Product Experiment

Understanding product experimentation

Product experimentation is a systematic and data-driven approach to testing. It helps you make informed decisions about your product features. By using data, you can reduce uncertainties and avoid costly mistakes.

The main goal of product experimentation is to validate features before full development. This means you test whether a feature will be successful before investing significant resources. It helps you focus on what truly matters to your users. For instance, you might test a new interface layout with a small group of users before a full rollout.

Controlled experiments, such as A/B testing, are essential tools in this process. These experiments allow you to compare different versions of a feature to see which works better. You can measure specific metrics like click-through rates or user engagement. Here's how you might use A/B testing:

  • Version A: Current layout of a webpage

  • Version B: New layout with redesigned navigation

By systematically comparing these versions, you gather actionable insights. This approach ensures that your decisions are based on evidence rather than assumptions. Product experimentation not only improves feature development but also enhances overall user experience.

Examples of product experimentation

Test different button colors to see which drives more clicks. This simple change can reveal user preferences quickly. Learn more about Bucket Testing and how it can help you understand user behavior.

Validate a new feature by testing it with a small user segment first. This minimizes risk and gathers initial feedback. Read about Creating Your First Experiment for step-by-step guidance.

Use A/B testing to compare two versions of a landing page. Identify which design performs better in terms of user engagement. For a deeper dive, check out Landing Page Experiments.

  • Button colors: Test various shades to find the most effective. Learn how to set up tests with the A/B Testing Calculator.

  • Feature validation: Roll out new features to a select group. Get started with Walkthrough Guides.

  • A/B testing: Compare and contrast landing page designs. Understand more about Split Testing.

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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
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