This project analyzes customer satisfaction at Citizens Bank using Net Promoter Score (NPS) survey data, focusing specifically on customers’ likelihood to recommend the bank to others. By combining survey responses with customer account data, transaction history, and banking behaviors, we identified key factors that drive customer satisfaction and loyalty. Our analysis employed multiple methodologies, including demographic analysis, survey completion pattern analysis, touchpoint impact assessment, feature engineering from banking behaviors, causal inference for macro drivers, time-series macro causality analysis, machine learning models for predicting satisfaction drivers, analysis of the hierarchical structure of data relationships to key operational metrics, and natural language processing of customer feedback. Through these approaches, we uncovered actionable insights into how different banking channels, customer segments, and service experiences influence satisfaction levels, providing Citizens Bank with data-driven recommendations to enhance the customer experience and increase loyalty across its diverse customer base.
Mentor: Hengzhong Liu
