Real-time profiles

Improve your revenue by enhancing customer profiling and data federation.


A payment service provider serves more than sixty thousand online stores (merchants) and has the ability to serve its own advertisements to individuals making purchases. To increase the effectiveness of these ads, the provider plans to implement a profiling process for individual customers making purchases.

The quality of profiling is proportional to the amount and scope of transaction data available. Currently, the provider only has basic information about transaction amounts and sales points. However, by collaborating with merchants and federating data from their shopping baskets, the provider can significantly increase the data set and improve profiling accuracy. This data can also be used to enhance the quality of recommendations used by merchants.

The provider plans to use this data not only for its own purposes but also as a service for merchants, differentiating itself in the market and increasing revenue. Allowing the provider to use their data to provide better recommendations does not affect merchants’ competitive position in the ecosystem. In fact, it increases their ability to increase the expected basket value.

The challenge is to build a platform that allows for data federation without losing ownership of the data and complying with privacy regulations. Additionally, the platform should allow for managed access to purchasing profiles and real-time recommendations by tens of thousands of online merchants.


The payment service provider has developed anonymous customer profiles stored externally on Trusted Twin, a data collaboration platform. These profiles aggregate data from participating merchants in real-time, immediately after each transaction. The platform ensures data security by allowing individual merchants to maintain ownership and control of their transferred data.

All sensitive data in the profiles undergoes a process of pseudoanonymization based on keyed hashing using strong cryptographic functions. This method ensures compliance with legal requirements related to GDPR. Merchants who use the service do not have access to sensitive data of other merchants or customers they cannot identify. They can only verify whether their customers are in the federated database and access data according to their permission levels.

The payment service provider is responsible for applying analytics to the federated, anonymized data and can manage access to the processed data in the profiles via access rules. This is a standard function of the Trusted Twin platform and can be done in real-time, with granularity at not only the profile level, but even at the level of a single entry within a profile, thanks to the unique architecture and data model of Trusted Twin.

The platform separates the traffic generated by specific partners, allowing for high scalability and real-time access. This also opens up opportunities for new services, such as real-time access to the profile of a person calling the contact center.


By using an external platform to store and share data, the payment service provider gained a competitive advantage by introducing a new service to its customers. The implementation took less than three months, without requiring the building of dedicated infrastructure or maintaining multiple integrations. The provider didn’t have to worry about scalability and availability, even as the number of registered transactions increased from thousands to millions per month.

This service is available to all participating merchants who wish to become part of the ecosystem. In exchange for their data, they provide better recommendations to their customers, which significantly increases their baskets size. Furthermore, merchants can choose to stop using the system at any time and withdraw the data they provided previously.

Let's discuss how Trusted Twin can enable data collaboration for your business.