Segmentation Study on Bank Customers Based on RNN

Abstract

Effective Customer Relationship Management Customer relationship management is critical as an indication of a firm’s future growth in times of technological change and shifting relationships between firms and consumers. This research focuses on customer segmentation as an important strategy to strengthen the relationship between the business and the customers. Customer segmentation is the overall approach of subdividing a customer database into groups based on characteristics and behaviour’s common in each segment. The research applies the K-means clustering technique to segment customers into six distinctive groups based on annual incomes and expenditure scores, and two main components. If the clustering results are effective, this will allow businesses to make decisions based on data, which contributes to enhancing their product and service range, making rapid adjustments according to altered customer needs. The main aim of the current research project is to prove the effectiveness of RNN-based customer segmentation with the use of mean values as a guiding signal for the assignment of new clients into the relevant clusters. The ability to use RNNs for high precision consumer segmentation is critical for sustainable competitive advantage in the market marked by rapid evolution and hence extreme competition.

Topics & Concepts