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Customer Acquisition
Customer Promotion
Mature Customers
Customer Decline
Customer Churn
Ruihedata All-scenario Marketing Solution is an enterprise-level solution that manages customer life-cycle marketing for financial institutions, covering business scenarios and events through customer life-cycle, including acquisition, maturing, lapse, and churn.
MGM marketing one covers business scenarios and business events at different stages such as customer acquisition, customer promotion, customer maturity, customer decline, and customer churn.
Increase in deposit, marketing for paycheck account holders, potential customer mining, analysis to activate dormant customers for higher MAU, long-tail customer marketing.
Cross-sell of wealth management products, analysis of debit card to credit card conversion, prediction of customer borrowing potential, behavior prediction of customers with maturing wealth management products, competitor pricing analysis.
Early warning of customer churn, retention, activation of dormant customers, prediction of fund flow.
Ruihedata has developed a distinctive marketing solution for a joint-stock commercial bank to focus on its depositors, leading to 90%+ of customer reach, spanning over 100 channels and loyalty marketing programs, increasing deposit of target customers by tens of millions of RMB Yuan, and contracting new depositors.
By modeling machine learning, Ruihedata helps a joint-stock commercial bank forecast the behavior of customers with maturing wealth management products. The solution flags customers with high risk of net AUM outflow, identifies common features of those desiring diversified asset allocation, and translates the findings into sales leads. By recommending non-money-market funds of top monthly sales, formulating a white list of wealth management products and other means, it helps the bank to target particular customer groups and channels with differentiated marketing strategies and campaigns. The solution reports 100% coverage of wealth management products and 90%+ coverage of customers with maturing wealth management products, reveals new patterns of customer behavior, and offers personalized marketing schemes for target customers.
Medium and high-end customers are leading contributors to retail banking profit of a state-owned bank. Therefore, it’s imperative to retain these customers for steady growth in profit. Ruihedata helps it construct an early warning model for customer churn. The model adopts data mining to analyze customer features and identify root causes of churn. With a list of potential churns automatically generated at regular intervals, the bank is able to segment high risk customers and conduct precision marketing to retain them.