Customer Values

New Customer Acquisition

Federated learning and privacy preserving computational modelling techniques are used to data mine for potential new users, optimizing and imporoving efficiency.


Enable banks to connect securely with third-party data sources to filter and mine their own first-party data, optimize for and the accurately deliver marketing contact to generate activation.

Customer Segment

Optimize customer segments by comparing big data from card scheme operators to refine data sets and assist banks to realise precise digital marketing.

Application Scenarios

Major commercial banks and Medium- to-small-size regional banks

An exploratory project designed to identify potential new customers with the help of third-party data, activate dormant customer segments and carry out customer segmentation for banks.

Core Functions

Federated Learning – user-acquisition marketing program
Behavioral Data Privacy Computation
Precise Digital Marketing

Successful Cases

Example of precise matching of customer segments

Configuring the data source

The financial service data for hundreds of millions of users nationwide is highly compatible with the target clientele of bank customers

Confirm user tags

Cell phone number/age/number of card subscriptions/financial consumption characteristics/Credit Card Rating/Value of financial products

Configuring the data source

Locale?/Do you have a Platinum Card?/Are you interested in financial management?/Do you participate in marketing campaigns?/Financial consumption level?

Bank Credit Card Marketing Effectiveness Comparison