Credit Card Holders Segmentation Using K-mean Clustering with Autoencoder

Abstract

Marketing is crucial for the company's growth and long-term viability. Marketers can aid in the development of a company's brand, customer engagement, revenue growth and sales. Knowing and identifying clients' needs is one of the most difficult tasks for the marketers. Marketers can begin a focused marketing strategy that is suited to specific demands by understanding the customer. Data science can be used to do market segmentation if the customer data is accessible. We are doing a credit card segmentation using New York City bank data set. By doing analysis on this dataset, we can know about the behavior of credit card holders and can lunch an effective market campaign which would be more focused on the targeted customer. This customer-centric campaign will help to reduce the overall marketing cost, and this will boost in the number of credit card holders. In the evolving world of technology, Fintech industry is growing very fast. We are doing behavioral segmentation process to make clusters of customers. We will be using Autoencoders and then perform k-means clustering, PCA for visualization.