Systems and methods for tailoring marketing

• US 9,697,529 B2
• Filed: 03/11/2013
• Issued: 07/04/2017
• 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: Smith, Andrew K., Fischer, Terrence, Wang, Michael
• Primary Examiner(s): Wu, Rutao
• Assistant Examiner(s): TSUI, ALFRED H
• Application Number:
  US13/794,334
• Publication Number:
  US 20130246178A1
• Time in Patent Office: 1,576 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:

analyzing, by a tailoring marketing computer-based system, transaction data associated with a consumer, wherein the transaction data is acquired from a merchant server;

determining, by the computer-based system and based on the analyzing, a consumer preference associated with a type of merchant,

determining, by the computer-based system, a consumer relevance value associated with a merchant based upon the consumer preference, an industry of the merchant, a consumer profile, a transaction history associated with the consumer, social data, demographic data, clickstream data, consumer feedback data and a plurality of merchant to merchant similarity values,

wherein the merchant to merchant similarity value is based on pairings of merchants at least one of occurring most often or are most strongly correlated,

wherein the merchant to merchant similarity value is determined by comparing record of charges (ROCs) of a plurality of consumers at the plurality of merchants,

wherein the merchant to merchant similarity is a result of ROCs by more than one consumer at the plurality of merchants,

wherein the merchant to merchant similarity shows a tendency by the more than one consumer to conduct transactions at the plurality of merchants,

wherein the merchant to merchant similarity between a first merchant and the plurality of merchants is a result of ROCs by a first consumer at a first merchant and ROCs by a second consumer at the plurality of merchants including the first merchant,

wherein a first merchant to merchant similarity between a third merchant and the first merchant and a second merchant to merchant similarity between the third merchant and a second merchant is not as strong of a similarity as between the first merchant and the second merchant from whom each consumer conducted a transaction,

storing, by the computer-based system, data sets of the consumer relevance value 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 consumer relevance value;

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 consumer relevance value from the database;

generating, by the computer-based system and in response to determining the consumer relevance value, a merchant matrix having coefficients indicating that the merchants are associated,

wherein each of the coefficients includes a record of charge (ROC) of the consumer with the merchant,

wherein the ROC indicates the consumer conducted a transaction with the merchant,

wherein the ROC includes a number of transactions the consumer conducted with the merchant,

wherein the number of transactions is capped at a maximum value,

adjusting, by the computer-based system, the consumer relevance value based on the association between merchants from the merchant matrix, the coefficients, a collaborative filtering, the merchant goal and a business rule,

wherein the merchant goal is at least one of acquiring only new consumers, tailoring existing consumers of the merchant, or tailoring all consumers, and

wherein the business rule is at least one of a holiday, a particular time of day, the consumer is traveling, the merchant is associated with another merchant who is a particular distance away or near to a location of the consumer, the consumer has indicated a preference not to receive the offers associated with the merchant;

associating, by the computer-based system, criteria with the merchant;

obtaining, by the computer-based system, social data from a social media website about the merchant;

comparing, by the computer-based system and based on matching rules, the criteria with the social data from the social media website about the merchant to determine a social media association between the criteria for the merchant and the social media data about the merchant;

providing, by the computer-based system, a higher ranking for the merchant based on the social media association and the consumer relevance value;

transmitting, by the computer-based system and in response to the adjusting, via an app and based on the consumer preference, information associated with the merchant to a web client associated with the consumer;

monitoring, by the computer-based system, real time transaction information associated with the consumer;

receiving, by the computer-based system, a request for a recommendation for the plurality of merchants from the consumer device;

adjusting, by the computer-based system, the consumer relevance value of the merchant based on the real time transaction information and in response to the request for the recommendation;

ranking, by the computer-based system, the plurality of merchants in a ranked list based on the real time transaction information; and

transmitting, by the computer-based system, the ranked list in real time and in response to the request for the recommendation.

2. The method of claim 1, wherein the consumer preference is further based on at least one of industry codes associated with the transaction data, transactions with merchants in similar industry groups, groups of similar merchants, mapping third party data to internal data, a consumer profile, social data, time information, date information, or feedback.

3. The method of claim 2, wherein the consumer preference is based on categorization associated with the merchant.

4. The method of claim 3, further comprising up-ranking, by the computer-based system, the merchant in a ranked list of merchants based upon the consumer preference.

5. The method of claim 4, wherein the merchants are associated based on at least one of a consumer with the merchant, the merchant with an interest, a consumer with a demographic, the merchant with a demographic, a consumer with another consumer, a consumer'"'"'s profile with another consumer'"'"'s profile, a share of wallet with a consumer, a size of wallet with a consumer, an age attribute with a consumer, a gender with a consumer, or a favorite cuisine with a consumer.

6. The method of claim 5, further comprising recommending, by the computer-based system, the merchant based upon the consumer relevance value.

7. The method of claim 1, further comprising determining, by the computer-based system and by using at least one of a co-occurrence method or a cosine method, the association between merchants in the merchant matrix.

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 tailoring marketing computer-based system, cause the computer-based system to be capable of performing operations comprising:

analyzing, by the computer-based system, transaction data associated with a consumer, wherein the transaction data is acquired from a merchant server;

determining, by the computer-based system and based on the analyzing, a consumer preference associated with a type of merchant,

determining, by the computer-based system, a consumer relevance value associated with a merchant based upon the consumer preference, an industry of the merchant, a consumer profile, a transaction history associated with the consumer, social data, demographic data, clickstream data, consumer feedback data and a plurality of merchant to merchant similarity values,

wherein the merchant to merchant similarity value is based on pairings of merchants at least one of occurring most often or are most strongly correlated,

wherein the merchant to merchant similarity value is determined by comparing record of charges (ROCs) of a plurality of consumers at the plurality of merchants,

wherein the merchant to merchant similarity is a result of ROCs by more than one consumer at the plurality of merchants,

wherein the merchant to merchant similarity shows a tendency by the more than one consumer to conduct transactions at the plurality of merchants,

wherein the merchant to merchant similarity between a first merchant and the plurality of merchants is a result of ROCs by a first consumer at a first merchant and ROCs by a second consumer at the plurality of merchants including the first merchant,

wherein a first merchant to merchant similarity between a third merchant and the first merchant and a second merchant to merchant similarity between the third merchant and a second merchant is not as strong of a similarity as between the first merchant and the second merchant from whom each consumer conducted a transaction,

storing, by the computer-based system, data sets of the consumer relevance value 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 consumer relevance value;

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 consumer relevance value from the database;

generating, by the computer-based system and in response to determining the consumer relevance value, a merchant matrix having coefficients indicating that the merchants are associated,

wherein each of the coefficients includes a record of charge (ROC) of the consumer with the merchant,

wherein the ROC indicates the consumer conducted a transaction with the merchant,

wherein the ROC includes a number of transactions the consumer conducted with the merchant,

wherein the number of transactions is capped at a maximum value,

adjusting, by the computer-based system, the consumer relevance value based on the association between merchants from the merchant matrix, the coefficients, a collaborative filtering, the merchant goal and a business rule,

wherein the merchant goal is at least one of acquiring only new consumers, tailoring existing consumers of the merchant, or tailoring all consumers, and

wherein the business rule is at least one of a holiday, a particular time of day, the consumer is traveling, the merchant is associated with another merchant who is a particular distance away or near to a location of the consumer, the consumer has indicated a preference not to receive the offers associated with the merchant;

associating, by the computer-based system, criteria with the merchant;

obtaining, by the computer-based system, social data from a social media website about the merchant;

comparing, by the computer-based system and based on matching rules, the criteria with the social data from the social media website about the merchant to determine a social media association between the criteria for the merchant and the social media data about the merchant;

providing, by the computer-based system, a higher ranking for the merchant based on the social media association and the consumer relevance value;

transmitting, by the computer-based system and in response to the adjusting, via an app and based on the consumer preference, information associated with the merchant to a web client associated with the consumer;

monitoring, by the computer-based system, real time transaction information associated with the consumer;

receiving, by the computer-based system, a request for a recommendation for the plurality of merchants from the consumer device;

adjusting, by the computer-based system, the consumer relevance value of the merchant based on the real time transaction information and in response to the request for the recommendation;

ranking, by the computer-based system, the plurality of merchants in a ranked list based on the real time transaction information; and

transmitting, by the computer-based system, the ranked list in real time and in response to the request for the recommendation.

9. The article of claim 8, wherein the consumer preference is further based on at least one of industry codes associated with the transaction data, transactions with merchants in similar industry groups, groups of similar merchants, mapping third party data to internal data, a consumer profile, social data, time information, date information, or feedback.

10. The article of claim 9, wherein the consumer preference is based on a categorization associated with the merchant.

11. The article of claim 10, wherein the consumer preference is associated with a favorite merchant of the consumer.

12. The article of claim 11, further comprising up-ranking, by the computer-based system, the merchant in a ranked list of merchants based upon the consumer preference.

13. The article of claim 12, wherein the consumer preference is associated with a favorite cuisine of the consumer.

14. The article of claim 13, further comprising recommending, by the computer-based system, the merchant based upon the consumer relevance value.

15. A system comprising:

a tailoring marketing processor,

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;

analyzing, by the processor, transaction data associated with a consumer, wherein the transaction data is acquired from a merchant server;

determining, by the processor and based on the analyzing, a consumer preference associated with a type of merchant,

determining, by the processor, a consumer relevance value associated with a merchant based upon the consumer preference, an industry of the merchant, a consumer profile, a transaction history associated with the consumer, social data, demographic data, clickstream data, consumer feedback data and a plurality of merchant to merchant similarity values,

wherein the merchant to merchant similarity value is based on pairings of merchants at least one of occurring most often or are most strongly correlated,

wherein the merchant to merchant similarity value is determined by comparing record of charges (ROCs) of a plurality of consumers at the plurality of merchants,

wherein the merchant to merchant similarity is a result of ROCs by more than one consumer at the plurality of merchants,

wherein the merchant to merchant similarity shows a tendency by the more than one consumer to conduct transactions at the plurality of merchants,

wherein the merchant to merchant similarity between a first merchant and the plurality of merchants is a result of ROCs by a first consumer at a first merchant and ROCs by a second consumer at the plurality of merchants including the first merchant,

wherein a first merchant to merchant similarity between a third merchant and the first merchant and a second merchant to merchant similarity between the third merchant and a second merchant is not as strong of a similarity as between the first merchant and the second merchant from whom each consumer conducted a transaction,

storing, by the processor, data sets of the consumer relevance value 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 consumer relevance value;

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 consumer relevance value from the database;

generating, by the processor and in response to determining the consumer relevance value, a merchant matrix having coefficients indicating that the merchants are associated,

wherein each of the coefficients includes a record of charge (ROC) of the consumer with the merchant,

wherein the ROC indicates the consumer conducted a transaction with the merchant,

wherein the ROC includes a number of transactions the consumer conducted with the merchant,

wherein the number of transactions is capped at a maximum value,

adjusting, by the processor, the consumer relevance value based on the association between merchants from the merchant matrix, the coefficients, a collaborative filtering, the merchant goal and a business rule,

wherein the merchant goal is at least one of acquiring only new consumers, tailoring existing consumers of the merchant, or tailoring all consumers, and

wherein the business rule is at least one of a holiday, a particular time of day, the consumer is traveling, the merchant is associated with another merchant who is a particular distance away or near to a location of the consumer, the consumer has indicated a preference not to receive the offers associated with the merchant;

associating, by the processor, criteria with the merchant;

obtaining, by the processor, social data from a social media website about the merchant;

comparing, by the processor and based on matching rules, the criteria with the social data from the social media website about the merchant to determine a social media association between the criteria for the merchant and the social media data about the merchant;

providing, by the processor, a higher ranking for the merchant based on the social media association and the consumer relevance value;

transmitting, by the processor and in response to the adjusting, via an app and based on the consumer preference, information associated with the merchant to a web client associated with the consumer;

monitoring, by the processor, real time transaction information associated with the consumer;

receiving, by the processor, a request for a recommendation for the plurality of merchants from the consumer device;

adjusting, by the processor, the consumer relevance value of the merchant based on the real time transaction information and in response to the request for the recommendation;

ranking, by the processor, the plurality of merchants in a ranked list based on the real time transaction information; and

transmitting, by the processor, the ranked list in real time and in response to the request for the recommendation.

16. The system of claim 15, wherein the consumer preference is further based on at least one of industry codes associated with the transaction data, transactions with merchants in similar industry groups, groups of similar merchants, mapping third party data to internal data, a consumer profile, social data, time information, date information, or feedback.

17. The system of claim 16, wherein the consumer preference is based on a categorization associated with the merchant.

18. The system of claim 17, wherein the consumer preference is associated with a favorite merchant of the consumer.

19. The system of claim 18, further comprising up-ranking, by the processor, the merchant in a ranked list of merchants based upon the consumer preference.

20. The system of claim 19, wherein the consumer preference is associated with a favorite cuisine of the consumer.

21. The system of claim 20, further comprising recommending, by the processor, the merchant based upon the consumer relevance value.