How We Compare

Our A/B test analysis package is designed to go beyond standard metrics like conversion rates and retention rates. Unlike many existing tools:

• Google Optimize – focused mainly on running and monitoring tests, but offered little insight into feature-level or interaction effects.
• Optimizely & VWO – provide strong experiment dashboards, but insights are often limited to aggregate differences between groups.
• Statsmodels / CausalML – open-source libraries that require heavy coding effort and don’t automate reporting for non-technical users.

In contrast, our package automatically:

• Reshapes continuous features into bins for more interpretable analysis.
• Provides both main effects and interaction effects, so you see not just “what works,” but also “what works best together.”
• Generates ready-to-use reports with coefficients, p-values, and confidence intervals.

This means you get the best of both worlds: the automation and clarity of a SaaS testing platform, combined with the rigor and flexibility of statistical modeling.