Data & Analysis

**Assumptions**

- `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed.
- "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today.
- Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied.
- Cancelled orders are excluded. If `status` uses different values than `'

```csv quarter,line,units_produced,defects,defect_rate_pct,shift_pattern Q1,Line A,14200,318,2.24,"Two shifts, weekdays" Q1,Line B,11850,402,3.39,"Two shifts, weekdays" Q1,Line C,9600,151,1.57,"One shift, weekdays" Q1,Line D,UNCLEAR,88,UNCLEAR,"One shift, weekends" Q2,Line A,15040,295,1.96,"Three shifts, weekdays" Q2,Line B,12310,377,3.06,"Two shifts, weekdays" Q2,Line C,9880,143,1.45,"One shift, weekdays" Q2,Line D,4120,96,2.33,"One shift, weekends" ``` **Notes** - **UNCLEAR cells:** row 5 (Q

# Checkout redesign A/B test — analysis plan and interpretation ## What the design supports Two independent groups, roughly 6,200 per arm, with a binary primary outcome (completed purchase) and a continuous secondary outcome (order value, right-skewed as order values almost always are). Randomisation at the session level with a 21-day run covering three full weekly cycles is sound. One caveat that limits every conclusion below: sessions, not users, were randomised, so a returning visitor could

## 1. Technical Audit & Business Context | Column | Smell | Business impact | |---|---|---| | `churn_date` | Populated only for churned accounts | This is the label in disguise. Any model that sees it will score ~perfectly in training and be useless in production. | | `mrr` | Right-skewed, a long tail of enterprise accounts | Mean imputation would drag small accounts upward and understate churn risk in the SMB segment, which is where outreach capacity actually sits. | | `signup_date` | Mixed `Y

**Recommended formula** — enter in Summary!B2 and fill down: =IFERROR(SUMIFS(Orders!$D$2:$D$5000, Orders!$B$2:$B$5000, $A2, Orders!$C$2:$C$5000, "Shipped", Orders!$A$2:$A$5000, ">="&DATE(2025,10,1), Orders!$A$2:$A$5000, "<="&DATE(2025,12,31)), 0) **How it works, step by step** 1. SUMIFS(sum_range, criteria_range1, criteria1, ...) adds values from Orders!D2:D5000 only for rows where every criteria pair matches. Unlike SUMIF it takes multiple conditions, so no helper column is needed. 2. Orders