Data Experimentation

Introduction to data experimentation

What is data experimentation?

Data experimentation is the practice of using controlled experiments to make data-driven decisions. These experiments often involve A/B testing, multivariate testing, and other statistical methods. By conducting these tests, you can uncover which changes will lead to better outcomes.

In A/B testing, you compare two versions of a feature to see which one performs better. Multivariate testing, on the other hand, looks at multiple variations simultaneously. Both methods help in making informed choices based on real user data.

Why is data experimentation important?

Data experimentation is crucial for several reasons. It helps you validate hypotheses by providing concrete evidence. Instead of guessing, you get to see what actually works.

This practice also allows you to measure changes in user behavior. For example, you can see how a new feature impacts engagement or conversion rates. Such insights are invaluable for making data-backed decisions.

Moreover, data experimentation informs product development and business strategies. By knowing what works and what doesn’t, you can prioritize features that add real value. This leads to better products and more effective business plans.

  • Validates hypotheses

  • Measures changes in user behavior

  • Informs product development and business strategies

Examples of data experimentation

Improving user sign-up flow

Test several sign-up form versions to identify the one with the highest conversion rate. Measure each version's completion rate and abandonment rate. Focus on the form that performs best.

Enhancing page load times

A/B test different content delivery networks (CDNs). Measure changes in page load time and user retention. Use the CDN that improves performance the most.

Join the #1 experimentation community

Connect with like-minded product leaders, data scientists, and engineers to share the latest in product experimentation.

Try Statsig Today

Get started for free. Add your whole team!

Why the best build with us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
Ancestry Ancestry
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Brex
Karandeep Anand
President
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Notion
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
SoundCloud
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Ancestry
Partha Sarathi
Director of Engineering
We use cookies to ensure you get the best experience on our website.
Privacy Policy