# Executive Summary: One-Click Checkout Experiment

## Recommendation: SHIP

The 28-day randomized experiment included **50,000 users**. Treatment conversion was **15.94%** versus **14.20%** in control, an absolute lift of **1.74%** (95% CI **[1.12%, 2.37%]**, p=0.0000). This is a **12.3% relative lift**.

Revenue per user moved from **$10.71** to **$11.87** (difference **$1.17**, bootstrap 95% CI **[$0.62, $1.74]**). Refund rate among buyers was **6.10%** versus **5.97%** (difference **0.12%**, p=0.821), with no statistically significant deterioration.

## Decision Rationale

- Randomization passed the SRM check (p=1.000); device allocation was also directionally balanced.
- The primary conversion effect is positive and statistically significant at alpha=0.05.
- There is **no Simpson's-paradox sign reversal** across the pre-specified device and country views.
- The refund guardrail did not show a significant increase. Revenue provides positive supporting evidence.

## Assumptions and Limitations

- This is synthetic, user-level intent-to-treat data; production logging defects, interference, bots, and repeat-device identity issues are not represented.
- The 28-day window measures near-term behavior only. Monitor refunds, support contacts, latency, and repeat purchase after rollout.
- Segment estimates are exploratory and underpowered; no multiplicity adjustment was applied.
- Revenue is simulated in a common currency and excludes payment fees, tax, and downstream margin.

## Rollout Plan

Use a staged rollout with live guardrail monitoring and an automatic rollback threshold. Recheck conversion, refunds, latency, and customer-support contacts after two full purchase cycles before moving to 100% exposure.