Behavioral and Demographic Segmentation using K-Means Clustering K-means clustering is a widely used technique for segmenting customers based on their behavi...
K-means clustering is a widely used technique for segmenting customers based on their behavior and demographics. This technique involves dividing a set of data points into k clusters (groups) based on their shared characteristics. Each data point is assigned to a cluster based on the cluster with the closest centroid (center point) in terms of Euclidean distance.
Key steps involved in K-means clustering:
Initialisation: Choose the number of k clusters (k) based on the data and its characteristics.
Data preparation: Transform the data into a format suitable for clustering, such as one-hot encoding for categorical variables.
Centroid calculation: Calculate the centroid for each cluster by averaging the data points in that cluster.
kmeans iteration: Assign each data point to a cluster based on the cluster with the closest centroid.
Repeat: Repeat steps 2-4 until the centroids no longer change or a convergence criterion is reached.
Evaluation: Evaluate the quality of the clusters by analysing factors like silhouette score (between-cluster variability) and Davies-Bouldin index (within-cluster similarity).
Benefits of K-means clustering:
Identifies natural customer groups with distinct characteristics.
Provides insights into customer behavior and preferences.
Can be used for targeted marketing campaigns to reach specific customer groups.
Example: Imagine a retail store with different departments (clothing, electronics, etc.). K-means clustering could be used to identify 3 distinct customer groups:
Group 1: Young tech enthusiasts who are interested in new gadgets.
Group 2: Fashion-conscious shoppers who prioritize quality and style.
Group 3: Value-conscious customers focused on getting the most out of their purchases.
By understanding customer behavior within these clusters, the store can tailor its marketing and promotions to each group, leading to increased sales and customer satisfaction