Customer Segmentation using K-Means Clustering

K-Means clustering is a strategy for consumer segmentation that groups customers according to shared features. This allows organisations to optimise product offers, improve customer service, and customise marketing. K-Means is an unsupervised machine learning technique that groups clients with similar behaviours, demographics, or preferences in order to split data into a predetermined number of clusters (K).

Important Steps in K-Means Data Collection for Customer Segmentation: Gather pertinent consumer information, such as demographics (age, location), transactional data (spending patterns), and behavioural data (frequency, past purchases, and browsing activity).

• Data Preprocessing: Standardize the data, especially if it includes different scales, such as income and purchase frequency. This ensures that no single feature dominates the clustering process.
• Choosing K and Applying K-Means: Select an appropriate number of clusters (K) by experimenting with different values and evaluating using methods like the elbow method, which helps find the K value where adding more clusters does not significantly reduce variance within clusters. K-Means then assigns each customer to the nearest cluster center based on Euclidean distance, iteratively refining the cluster centers.
• Analyzing Segments: Once segmented, each cluster can be analyzed to identify unique characteristics, like high spenders, frequent shoppers, or customers who only purchase specific product types.
• Actionable Insights: Businesses can use these clusters for targeted marketing (e.g., offering discounts to price-sensitive segments), personalized product recommendations, and improved customer experience by catering to the specific needs of each segment.