SEGMENTATION OF CUSTOMER PROFILING VARIABLES FOR MARKET ANALYSIS USING EXPLORATORY DATA ANALYSIS AND K-MEANS CLUSTERING

ABSTRACT

Market analysis has evolved significantly over the years, with customer profiling and segmentation playing a pivotal role in understanding and catering to diverse consumer needs. One powerful approach to customer segmentation involves the combined use of Exploratory Data Analysis (EDA) and K-means clustering. EDA, encompassing univariate, bivariate, and multivariate analyses, allows for a comprehensive examination of customer data, while K-means clustering assists in identifying distinct customer segments based on similarities in various profiling variables. This study employs Exploratory Data Analysis, encompassing univariate, bivariate, and multivariate analyses, to gain profound insights into customer demographics. The initial analysis revealed that a substantial portion of the customer base falls within the age range of 41 to 60 years, possesses first-degree qualifications, and is predominantly married, constituting approximately 65% of the sample. Additionally, income distribution exhibited a diverse pattern, with the majority earning between 0 to 100k$, but a noteworthy proportion having incomes exceeding 600k$. The bivariate analysis further unveiled intriguing insights, particularly in terms of spending patterns linked to educational backgrounds.