Unsupervised Learning: Dimensionality & Clustering for Marketing Management 

This course dives into the world of unsupervised learning, a powerful branch of machine learning that unlocks hidden patterns and structures within your marketing data. You'll explore key techniques like dimensionality reduction and clustering, empowering you to gain deeper customer insights, personalize marketing campaigns, and improve sales targeting within your Marketing, Pricing Strategy, and Sales Management strategies. 

• Define unsupervised learning and its role in uncovering hidden patterns within customer data for marketing, pricing, and sales applications.

• Understand the concept of dimensionality and its challenges for machine learning models.

• Explore dimensionality reduction techniques like Principal Component Analysis (PCA) for simplifying complex datasets.

• Master clustering algorithms (k-means clustering) to segment customers based on their inherent characteristics.

• Apply unsupervised learning techniques to real-world marketing, pricing, and sales challenges (e.g., customer segmentation for targeted promotions, identifying high-value customer segments).

• Analyze the benefits and limitations of unsupervised learning compared to supervised learning for specific marketing tasks.

1. Unveiling Hidden Patterns with Unsupervised Learning

• Introduction to Unsupervised Learning: Understanding its core concepts and how it differs from supervised learning.

• Demystifying Dimensionality: Exploring the challenges of high-dimensional data for marketing analytics and AI models.

• Exploring Dimensionality Reduction Techniques: Focusing on Principal Component Analysis (PCA) for simplifying complex customer datasets.

• Hands-on Exercises (Optional): Utilizing online tools or libraries to explore PCA on marketing-related datasets (e.g., reducing dimensionality of customer purchase history data).

• Case Studies: Examining how companies leverage PCA for tasks like customer segmentation or optimizing marketing campaigns based on reduced-dimensionality data.

2. The Power of Clustering for Customer Insights

• Introduction to Clustering Algorithms: Understanding the concept of clustering and its applications in marketing, pricing, and sales.

• Deep dive into k-means Clustering: Mastering a popular clustering technique to segment customers based on their similarities.

• Applying k-means Clustering to Marketing Challenges: Utilizing k-means for tasks like customer segmentation for targeted email campaigns or identifying high-potential customer groups.

• Hands-on Exercises (Optional): Working with marketing datasets to practice k-means clustering and customer segmentation techniques (may involve basic coding).

• Case Studies: Analyzing real-world examples of k-means clustering used in marketing, such as segmenting customers for personalized product recommendations or targeted pricing strategies.

• Course Wrap-up: Understanding the limitations of unsupervised learning, choosing the right technique for your marketing goals, and best practices for responsible data analysis.

• Solid understanding of mathematics, including linear algebra and statistics

• Proficiency in programming with Python or R

• Familiarity with basic machine learning concepts and supervised learning algorithms