Feature Management & Experimentation (FME)
The Harness Feature Management & Experimentation (FME) module combines feature flags with experiment analysis to help product teams make data-driven decisions.Core Features
Experiment Management
- A/B Testing: Compare the effects of different feature versions
- Multivariate Testing: Test multiple variable combinations simultaneously
- Segmented Users: Conduct experiments for specific user groups
Analytics Capabilities
- Real-Time Metrics: View real-time data during experiments
- Statistical Analysis: Automatically calculate statistical significance
- Anomaly Detection: Identify anomalies in data
Decision Support
- Recommendation Engine: Provide feature release suggestions based on experiment data
- Confidence Intervals: Show the reliability of results
- Sample Calculator: Determine minimum sample size needed for statistical significance
Relationship with Feature Flags
FME is built on Feature Flags with advanced analytics capabilities:| Feature | Feature Flags | FME |
|---|---|---|
| Flag Management | ✅ | ✅ |
| Targeting Rules | ✅ | ✅ |
| A/B Testing | - | ✅ |
| Statistical Analysis | - | ✅ |
| Product Experiments | - | ✅ |
Use Cases
| Scenario | Description |
|---|---|
| Conversion Rate Optimization | Test different button text, colors on conversion rate |
| UX Experiments | Compare different interaction design approaches |
| Pricing Strategy | Test different pricing scheme effects |
| Recommendation Algorithms | Evaluate different recommendation algorithm performance |
Getting Started
1. Create Experiment
Create a new experiment in Harness FME, defining experiment goals and metrics.2. Configure Variants
Define different versions of the experiment (control and treatment groups).3. Set Traffic Allocation
Decide what percentage of users will participate in the experiment.4. Integrate SDK
Implement user grouping and event tracking using the FME SDK.5. Analyze Results
After the experiment runs for a period, analyze the collected data to make decisions.Data Privacy
FME follows data privacy best practices:- Anonymization: User data is anonymized
- Compliance Support: GDPR, CCPA, and other privacy regulations
- Data Isolation: Ensure complete isolation of different tenant data