An experimentation platform is a system used to design, run, and analyze experiments such as A/B tests, feature rollouts, and controlled trials. It allows organizations to test changes scientifically rather than relying on intuition.

Experimentation platforms handle:

• User segmentation and traffic allocation

• Variant assignment

• Metric tracking

• Statistical analysis

• Experiment monitoring

Examples include tools for product experiments, marketing tests, pricing tests, and feature flags.

From a BI perspective, experimentation platforms integrate closely with analytics systems. Metrics used in experiments must align with core business definitions to avoid misleading results.

Technically, experimentation platforms rely on:

• Randomization logic

• Event tracking

• Statistical testing frameworks

• Feature flagging systems

Well-run experimentation helps teams:

• Reduce risk

• Validate assumptions

• Improve conversion rates

• Optimize user experience

A common mistake is running experiments without enough data or stopping them too early. Another issue is testing too many changes at once, which makes results hard to interpret.

Experimentation platforms turn analytics into a decision engine, enabling continuous improvement through evidence rather than opinion.