Sales analysis queries with CTEs and window functions

Write a set of advanced SQL queries for sales analysis at [COMPANY NAME], given the following tables:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`orders\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\` (id, order_date, customer_id, seller_id, total_amount, status, channel)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`order_items\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\` (id, order_id, product_id, quantity, unit_price, discount)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`products\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\` (id, name, category_id, subcategory, cost, sale_price)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`customers\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\` (id, name, city, state, registration_date, segment)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`sellers\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\` (id, name, team, region, hire_date)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Query 1 — Seller ranking with targets:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nUsing ROW_NUMBER() and DENSE_RANK() OVER(), create a monthly seller ranking with:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Total sales, average ticket, order count\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Comparison vs. target of R$ [AMOUNT]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- % achievement and ranking position\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Variance vs. previous month using LAG()\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Query 2 — Customer cohort analysis:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nUsing CTEs, group customers by first purchase date (acquisition month) and calculate:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Month-over-month retention (month 0 to month 12)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Cumulative revenue per cohort\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Average LTV per cohort\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Query 3 — Fast-growing products:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nUsing window functions, identify the [NUMBER] products with the highest sales acceleration:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- 3-month rolling average of revenue\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- MoM growth rate (SUM() OVER with ROWS BETWEEN)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- % contribution to category total\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Query 4 — RFM Analysis (Recency, Frequency, Monetary):**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nSegment customers into 5 tiers for each dimension using NTILE(5):\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Recency: days since last purchase\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Frequency: total orders in the last [MONTHS] months\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Monetary: total spend\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Final classification: Champions, Loyal, At Risk, Lost\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Query 5 — Executive dashboard in a single query:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nChained CTE returning in one query:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Period revenue vs. previous period\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Top 5 products, top 5 sellers, top 5 cities\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Cancellation rate\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Average ticket and orders by channel\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nSQL dialect: [PostgreSQL/MySQL/BigQuery]. Comment each block explaining the logic.