Education 

🧠 What is Forecasting?

Forecasting means using past data to predict future trends, like:

• Revenue growth
  
  

• Expenses
  
  

• Views/subscribers (for YouTube)
  
  

• Donations or trip sign-ups (for nonprofits)
  
  

⚙️ Step-by-Step Forecasting Model Setup

✅ Step 1: Gather Your Data

You’ll need historical data—at least 6–12 months if possible. For example:

Month

Revenue ($)

Jan 2024

1,000

Feb 2024

1,200

...

...

Mar 2025

2,000

✅ Step 2: Choose a Forecasting Method

🧮 A. Simple Linear Regression (Excel-friendly)

Assumes a straight-line growth trend.

1. Plot your data on a line chart.
   
   

2. Add a trendline in Excel (right-click → “Add Trendline”).
   
   

3. Check the box for “Display Equation on chart”.
   
   

4. Use that equation to project future values (e.g., y = 100x + 900).
   
   

📈 B. Moving Average

Great if your data has ups and downs (e.g. seasonality).

1. Use Excel’s AVERAGE() function over a 3- or 6-month period.
   
   

2. Drag it down to create smoothed values.
   
   

3. Extend it forward as a prediction.
   
   

🤖 C. ARIMA or ML (Python Advanced)

Used when you want accuracy and have more data.

🧪 Example in Excel (Linear Forecast)

Let’s say:

• YouTube ad revenue grew steadily
  
  

• You want to forecast the next 6 months
  
  

You could use the TREND() function:

excel

CopyEdit

=TREND(B2:B13, A2:A13, A14:A19)

Where:

• B2:B13 = past revenue
  
  

• A2:A13 = months (as numbers)
  
  

• A14:A19 = future months
  
  

🐍 Example in Python (Simple Forecast)

python

CopyEdit

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

# Sample data

months = np.array(range(1, 13)).reshape(-1, 1)

revenue = np.array([1000, 1100, 1150, 1300, 1250, 1350, 1450, 1500, 1600, 1650, 1750, 1800])

# Model

model = LinearRegression()

model.fit(months, revenue)

# Forecast next 6 months

future_months = np.array(range(13, 19)).reshape(-1, 1)

forecast = model.predict(future_months)

# Plot

plt.plot(months, revenue, label='Historical')

plt.plot(future_months, forecast, label='Forecast', linestyle='--')

plt.legend()

plt.show()

📈 1. Revenue

What it is: Total income from sales of goods or services before expenses.

Formula:

text

CopyEdit

Revenue = Price × Quantity Sold

Why it matters:
It shows the size of the business. Bigger revenue often means more market presence, but not necessarily more profit.

💰 2. Net Income (Profit)

What it is: The money left after all expenses, taxes, and costs are deducted from revenue.

Formula:

text

CopyEdit

Net Income = Revenue - Expenses

Why it matters:
It tells you if the company is actually making money. High revenue means nothing if costs are eating it all up.

🧮 3. Gross Margin (%)

What it is: Shows how much money a company keeps after covering the cost of making its product or service.

Formula:

text

CopyEdit

Gross Margin % = (Revenue - Cost of Goods Sold) / Revenue × 100

Why it matters:
This shows core efficiency. A higher margin means the company makes more from each sale after direct costs.

⚙️ 4. Operating Margin (%)

What it is: Measures profit from normal operations before taxes and interest.

Formula:

text

CopyEdit

Operating Margin % = Operating Income / Revenue × 100

Why it matters:
It shows how efficiently the business runs day-to-day, excluding financing and tax impacts.

📊 5. Earnings Per Share (EPS)

What it is: Profit available to each share of stock.

Formula:

text

CopyEdit

EPS = Net Income / Number of Outstanding Shares

Why it matters:
Investors use this to measure profitability per share. Higher EPS = more value to shareholders.

🌱 6. Growth Rate (YoY or QoQ)

What it is: Tracks how fast a company is growing in revenue, profit, users, etc.

Formula (YoY Growth):

text

CopyEdit

Growth Rate % = [(This Year - Last Year) / Last Year] × 100

Why it matters:
Growth is often more attractive than size—investors want to see increasing sales, profits, or user base.

💵 7. Customer Acquisition Cost (CAC)

What it is: How much it costs to gain one customer.

Formula:

text

CopyEdit

CAC = Total Marketing + Sales Costs / Number of New Customers Acquired

Why it matters:
Lower CAC = more efficient marketing. It’s key for scaling businesses (especially startups and online brands).

🧠 8. Lifetime Value (LTV)

What it is: The total money a customer will bring in over the entire relationship.

Formula (simplified):

text

CopyEdit

LTV = Average Purchase Value × Purchase Frequency × Customer Lifespan

Why it matters:
LTV tells you how valuable a customer is. You want LTV to be much higher than CAC.

🥧 9. Market Share

What it is: The portion of total industry sales a company has.

Formula:

text

CopyEdit

Market Share % = Company Revenue / Total Market Revenue × 100

Why it matters:
This shows a company’s dominance or competitive position in its industry.

🔁 Example: Compare Two Companies

Let’s say you're comparing Company A vs Company B in an industry:

Metric

Company A

Company B

Revenue

$10M

$8M

Net Income

$2M

$1.2M

Gross Margin (%)

60%

40%

EPS

$1.25

$0.90

CAC

$20

$50

LTV

$400

$300

→ Company A is larger, more efficient, and has more valuable customers.