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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

ColumnData typeDescription
order_numberVARCHARUnique order number.
dateDATEDate of the order, from June to August 2021.
warehouseVARCHARThe warehouse that the order was made from— North, Central, or West.
client_typeVARCHARWhether the order was Retail or Wholesale.
product_lineVARCHARType of product ordered.
quantityINTNumber of products ordered.
unit_priceFLOATPrice per product (dollars).
totalFLOATTotal price of the order (dollars).
paymentVARCHARPayment method—Credit card, Transfer, or Cash.
payment_feeFLOATPercentage of total charged as a result of the payment method.

Your query output should be presented in the following format:

product_linemonthwarehousenet_revenue
product_one---------
product_one---------
product_one---------
product_one---------
product_one---------
product_one---------
product_two---------
............
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DataFrameas
revenue_by_product_line
variable

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,
    warehouse,
	SUM(total) - SUM(payment_fee) AS net_revenue
FROM sales
WHERE client_type = 'Wholesale'
GROUP BY product_line, warehouse, month
ORDER BY product_line, month, net_revenue DESC

Extended Project below

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.

Spinner
DataFrameas
df
variable
WITH monthly AS (
  SELECT
    CASE EXTRACT(MONTH FROM date)
      WHEN 6 THEN 'June'
      WHEN 7 THEN 'July'
      WHEN 8 THEN 'August'
    END AS month,
    warehouse,
    payment,
    SUM(total) - SUM(payment_fee) AS net_revenue
  FROM sales
  WHERE EXTRACT(MONTH FROM date) IN (6,7,8)
  GROUP BY 1,2,3
),
ranked AS (
  SELECT
    month, warehouse, payment, net_revenue,
    ROW_NUMBER() OVER (
      PARTITION BY warehouse, month
      ORDER BY net_revenue DESC
    ) AS rn
  FROM monthly
)
SELECT *
FROM (
  SELECT 'all_methods' AS section, month, warehouse, payment, net_revenue
  FROM monthly

  UNION ALL

  SELECT 'top_only' AS section, month, warehouse, payment, net_revenue
  FROM ranked
  WHERE rn = 1
) AS combined
ORDER BY
  section,
  CASE month WHEN 'June' THEN 1 WHEN 'July' THEN 2 WHEN 'August' THEN 3 END,
  warehouse,
  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.

Spinner
DataFrameas
df1
variable

WITH totals AS (
  SELECT
    client_type,
    product_line,
    SUM(quantity) AS total_units
  FROM sales
  GROUP BY client_type, product_line
),
ranked AS (
  SELECT
    client_type, product_line, total_units,
    DENSE_RANK() OVER (PARTITION BY client_type ORDER BY total_units DESC) AS rnk
  FROM totals
)
SELECT client_type, product_line, total_units
FROM ranked
WHERE rnk <= 3
ORDER BY client_type, total_units DESC;