American Express Travel Related Services Company, Inc.

Subhra Shankar Purkayastha

Susana Sung-hye Kim

Jill Zucker

Abhinav K Kala

Tirthankar Choudhuri

Lee C Chau

Subhajit Sanyal

Kamal Gupta

Dawn Thomas

Mohd Arshad Naeem

Vikas Grover

Ajay Choudhary

Amit Jagdish Agarwal

Pranav Mehta

Prakruthi Prabhakar

The present disclosure presents systems and related methods for creating real-time predictions. One such method comprises receiving, by a computing device, a first set of data and a second set of data, wherein the first set of data comprises a plurality of items available from a first source for a first set of users and the second set of data comprises transaction purchase data for a second set of users that have reward accounts, utilizing a predictive data model that determines a propensity score for a user from only behavior data that is not attributed to the user; receiving a third set of data from a third source comprising social media channel data for a third set of users; and updating the predictive data model to determine the propensity score for the user based at least in part on the third set of data.

Systems and methods for tailoring marketing

The systems, methods and computer program products (collectively 'system') described herein relate to customized real time data delivery. The system may be configured to receive, by a performance marketing cluster, first data from a first data source. The system may also receive, by the performance marketing cluster, second data from a second data source. The system may determine, by the marketing cluster, an analysis scheme for the first data and the second data based on the first data source. The system may also determine, by the marketing cluster, at least one of a propensity to act or a recommendation selected from a predefined number of available options for a population based on the analysis scheme and the first data source.

Utilization of information in a closed loop data system further augments modeling while at the same time enabling customization of offers based on spend patterns. Data mining techniques are leveraged to identify rules to determine higher response rate populations. These rules are referred to herein as 'triggers,' in that the presence of particular attributes will trigger a cardholder as being more likely to respond to a particular offer. The benefit yielded by this approach is a greater acceptance rate to an offer provided by a transactional account company. To identify the triggers, records of cardmembers who already utilize a given product are analyzed to determine their spend patterns. The spend histories of customers who are eligible to use the product are analyzed according to the identified triggers. Customers whose spend patterns most closely correspond to the triggers are then targeted with offers for the given product.

System and method for targeting family members of transaction account product holders to receive supplementary transaction account products

System and method for targeting transaction account product holders to receive upgraded transaction account products

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Haryana, India

Subhra Shankar Purkayastha