ship this as it is. As you improve the feature, your experimental metrics will begin moving to the right, and as you increase exposure, the confidence intervals will narrow down. When the metrics show a lift with tight enough confidence intervals, you’ll achieve statistical significance providing assurance that this feature is ship-worthy.
If you want to see it in action in regard to your application, grab a demo!
Kong is our Typescript-based write-once-run on every SDK framework. “Write once, run anywhere” is always a dream for programmers, and now we have just that!
LaunchDarkly was mandatory for every new feature in Motion’s backend, web app, and Chrome extension. "It was obvious this was a huge mistake."
Last Tuesday, Statsig brought a cadre of data science and experimentation fans together at a loft space in San Francisco for the first-ever Data Science Meetup.
Well-designed experimentation is the first step in creating a rollout structure that consistently delivers optimal results—whatever they may be.
Using data and experimentation, the Obama 2012 campaign generated over one billion dollars in donations, nearly $700,000,000 of which were online.
It’s only my first week yet, but each day I am more and more impressed by the team’s velocity, excitement, and transparency, and feeling more sure that I’ve made the right decision for /me/.
Explore Statsig’s smart feature gates with built-in A/B tests, or create an account instantly and start optimizing your web and mobile applications. You can also schedule a live demo or chat with us to design a custom package for your business.