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.

• Learn more about conversion rate optimization

• Read about A/B testing

• Explore Statsig's A/B testing calculator

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.

• Check out Statsig's documentation

• Learn about A/A testing

• Explore Statsig's integrations