Profitability Metrics in Sales Data Analysis with Python - Part2

Profitability metrics tell us whether sales are actually creating value for the business. Revenue alone doesn’t guarantee success — we need to measure margins, profits, and returns.

Generate a Sample Sales Dataset

Before diving into KPIs, let’s create a realistic dataset with customers, sales reps, products, regions, and revenues.

The description of some of the fields are given below:

• ListPricePerUnit → Original price before discount

• Discount% → Percentage discount applied

• CostPerUnit → Cost per unit is the total cost of producing one unit of a product or service.

  CostPerUnit=Total Production Cost​ / Number of Units Produced

• PricePerUnit → Final price after discount

• PromotionFlag → 1 if the transaction was during a promotion, else 0

• Revenue → UnitsSold × PricePerUnit

• Cost → UnitsSold × CostPerUnit

• Profit → Revenue – Cost

1.Gross Profit

Definition:
The profit after subtracting the direct costs of goods sold (COGS) from total revenue. It measures how efficiently you produce/sell your products.

Formula:

Gross Profit=Revenue−Cost

gross_profit = df["Profit"].sum() # Profit already = Revenue - Cost

print("Gross Profit:", gross_profit)

2.Gross Margin %

Definition:
Shows what percentage of revenue remains after covering COGS. Higher margins means more efficient operations.

gross_margin_pct = (df["Profit"].sum() / df["Revenue"].sum()) * 100

print("Gross Margin %:", round(gross_margin_pct, 2))

3.Operating Profit

Definition:
Profit after deducting operating expenses (like salaries, rent, marketing). It excludes taxes and financing.

Formula:

Operating Profit=Gross Profit−Operating Expenses

operating_expenses = 0.2 * df["Revenue"].sum() # assume 20% of revenue

operating_profit = df["Profit"].sum() - operating_expenses

print("Operating Profit:", operating_profit)

4.Net Profit

Definition:
The bottom-line profit after subtracting taxes and all expenses. This is the true profitability of the company.

Formula:

Net Profit=Operating Profit−Taxes

taxes = 0.3 * operating_profit # assume 30% tax

net_profit = operating_profit - taxes

print("Net Profit:", net_profit)

5.Net Profit Margin %

Definition:
Percentage of revenue that turns into actual profit after all expenses and taxes.

net_profit_margin_pct = (net_profit / df["Revenue"].sum()) * 100

print("Net Profit Margin %:", round(net_profit_margin_pct, 2))

6.Contribution Margin

Definition:
Measures how much revenue is left after variable costs, to cover fixed costs and generate profit. Helps in pricing & product decisions.

Formula:

Contribution Margin=Revenue−(UnitsSold×CostPerUnit)

df["Contribution"] = df["Revenue"] - (df["UnitsSold"] * df["CostPerUnit"])

contribution_margin = df["Contribution"].sum()

print("Contribution Margin:", contribution_margin)

7.Profit per Product

Definition:
Shows the average profit contribution of each product category. Useful for identifying high-margin vs low-margin products.

Formula:

Profit per Unit=Profit / UnitsSold

df["Profit_per_Unit"] = df["Profit"] / df["UnitsSold"]

profit_per_product = df.groupby("ProductCategory")["Profit_per_Unit"].mean()

print(profit_per_product)

8.Break-even Sales Volume

Definition:
The sales volume needed to cover fixed costs. Beyond this point, the company starts making profit.

Formula:

Break-even Volume=Fixed Costs / (PricePerUnit−CostPerUnit)

fixed_costs = 50000

avg_price = df["PricePerUnit"].mean()

avg_cost = df["CostPerUnit"].mean()

break_even_volume = fixed_costs / (avg_price - avg_cost)

print("Break-even Sales Volume:", round(break_even_volume))

9.Return on Sales (ROS)

Definition:
Measures how much profit is generated from each unit of revenue. A key efficiency metric.

Formula:

ROS=(Operating Profit / Revenue) ×100

ros = (operating_profit / df["Revenue"].sum()) * 100

print("Return on Sales (ROS) %:", round(ros, 2))

10.EBITDA Margin

Definition:
Earnings before interest, taxes, depreciation, and amortization — shows operational profitability.

Formula:

EBITDA Margin %=(EBITDA / Revenue) ×100

depreciation = 0.05 * df["Revenue"].sum() # assume 5% of revenue

ebitda = operating_profit + depreciation

ebitda_margin = (ebitda / df["Revenue"].sum()) * 100

print("EBITDA Margin %:", round(ebitda_margin, 2))

Summary