Analysis of Customer Segmentation Model through K-Means Clustering

Customer Segmentation as the name suggests simply means the division of the potential customers in the market into discrete groups. Companies employing customer segmentation operate under the fact that every customer is different and that their marketing efforts would be better served if they target specific, smaller groups with messages that those consumers would find relevant and lead them to buy something. In this paper, a mall customers data set was taken initially before the beginning of this research work. The dataset was described and null values were checked because they might cause trouble. In the next step, analysis was done over various parameters based on age, spending score, annual income, gender, etc. After the analysis, two variables for comparison were taken i.e., annual income and spending score. Then the WCSS (Within Cluster Sum of Squares) method was applied to find the optimum number of clusters that could be developed between these two variables. On getting the optimum number of clusters K-Means clustering algorithm was implemented and results were generated based on which interpretations can be made.