top of page

Web Experimentation with Python:
The Science of CRO

A/B testing is a cornerstone of modern digital marketing and product development. This guide introduces a robust, Python-powered experimentation framework that helps you make data-driven decisions and achieve measurable growth.

Why Python for CRO?

Python's extensive data science ecosystem makes it ideal for running statistically valid experiments:

  • Libraries like scipy.stats provide accurate hypothesis testing

  • Frameworks like Django/Flask allow for fast deployment of test variants

  • Scripting tools and feature flags streamline rollout control and iteration

Key Takeaways

By the end of this guide, you’ll understand how to:

  • Design statistically sound experiments using Jupyter notebooks and scipy.stats

  • Build A/B testing workflows that integrate easily with your existing Python web apps

  • Deploy experiments at scale using feature flags and rollout scripts

  • Track, analyze, and visualize results for clear business impact

Real-World Applications

  • Landing page testing for SaaS products

  • Feature adoption analysis in apps

  • Conversion rate optimization in eCommerce

bottom of page