American Express Travel Related Services Company, Inc. - , US Lee Chau - New York NY, US Terrence Fischer - New York NY, US Anthony Harry Mavromatis - New York NY, US
Assignee:
American Express Travel Related Services Company, Inc. - New York NY
International Classification:
G06Q 30/02
US Classification:
705 1453, 705 267
Abstract:
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.
American Express Travel Related Services Company, Inc. - , US Terrence Fischer - New York NY, US Anthony Mavromatis - Brooklyn NY, US Andrew K. Smith - Hoboken NJ, US Venkat Varadachary - New York NY, US Henry Yuan - New York NY, US
Assignee:
American Express Travel Related Services Company, Inc. - New York NY
International Classification:
G06Q 30/02
US Classification:
705 1466
Abstract:
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. The method may further comprise generating a ranked list of items based upon a plurality of 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.
Terrence Fischer - New York NY, US Anthony Mavromatis - Brooklyn NY, US Venkat Varadachary - New York NY, US Michael Wang - New York NY, US
Assignee:
American Express Travel Related Services Company, Inc. - New York NY
International Classification:
G06Q 30/06
US Classification:
705 267
Abstract:
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. The method may further comprise generating a ranked list of items based upon a plurality of 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.
Systems And Methods Determining A Merchant Persona
American Express Travel Related Services Company, Inc. - , US Lee Chau - New York NY, US Terrence Fischer - New York NY, US Anthony Harry Mavromatis - Brooklyn NY, US
Assignee:
American Express Travel Related Services Company, Inc. - New York NY
International Classification:
G06Q 30/02
US Classification:
705 729
Abstract:
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.
System And Method For A Service Sentiment Indictor
- New York NY, US Terrence Fischer - New York NY, US Anthony Mavromatis - Brooklyn NY, US Venkat Varadachary - New York NY, US Michael Wang - New York NY, US
International Classification:
G06Q 30/02 G06Q 30/06 G06Q 20/20
Abstract:
At least some embodiments are directed to a system that receives data generated when point of sale terminals are used. The point of sale terminals are associated with multiple merchants. The system generates a quantitative data from the received data. The system computes a quality of service score of a first merchant based on the quantitative data. The system generates an item data structure storing values that correlate multiple users with multiple merchants at least based on the quality of service score. The system determines users that are shared by multiple merchants and computes a customer relevance value associated with the user and the first merchant. The system outputs a user-specific recommendation associated with a second merchant to a user computing device, wherein the user-specific recommendation is computed at least in part based on the customer relevance value associated with the first merchant.
Merchant Recommendations Associated With A Persona
- New York NY, US Lee Chau - New York NY, US Terrence Fischer - New York NY, US Anthony Harry Mavromatis - Brooklyn NY, US
Assignee:
American Express Travel Related Services Company, Inc. - New York NY
International Classification:
G06Q 30/06 G06Q 30/02 G06Q 50/00
Abstract:
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.
Social Media Distribution Of Offers Based On A Consumer Relevance Value
- New York NY, US TERRENCE FISCHER - New York NY, US ANTHONY MAVROMATIS - Brooklyn NY, US ANDREW K. SMITH - Hoboken NJ, US VENKAT VARADACHARY - New York NY, US HENRY YUAN - New York NY, US
Assignee:
American Express Travel Related Services Company, Inc. - New York NY
International Classification:
G06Q 30/02 G06Q 30/06 G06Q 20/20
Abstract:
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.
- New York NY, US Lee Chau - New York NY, US Terrence Fischer - New York NY, US Anthony Harry Mavromatis - Brooklyn NY, US
Assignee:
American Express Travel Related Services Company, Inc. - New York NY
International Classification:
G06Q 30/06 G06Q 50/00
Abstract:
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.
Turner (Turner Broadcasting System, Inc)
Vice President of Revenue Analytics
American Express Aug 2011 - Apr 2016
Director of Information Management, Analytics For Digital and Mobile Products
Rocket Fuel Inc, Formerly [X+1] Dec 2009 - Jul 2011
Director of Client Solutions
Digitas Jan 2006 - Dec 2009
Associate Director, Strategy and Analysis
Jpmorgan Chase & Co. 2002 - 2004
Associate, Application Development
Education:
Nyu Stern School of Business
Master of Science, Masters, Business
The Johns Hopkins University
Bachelors, Bachelor of Science, Economics, Computer Science
Skills:
Analytics Strategy Digital Media Web Analytics Digital Marketing Segmentation Hadoop Crm Marketing Strategy Digital Strategy Marketing Start Ups Big Data Business Strategy Predictive Analytics Integrated Marketing
Interests:
Nike Sportswear Oakdale The Daily Show American Express Johns Hopkins