Data Analysis Mode

Data quality first — 37 raw rows → 36 clean

Three problems found, all handled inside a reproducible script:

Cross-check: the interpolated East March figure implies an order value of 159.5, almost identical to East's annual median of 159.4 and consistent with the 812 orders recorded that month.

Region comparison, 2024

What the numbers say

• The gap is structural, not a pacing problem: the three regions correlate at ≥0.996 month over month, so the ranking never shifts on its own.
• East leads North by 79.6%. East and South have almost identical order values (159.2 vs 160.1), so that 30.6% gap is volume, not pricing — 11,527 vs 8,779 orders.
• North's problem is price and momentum, not just volume: lowest order value (152.0, 5.1% below South) and the weakest second-half growth (+20.0%).

Deliverables written to the workspace

analyze_sales.py (reproducible pipeline with cleaning comments), region_revenue_comparison.png (200 dpi, four panels), region_summary.csv, analysis_report.md.

Limitations

The missing March figure is an estimate and is flagged in the chart. One year of monthly data cannot separate seasonality from trend — a second year is needed to test whether the North slowdown is cyclical.

Next steps

1. Pull the same breakdown for 2023 to test whether North's H2 deceleration is seasonal or persistent.
2. Split North's orders by product tier to confirm the low average order value is a mix problem.
3. Choose the intervention from that mix analysis rather than from the headline gap.