Audience targeting (demographics, interests, lookalike) in Digital Marketing - Mini Project: Build & Apply

Audience Targeting with Demographics, Interests, and Lookalike Audiences

📖 Scenario: You are managing an online store and want to reach the right people with your ads. To do this, you will create different audience groups based on who they are, what they like, and people similar to your best customers.

🎯 Goal: Build a simple audience targeting setup that includes demographic data, interest categories, and a lookalike audience group.

📋 What You'll Learn

Create a dictionary called audience_demographics with exact keys and values for age and location

Create a list called audience_interests with exact interest strings

Create a dictionary called lookalike_audience with exact keys and values for source audience and similarity score

Combine all three into a final dictionary called audience_targeting with keys demographics, interests, and lookalike

💡 Why This Matters

🌍 Real World

Marketers use audience targeting to show ads to people who are most likely to be interested, saving money and increasing sales.

💼 Career

Understanding how to organize and use audience data is important for digital marketing roles, ad campaign management, and customer analysis.

1

Set up audience demographics

Create a dictionary called audience_demographics with these exact entries: 'age_range': '25-40' and 'location': 'United States'.

Digital Marketing

# Create the audience_demographics dictionary with age_range and location
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with the keys 'age_range' and 'location'.

2

Add audience interests

Create a list called audience_interests containing these exact strings: 'fitness', 'healthy eating', and 'outdoor activities'.

Digital Marketing

audience_demographics = {'age_range': '25-40', 'location': 'United States'}
# Create the audience_interests list with the given interests
# Your code here

Need a hint?

Use square brackets [] to create a list with the exact interest strings.

3

Create a lookalike audience

Create a dictionary called lookalike_audience with these exact entries: 'source_audience': 'best_customers' and 'similarity_score': 0.85.

Digital Marketing

audience_demographics = {'age_range': '25-40', 'location': 'United States'}
audience_interests = ['fitness', 'healthy eating', 'outdoor activities']
# Create the lookalike_audience dictionary with source_audience and similarity_score
# Your code here

Need a hint?

Use a dictionary with the keys 'source_audience' and 'similarity_score' and the exact values.

4

Combine all audience data

Create a dictionary called audience_targeting with these exact keys and values: 'demographics' set to audience_demographics, 'interests' set to audience_interests, and 'lookalike' set to lookalike_audience.

Digital Marketing

audience_demographics = {'age_range': '25-40', 'location': 'United States'}
audience_interests = ['fitness', 'healthy eating', 'outdoor activities']
lookalike_audience = {'source_audience': 'best_customers', 'similarity_score': 0.85}
# Combine all into audience_targeting dictionary
# Your code here

Need a hint?

Create a dictionary that groups the three audience parts under the keys 'demographics', 'interests', and 'lookalike'.