Systems and methods for tailoring marketing

• US 9,672,526 B2
• Filed: 03/11/2013
• Issued: 06/06/2017
• Est. Priority Date: 03/13/2012
• Status: Active Grant

• Current Assignee: American Express Travel Related Services Company, Inc. (American Express Company)
• Original Assignee: American Express Travel Related Services Company, Inc. (American Express Company)
• Inventors: Lun, Alex, Fischer, Terrence
• Primary Examiner(s): Wu, Rutao
• Assistant Examiner(s): TSUI, ALFRED H
• Application Number:
  US13/794,226
• Publication Number:
  US 20130246177A1
• Time in Patent Office: 1,548 Days
• Field of Search: None
• CPC Class Codes: G06F 21/6218 to a system of files or objects, e.g. local or distributed file system or database
• CPC Class Codes: G06Q 20/209 Specified transaction journal output feature, e.g. printed receipt or voice output
• CPC Class Codes: G06Q 20/384 using social networks
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0222 During e-commerce, i.e. online transactions
• CPC Class Codes: G06Q 30/0236 Incentive or reward received by requiring registration or ID from user
• CPC Class Codes: G06Q 30/0246 Traffic
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0267 Wireless devices
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: G06Q 50/12 Hotels or restaurants

Abstract

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.

Claims

1. A method comprising:

counting, by a computer-based system, all transactions by all consumers with a merchant during a number of days;

determining, by the computer-based system, an average transaction count for the merchant for a single day;

determining, by the computer-based system, a maximum transaction count of the merchant during any one of the number of days;

determining, by the computer-based system, a normalized popularity score for the merchant which is a quotient of the average transaction count over the maximum transaction count, wherein the normalized popularity score normalizes a popularity score among merchants with a larger number of transactions and merchants with a smaller number of transactions, and wherein a higher normalized popularity score represents a higher probability that the merchant will reach maximum capacity on any day;

ranking, by the computer-based system and in a list, the merchants with a higher normalized popularity score above the merchants with a lower normalized popularity score;

adjusting, by the computer-based system and based on the ranking, an order of the list of the merchants in response to collaborative filtering;

removing, by the computer-based system, the merchants from the list that have previously transacted with a consumer;

storing, by the computer-based system, data sets of the list of the merchants in a database as ungrouped data elements formatted as a block of binary (BLOB) via a fixed memory offset;

partitioning, by the computer-based system and using a key field, the database according to a class of objects defined by the key field to speed searching for the list of the merchants;

linking, by the computer-based system, data tables based on the type of data in the key fields;

annotating, by the computer-based system, the data sets to include security information establishing access levels;

obtaining, by the computer-based system, the list of the merchants from the database;

determining, by the computer-based system and based on a global positioning system signal (“

GPS”

) from a mobile communications device of the consumer, the merchants from the list that are outside a pre-determined distance from the mobile communications device of the consumer;

filtering, by the computer-based system, the merchants from the list that are outside the pre-determined distance from the mobile communications device of the consumer;

further adjusting, by the computer-based system, the merchants from the list that have a relationship with the computer-based system; and

providing, by the computer-based system, the list of the merchants to the mobile communications device of the consumer.

2. The method of claim 1, further comprising associating with the transaction at least one of social data, time of the transaction, date of the transaction, information about the consumer involved in the transaction or merchant feedback from the transaction.

3. The method of claim 2, further comprising adjusting, by the computer-based system and based upon the normalized popularity score, the list of the merchants that is output by a recommender system based upon a communication channel through which the consumer receives the list.

4. The method of claim 3, further comprising adjusting, by the computer-based system, the list of the merchants based on the normalized popularity score of the merchants on the list to adjust for a popularity bias based on greater capacity of the large merchants on the list.

5. The method of claim 4, further comprising including the normalized popularity score for each merchant of the merchants in the list provided to the consumer.

6. The method of claim 5, further comprising transmitting, by the computer-based system, the list of the merchants to a web client associated with the consumer, wherein the list is based on the normalized popularity score.

7. The method of claim 6, further comprising adjusting, by the computer-based system and based upon the normalized popularity score, the list of the merchants that is output by a recommender system based upon the consumer receiving the list via the mobile communication device, wherein the merchants that are within a certain distance of a location of the mobile communications device are ranked higher.

8. An article of manufacture including a non-transitory, tangible computer readable storage medium having instructions stored thereon that, in response to execution by a computer-based system configured for generating a normalized popularity score, cause the computer-based system to be capable of performing operations comprising:

counting, by the computer-based system, all transactions by all consumers with a merchant during a number of days;

determining, by the computer-based system, an average transaction count for the merchant for a single day;

determining, by the computer-based system, a maximum transaction count of the merchant during any one of the number of days;

determining, by the computer-based system, a normalized popularity score for the merchant which is a quotient of the average transaction count over the maximum transaction count, wherein the normalized popularity score normalizes a popularity score among merchants with a larger number of transactions and merchants with a smaller number of transactions, and wherein a higher normalized popularity score represents a higher probability that the merchant will reach maximum capacity on any day;

ranking, by the computer-based system and in a list, the merchants with a higher normalized popularity score above the merchants with a lower normalized popularity score;

adjusting, by the computer-based system and based on the ranking, an order of the list of the merchants in response to collaborative filtering;

removing, by the computer-based system, the merchants from the list that have previously transacted with a consumer;

storing, by the computer-based system, data sets of the list of the merchants in a database as ungrouped data elements formatted as a block of binary (BLOB) via a fixed memory offset;

partitioning, by the computer-based system and using a key field, the database according to a class of objects defined by the key field to speed searching for the list of the merchants;

linking, by the computer-based system, data tables based on the type of data in the key fields;

annotating, by the computer-based system, the data sets to include security information establishing access levels;

obtaining, by the computer-based system, the list of the merchants from the database;

determining, by the computer-based system and based on a global positioning system signal (“

GPS”

) from a mobile communications device of the consumer, the merchants from the list that are outside a pre-determined distance from the mobile communications device of the consumer;

filtering, by the computer-based system, the merchants from the list that are outside the pre-determined distance from the mobile communications device of the consumer;

further adjusting, by the computer-based system, the merchants from the list that have a relationship with the computer-based system; and

providing, by the computer-based system, the list of the merchants to the mobile communications device of the consumer.

9. The article of claim 8, further comprising associating with the transaction at least one of social data, time of the transaction, date of the transaction, information about the consumer involved in the transaction or merchant feedback from the transaction.

10. The article of claim 9, further comprising adjusting, by the computer-based system and based upon the normalized popularity score, the list of the merchants that is output by a recommender system based upon a communication channel through which the consumer receives the list.

11. The article of claim 10, further comprising adjusting, by the computer-based system, the list of the merchants based on the normalized popularity score of the merchants on the list to adjust for a popularity bias based on greater capacity of the large merchants on the list.

12. The article of claim 11, further comprising including the normalized popularity score for each merchant of the merchants in the list provided to the consumer.

13. The article of claim 12, further comprising further comprising transmitting, by the computer-based system, the list of the merchants to a web client associated with the consumer, wherein the list is based on the normalized popularity score.

14. The article of claim 13, further comprising adjusting, by the computer-based system and based upon the normalized popularity score, the list of the merchants that is output by a recommender system based upon the consumer receiving the list via the mobile communication device, wherein the merchants that are within a certain distance of a location of the mobile communications device are ranked higher.

15. A system comprising:

a processor configured for generating a normalized popularity score, a tangible, non-transitory memory configured to communicate with the processor, the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to be capable of performing operations comprising;

counting, by the processor, all transactions by all consumers with a merchant during a number of days;

determining, by the processor, an average transaction count for the merchant for a single day;

determining, by the processor, a maximum transaction count of the merchant during any one of the number of days;

determining, by the processor, a normalized popularity score for the merchant which is a quotient of the average transaction count over the maximum transaction count, wherein the normalized popularity score normalizes a popularity score among merchants with a larger number of transactions and merchants with a smaller number of transactions, and wherein a higher normalized popularity score represents a higher probability that the merchant will reach maximum capacity on any day;

ranking, by the processor and in a list, the merchants with a higher normalized popularity score above the merchants with a lower normalized popularity score;

adjusting, by the processor and based on the ranking, an order of the list of the merchants in response to collaborative filtering;

removing, by the processor, the merchants from the list that have previously transacted with a consumer;

storing, by the processor, data sets of the list of the merchants in a database as ungrouped data elements formatted as a block of binary (BLOB) via a fixed memory offset;

partitioning, by the processor and using a key field, the database according to a class of objects defined by the key field to speed searching for the list of the merchants;

linking, by the processor, data tables based on the type of data in the key fields;

annotating, by the processor, the data sets to include security information establishing access levels;

obtaining, by the processor, the list of the merchants from the database;

determining, by the processor and based on a global positioning system signal (“

GPS”

) from a mobile communications device of the consumer, the merchants from the list that are outside a pre-determined distance from the mobile communications device of the consumer;

filtering, by the processor, the merchants from the list that are outside the pre-determined distance from the mobile communications device of the consumer;

further adjusting, by the processor, the merchants from the list that have a relationship with the processor; and

providing, by the processor, the list of the merchants to the mobile communications device of the consumer.

16. The system of claim 15, further comprising associating with the transaction at least one of social data, time of the transaction, date of the transaction, information about the consumer involved in the transaction or merchant feedback from the transaction.

17. The system of claim 16, further comprising adjusting, by the processor and based upon the normalized popularity score, the list of the merchants that is output by a recommender system based upon a communication channel through which the consumer receives the list.

18. The system of claim 17, further comprising adjusting, by the processor, the list of the merchants based on the normalized popularity score of the merchants on the list to adjust for a popularity bias based on greater capacity of the large merchants on the list.

19. The system of claim 18, further comprising including the normalized popularity score for each merchant of the merchants in the list provided to the consumer.

20. The system of claim 19, further comprising further comprising transmitting, by the processor, the list of the merchants to a web client associated with the consumer, wherein the list is based on the normalized popularity score.