Customer segmentation pattern in Data Analysis Python - Mini Project: Build & Apply

Customer segmentation pattern

📖 Scenario: You work for a small online store. You want to group customers based on how much they spent last month. This helps the store send special offers to big spenders and thank smaller spenders.

🎯 Goal: Build a simple program that groups customers into 'High spender' and 'Low spender' based on their spending amount.

📋 What You'll Learn

Create a dictionary with customer names and their spending amounts.

Set a spending threshold to separate high and low spenders.

Use a dictionary comprehension to create a new dictionary with customer names and their segment ('High spender' or 'Low spender').

Print the final segmentation dictionary.

💡 Why This Matters

🌍 Real World

Stores and businesses often group customers by spending to target marketing and rewards effectively.

💼 Career

Data analysts and marketers use customer segmentation to improve sales strategies and customer satisfaction.

1

Create the customer spending data

Create a dictionary called customer_spending with these exact entries: 'Alice': 120, 'Bob': 75, 'Charlie': 200, 'Diana': 50, 'Evan': 180.

Data Analysis Python

# Create the customer_spending dictionary with given values
# Your code here

Hint

Use curly braces {} to create a dictionary with the exact customer names and spending amounts.

2

Set the spending threshold

Create a variable called threshold and set it to 100. This will separate high spenders from low spenders.

Data Analysis Python

customer_spending = {'Alice': 120, 'Bob': 75, 'Charlie': 200, 'Diana': 50, 'Evan': 180}
# Create the threshold variable and set it to 100
# Your code here

Hint

Just create a variable named threshold and assign it the value 100.

3

Create the customer segmentation dictionary

Use a dictionary comprehension to create a new dictionary called customer_segment. For each customer and spending in customer_spending.items(), assign 'High spender' if spending is greater than or equal to threshold, otherwise assign 'Low spender'.

Data Analysis Python

customer_spending = {'Alice': 120, 'Bob': 75, 'Charlie': 200, 'Diana': 50, 'Evan': 180}
threshold = 100
# Create customer_segment dictionary using dictionary comprehension
# Your code here

Hint

Use a dictionary comprehension with for name, spending in customer_spending.items() and a conditional expression to assign segments.

4

Print the customer segmentation

Write a print statement to display the customer_segment dictionary.

Data Analysis Python

customer_spending = {'Alice': 120, 'Bob': 75, 'Charlie': 200, 'Diana': 50, 'Evan': 180}
threshold = 100
customer_segment = {name: ('High spender' if spending >= threshold else 'Low spender') for name, spending in customer_spending.items()}
# Print the customer_segment dictionary
# Your code here

Hint

Use print(customer_segment) to show the final dictionary.