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You're working for a company that sells motorcycle parts, and they've asked for some help in analyzing their sales data!
They operate three warehouses in the area, selling both retail and wholesale. They offer a variety of parts and accept credit cards, cash, and bank transfer as payment methods. However, each payment type incurs a different fee.
The board of directors wants to gain a better understanding of wholesale revenue by product line, and how this varies month-to-month and across warehouses. You have been tasked with calculating net revenue for each product line and grouping results by month and warehouse. The results should be filtered so that only "Wholesale" orders are included.
They have provided you with access to their database, which contains the following table called sales:
Sales
| Column | Data type | Description |
|---|---|---|
order_number | VARCHAR | Unique order number. |
date | DATE | Date of the order, from June to August 2021. |
warehouse | VARCHAR | The warehouse that the order was made from— North, Central, or West. |
client_type | VARCHAR | Whether the order was Retail or Wholesale. |
product_line | VARCHAR | Type of product ordered. |
quantity | INT | Number of products ordered. |
unit_price | FLOAT | Price per product (dollars). |
total | FLOAT | Total price of the order (dollars). |
payment | VARCHAR | Payment method—Credit card, Transfer, or Cash. |
payment_fee | FLOAT | Percentage of total charged as a result of the payment method. |
Your query output should be presented in the following format:
product_line | month | warehouse | net_revenue |
|---|---|---|---|
| product_one | --- | --- | --- |
| product_one | --- | --- | --- |
| product_one | --- | --- | --- |
| product_one | --- | --- | --- |
| product_one | --- | --- | --- |
| product_one | --- | --- | --- |
| product_two | --- | --- | --- |
| ... | ... | ... | ... |
-- whole sale net revenue by product line across warehouse by month
SELECT product_line,
CASE WHEN EXTRACT('month' from date) = 6 THEN 'June'
WHEN EXTRACT('month' from date) = 7 THEN 'July'
WHEN EXTRACT('month' from date) = 8 THEN 'August'
END as month_name,
warehouse,
SUM(total) - SUM(payment_fee) AS net_revenue
FROM sales
WHERE client_type = 'Wholesale'
GROUP BY product_line, warehouse, month_name
ORDER BY product_line, month_name, net_revenue DESC;
The finance team is exploring ways to reduce transaction costs and improve profitability. They’ve asked you to determine the most profitable payment method for each warehouse in each month. Calculate the net revenue for each payment method, grouped by warehouse and month, and identify the top payment method for each combination.
-- net revenue by payment method, warehouse and month
-- find top payment method
SELECT
payment,
warehouse,
CASE WHEN EXTRACT('month' from date) = 6 THEN 'June'
WHEN EXTRACT('month' from date) = 7 THEN 'July'
WHEN EXTRACT('month' from date) = 8 THEN 'August'
END as month_name,
SUM(total) - SUM(payment_fee) AS net_revenue
FROM sales
GROUP BY warehouse, month_name, payment
ORDER BY warehouse, month_name, net_revenue DESC;The marketing team is planning a targeted campaign and wants to know the most popular product lines for retail and wholesale customers.
They have given you the task to find the top 3 most ordered product lines for each client type.
-- the 3 most popular product lines for both retail and wholesale
SELECT product_line, client_type, units_sold
FROM (
-- rank total unit sold by product line for each client type
SELECT
product_line,
client_type,
SUM(quantity) AS units_sold,
DENSE_RANK() OVER(PARTITION BY client_type ORDER BY SUM(quantity)) AS rnk
FROM sales
GROUP BY product_line, client_type
) AS sub
--filter top 3 product lines by client type
WHERE rnk <= 3
ORDER BY client_type, units_sold DESC;SELECT p.product_id, p.product_name, p.brand_id, p.category_id, p.model_year, p.list_price, s.store_id, s.quantity
FROM production.products p
JOIN production.stocks s ON p.product_id = s.product_id
ORDER BY p.product_id;