I conducted an A/B test for an e-commerce brand to evaluate how different pricing strategies impacted user conversion rates. Two price points were tested for a high-demand product across different audience segments. The test was designed to understand customer price sensitivity and optimize revenue.

Data was collected via Google Analytics and CRM tools, then cleaned and structured for analysis in R. I performed hypothesis testing using t-tests and confidence intervals to determine statistical significance. I also segmented the data by user demographics and acquisition channels to uncover deeper patterns.

The results revealed that a 10% price drop led to a 22% increase in conversion rate among returning users but had no significant effect on first-time visitors. Based on these insights, I recommended implementing personalized pricing strategies and loyalty-based offers.

Skills Gained:

• A/B testing design & analysis

• Hypothesis testing (t-tests, p-values)

• Experiment segmentation & cohort analysis

• R for statistical computing

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