Analytical Methods for Unsupervised Learning
Analytical Methods for Unsupervised Learning
Modern businesses collect massive amounts of customer data. However, data alone does not automatically create value. Organizations must analyze patterns in the data to understand different groups of customers. One powerful method used by companies worldwide is Cluster Analysis.
In the Philippine business environment-where markets are diverse and consumer behavior varies across income groups, regions, and digital adoption levels-cluster analysis becomes a powerful marketing analytics tool.
For example, a company like Jollibee Foods Corporation may use customer purchase data to determine which groups prefer family meals, value meals, or premium menu offerings.
Through this module, students will learn how cluster analysis works, how clustering algorithms operate, and how these techniques help businesses make data-driven marketing decisions.
Cluster Analytics
Cluster Analytics
Cluster Analytics (also called Cluster Analysis) is a statistical technique used in Marketing Analytics to group customers into distinct segments based on similarities in their data - such as purchasing behavior, demographics, preferences, or engagement patterns.
Cluster analysis helps businesses identify groups of customers who behave similarly, allowing firms to design more effective marketing strategies, promotions, pricing, and product offerings.
In marketing analytics, cluster analysis is often used for:
Customer segmentation
Product grouping
Market segmentation
Behavioral analysis
Personalization strategies
Simply put:
Cluster Analysis equals Grouping similar customers together based on their behaviors or characteristics.