Design an Experiment to Test a New Checkout Flow

I would approach this by first establishing a hypothesis: 'Simplifying the guest checkout form from five fields to three by auto-detecting address details will increase conversion rates by at least 2% without negatively impacting fraud detection.' For the primary success metric, I'd focus on Checkout Completion Rate, while monitoring 'Order Value' and 'Fraud Flag Rate' as critical guardrails to ensure we aren't losing high-value transactions or increasing risk. Regarding segmentation, I would run a standard A/B test where 50% of traffic sees the current flow and 50% sees the new one, stratified by device type since mobile conversion is often lower. To determine duration, I wouldn't pick an arbitrary date; instead, I'd calculate the sample size needed to achieve 95% confidence and 80% power, likely requiring a two-week run to account for weekly purchase cycles. Finally, I'd plan to analyze the results by looking at the p-value for the conversion rate difference and segmenting performance by geography to ensure the new flow works globally, aligning with Microsoft's commitment to inclusive design. If the data shows a lift in conversion but a spike in support tickets regarding address errors, I would recommend a rollback despite the positive revenue signal.