A/B Testing Analysis
for Conversion Rate Optimization
An e-commerce company saw conversion rates declining in international markets. A new localized version of the site was developed — this analysis measures whether it actually worked, using proper statistical methods.
View full notebook →Conversion rates were dropping in specific international markets. The company developed a localized version of the site adapting content and design to regional preferences — but without data, there's no way to know if it actually moved the needle or if the change was just noise.
Designed a proper A/B test framework to compare the original site against the localized version. The analysis goes beyond just comparing averages — it applies statistical significance testing to determine whether the observed differences are real or random, and calculates the practical impact on revenue.
Most teams stop at "version B had a higher conversion rate." This analysis goes further — testing whether that difference is statistically significant before recommending any action.
Adapting content and design to regional markets isn't just a UX decision — it's a revenue decision. The data shows exactly how much impact localization had on conversion rates.
Even a 1-2% improvement in conversion rate can represent significant revenue at scale. The analysis quantifies this impact in business terms, not just statistical ones.