Customer Segmentation using Clustering Techniques: Data-Driven Approach to Enhance Marketing Strategy

Customer segmentation is a vital strategy for businesses seeking to enhance their marketing efforts and optimize resource allocation. This research focuses on segmenting customers using the Mall Customer Segmentation dataset, which includes demographic and behavioral attributes such as age, gender, annual income, and spending score. The work begins with data preprocessing, involving the handling of missing values, encoding of categorical variables, and scaling of numerical features to prepare the data for analysis. Following preprocessing, exploratory data analysis (EDA) is conducted to visualize feature distributions and relationships, providing insights into the data's structure. The core of the research involves applying various clustering algorithms-K-Means, Hierarchical Clustering, and DBSCAN-to identify distinct customer segments. The optimal number of clusters is determined through methods like the Elbow Method and Silhouette Score. The clusters are then evaluated for quality, and their characteristics are analyzed to understand different customer segments. This segmentation enables businesses to their marketing strategies, enhance customer satisfaction, and make data-driven decisions based on customer behavior insights and demographics.