Experiment Design & Analysis
Product A/B Testing Analytics Framework
A/B testing analytics framework for measuring product experiment impact with statistical significance testing and KPI tracking.
PythonSQLApache SupersetStatistics
10+
Experiments
A/B Test
Method
95%
Confidence
Problem Statement
Product experiments lacked rigorous statistical analysis, making it difficult to determine whether changes truly improved key metrics.
Architecture
SQL experiment assignment tracking, Python statistical analysis pipeline, Superset dashboards for experiment result visualization.
Database
SQL database with experiment assignments, events, and conversion data.
Solution
Built an A/B testing analytics framework with experiment tracking, statistical significance calculation, and visual impact reporting.
Key Queries
- Experiment group assignment queries
- Conversion rate comparison queries
- Statistical significance calculation queries
Dashboards Built
- Experiment results dashboard
- Statistical significance panel
- KPI impact comparison view
Business Insights
Data-backed product experiment decisions
Reduced risk of shipping harmful changes
Accelerated product optimization cycles
Challenges
- Ensuring proper experiment randomization
- Calculating statistical significance correctly
- Handling multiple concurrent experiments
Future Scope
- Multi-variate testing support
- Automated experiment recommendations
- Bayesian analysis integration
Features
Experiment tracking and monitoringStatistical significance testingKPI impact visualizationExperiment history archive