Amplitude Announces Experiment Results to Help Every Company Make the Right Product Decisions at Scale

New offering allows organizations to pair homegrown and third-party feature flagging systems with Amplitude Experiment, Amplitude’s world-class experimentation platform

Amplitude, Inc., the pioneer in digital optimization, announced the launch of Experiment Results, a new feature of Amplitude Experiment. Experiment Results empowers teams to plan, track, and analyze experiments to make smarter decisions while using their existing feature flagging system. Product and data science teams can now dramatically scale experimentation with Amplitude’s powerful self-service experimentation analysis built on the industry’s #1 ranked product analytics solution. Experiment Results delivers the data and analytics for teams to set goals, determine if experiments reach statistical significance and achieve their goals, and automatically provides recommended next steps.

“How do our digital products drive our business?”

The top product teams test all new features – often running tens of thousands of experiments to understand the impact of each product change. The biggest barrier to scaling product experimentation is having data and analytics to analyze the results of experiments. Many companies already have the infrastructure to deliver new experiments, but rely heavily on data science teams and homegrown infrastructures to formulate the results and provide recommendations on what to do next. Experiment Results removes those bottlenecks with self-service experimentation so that more teams can run more experiments quickly and efficiently. Starting today, companies can pair their existing feature flagging infrastructure with Amplitude’s sophisticated experimentation analysis capabilities to inform product decisions, accelerate innovation, and drive revenue.

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“Winning product leaders use data-driven insights to determine what product changes to make. With Experiment Results, customers can exponentially increase the number of experiments they run, and get recommended next steps on their experiments,” said Justin Bauer, chief product officer at Amplitude. “This means non-technical teams can make smarter decisions faster at scale and ultimately deliver the right features that drive value for their customers and growth for their organizations.”

Key innovations include:

• Multi-Metric Causal Analysis: Experiment Results helps teams determine the causal, statistically significant outcomes of experiments, like identifying if users increased their average order spend due to the product change. It reduces the effort to define metrics, analyze data, and present results, supporting both sequential testing and t-test analysis within the Amplitude Experiment product.
• Downstream Analysis for Experiments: With predefined metrics and pre-populated charts, Experiment Results moves teams from experimentation to decision-making stages faster. Amplitude’s easy-to-understand dashboards enable cross-functional leaders to quickly share experiment results for accessible downstream analysis.
• Automated Goals and Recommendations: Experiment Results also guides users to set the right goals, automatically uncovers whether tests are statistically significant, and provides recommended next steps in a quick snapshot.

Experiment Results allows product and data scientists to scale their analysis services while empowering non-technical teams to interpret results.