Customer Segmentation - Using k-means

Customer segmentation is simply grouping customers with similar characteristics. These characteristics include geography, demography, behavioural, purchasing power, situational factors, personality, lifestyle, psychographic, etc. The goals of customer segmentation are customer acquisition, customer retention, increasing customer profitability, customer satisfaction, resource allocation by designing marketing measures or programs and improving target marketing measures.

Clustering is an efficient technique used for customer segmentation. Clustering places homogenous data points in a given dataset. Each of these groups is called a cluster. While the objects in each cluster are similar between themselves, they are dissimilar to the objects of other groups. Clustering is a type of data mining approach in machine learning classified under unsupervised learning. This is because it is able to discover patterns and information from unlabelled data. It is used extensively in machine learning, classification, and pattern recognition.

Clustering algorithms include the K-means algorithm, hierarchical clustering, DBSCAN. In this project, the k-means clustering algorithm has been applied in customer segmentation. K-means is a clustering algorithm based on the principle of partition. The letter k represents the number of clusters chosen. It is the most common centroid-based algorithm.