American Express Travel Related Services Company, Inc.

Lee C Chau

Anthony Mavromatis

Terrence Fischer

Venkat Varadachary

Michael Wang

Ching-nu Chang

Tirthankar Choudhuri

Subhajit Sanyal

Subhra Shankar Purkayastha

Kamal Gupta

Andrew K Smith

Dawn Thomas

Vikas Grover

Ajay Choudhary

Henry Yuan

Amit Jagdish Agarwal

Pranav Mehta

Prakruthi Prabhakar

Mohd Arshad Naeem

Glade Raymond Erikson

Desmond Liang

Nora Maude Hendrycks

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 and methods described herein may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input at least one of: a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, a share of wallet, and/or the like.

Social media distribution of offers based on a consumer relevance value

The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

System and method for a relative consumer cost

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.

System and method for an estimated consumer price

The method of processing an analysis cycle to determine interest merchants may include selecting a seed merchant relevant to a topic interest, identifying consumers that have completed a transaction with the seed merchant to generate a list of identified consumers, determining merchants visited by the identified consumers, scoring all the merchants based on network connectivity, activity, and merchant over-index, updating the seed merchant in response to the list of scored merchants relative to a scoring threshold, and scoring the list of identified consumers based on the number of distinct merchants in transaction and over-indexing. Additionally, the method may further comprise producing a list of updated interest merchants and a list of updated identified consumers, where the updated interest merchants and the updated identified consumers are relevant to the topic interest.

Merchant recommendations associated with a persona

Determining merchant recommendations

Systems and methods determining a merchant persona

The disclosed system enables merchants to ship products to a predefined address that may or may not be an account holder's billing address. The account holder may define one or more alias identifiers that correspond to a predefined shipping address. The predefined shipping address is stored with a corresponding alias identifier. A merchant order form includes a field to accept an alias identifier, such that when the order form is submitted to the merchant and the merchant prepares a purchase authorization request, the alias identifier is included in the request. The credit account issuer receives the payment authorization request and extracts the alias identifier, which is used to locate a matching stored alias identifier. A shipping address corresponding to the stored alias identifier is retrieved and returned to the merchant with an authorization message.

System and method for facilitating a secured financial transaction using an alternate shipping address

Growing community of inventors

Livingston, NJ, United States of America

Lee C Chau