What is Clustering Analysis?
Cluster analysis involves using mathematical models to discover groups or “personas” of similar customers by identifying the smallest variances among customers within each group. This method, free from predetermined thresholds, relies on the data itself to reveal the natural groupings, or customer archetypes, present within a customer base.
Clustering Analysis Techniques
- K-means Clustering: Often referred to as scientific segmentation, this method partitions customers into k clusters, where k is determined by the analyst.
- Hierarchical and Density-Based Clustering: These methods cater to more complex scenarios where the data might not be well-suited for K-means, offering a more nuanced understanding of customer groupings.
Customer Segmentation via Cluster Analysis
Customer segmentation via clustering analysis is a critical part of the current marketing and analytics systems. Customer segmentation is performed by grouping customers based on their common traits that permit the businesses to plan, develop, and deliver their strategies, products, and services thus more efficiently. Through data mining, retailers can analyze customer behaviors, preferences, and needs, and as such they can boost customer loyalty and global sales revenue.
Contact Us