This project developed three segmentation models tailored to the bank's actual operations: dormant customers, low-value active customers, and high-value active customers. Each customer group was analyzed based on its unique characteristics. These segmentation insights were surfaced through the CRM system’s front-end interface, enabling internal staff to perform targeted follow-up marketing.

ACRM Phase One

Phase One focused on building three core modeling themes—Customer Segmentation, Customer Acquisition, and Customer Retention—along with nine major customer label categories: Basic Information, Risk, Behavioral Data, Early Warning, Asset Profile, Loyalty, Business Activity, Marketing Potential, and Contribution.

ACRM Phase Two

Phase Two emphasized the development of refined customer segmentation models, cross-selling models, and customer churn prediction models.

This project developed three segmentation models tailored to the bank's actual operations: dormant customers, low-value active customers, and high-value active customers. Each customer group was analyzed based on its unique characteristics. These segmentation insights were surfaced through the CRM system’s front-end interface, enabling internal staff to perform targeted follow-up marketing.

1.Managing a massive customer base is no longer feasible through manpower alone. The solution involved building a bank-wide customer database and deploying a comprehensive analytics platform. Advanced data analysis models were used to segment customers across multiple dimensions, such as customer tier, product preference, consumption behavior, channel interaction preference, risk appetite, investment preference, and lifecycle stage.

2.The system enabled behavior prediction through customer data analysis. Based on segmentation and predictive outcomes, the bank could accurately identify customer groups, enabling the development of tailored products and improving both response rates and marketing success rates.

3.Day-to-day banking operations generate vast volumes of data. Traditional reporting alone is insufficient to uncover hidden patterns or forecast trends. By leveraging data mining techniques, the bank could extract actionable insights from large datasets and apply these across all stages of the customer lifecycle.

This project successfully integrated customer-centric management principles with modern information technology to build an advanced retail CRM system. Once launched, the system enabled more responsive and comprehensive services across the customer lifecycle—pre-sale, during service, and post-sale—significantly improving marketing efficiency.
By restructuring the customer service workflow, the bank centralized customer data resources, empowering relationship managers to better discover, understand, and engage with high-quality clients. This not only fostered long-term customer relationships but also provided robust support for daily operations and the development of the relationship manager team.