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 | --- | --- | --- |
| ... | ... | ... | ... |
-- Wholesale Net revenue for each productline, how it varies month to month and across warehouses
SELECT product_line, to_char(date, 'Month') AS month, warehouse, ROUND(SUM(total)*(1-payment_fee) :: numeric,2) AS net_revenue
FROM sales
WHERE client_type = 'Wholesale'
GROUP BY product_line, month, warehouse, payment_fee
ORDER BY product_line, month DESC, net_revenue DESC;SELECT * FROM public.salesFind the total sales for productlines in the retail space
SELECT product_line, SUM(total) AS total_sales
FROM public.sales
WHERE client_type = 'Retail'
GROUP BY product_line
ORDER BY total_sales Desc;Which payment method is popular for retail clients
SELECT payment, COUNT(*), SUM(total)
FROM public.sales
WHERE client_type = 'Retail'
GROUP BY payment
ORDER BY COUNT(*) DESC;Power BI overview of sales table data