Determining merchant recommendations

• US 10,192,256 B2
• Filed: 04/26/2016
• Issued: 01/29/2019
• 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: Chang, Ching-nu, Chau, Lee, Fischer, Terrence, Mavromatis, Anthony Harry
• Primary Examiner(s): Sterrett, Jonathan G
• Application Number:
  US15/139,176
• Publication Number:
  US 20160253737A1
• Time in Patent Office: 1,008 Days
• 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 30/0255 based on user history
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking

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.

Claims

1. A method, comprising:

creating, by a computer based system, a group containing a plurality of merchants based on a common trait within a time period,

wherein the common trait includes at least one of a keyword association, similar industry code, criteria, items sold, offers, behaviors, or transaction information;

determining, by the computer based system, a plurality of consumer transaction accounts that were used to initiate transactions at the plurality of merchants;

assigning, by the computer based system, a score to each of the plurality of merchants based on connectivity, activity, common consumers and merchant over-index,

wherein the merchant over-index includes a ratio of consumers with the transactions at a merchant compared to a baseline population of the consumers;

removing, by the computer based system and from the group, a portion of the plurality of merchants with the score below a threshold;

selecting, by the computer based system, an interest,

wherein the interest is associated with a persona, and

wherein at least one of the interest or the persona include parameters that define the at least one of the interest or the persona;

evaluating, by the computer based system, the group containing the plurality of merchants to determine a seed merchant based on the interest;

determining, by the computer based system, a pool of merchants comprising a first subset of merchants that are associated with the seed merchant and a second subset of merchants of which the seed customer has transacted;

receiving, by the computer based system and from a consumer, a request for recommendations of merchants from the pool of merchants based on the persona presented to the consumer;

detecting, by the computer based system via a real time system, a location of a mobile device of the consumer, in response to the receiving the request for recommendations from the mobile device of the consumer;

monitoring, by the computer based system and via the real time system, the location of the mobile device of the consumer;

adjusting, in real time and by the computer based system via the real time system, a list of recommended merchants associated with the persona based on at least one of the monitoring the location of the mobile device of the consumer, a time of day, or a percentage of the transactions at a meal-time exceeding a percentage of overall transactions;

presenting, by the computer based system and via the real time system and to the mobile device, the list of recommended merchants in response to the adjusting; and

associating, by the computer based system, a consumer profile with the persona and in response to the request for recommendations of the merchants from the consumer.

2. The method of claim 1, wherein the keyword association is contained in names of the plurality of merchants.

3. The method of claim 1, wherein the connectivity is based on a z-score, and wherein the z-score includes a number of distinct merchants connected by a common customer, reduced by an average of distinct merchants connected by the common customer, then divided by a standard deviation.

4. The method of claim 1, wherein the interest is determined based on a trend in at least one of transaction data or a trend in social data.

5. The method of claim 1, wherein the seed merchant has a merchant profile with a first plurality of attributes that at least partially match the parameters associated with the at least one of the interest or the persona, and wherein the seed customer has the customer profile that includes a second plurality of attributes that at least partially match the parameters associated with the at least one of the interest or the persona.

6. The method of claim 1, wherein each of the plurality of merchants has a corresponding merchant profile that includes a plurality of attributes, and wherein the plurality of attributes are compared to the parameters associated with the at least one of the interest or the persona.

7. The method of claim 1, further comprising defining, by the computer based system, the interest based on at least one of transaction data or social data, wherein the social data is at least one of a hashtag, an interest, or a keyword.

8. The method of claim 1, further comprising performing, by the computer based system, an additional analysis cycle in response to a turnover index being greater than a turnover index threshold.

9. The method of claim 1, further comprising producing, by the computer based system, a list of updated merchants and a list of updated consumers, wherein the updated merchants and the updated consumers are relevant to the interest.

10. The method of claim 1, further comprising determining, by the computer based system, a group of boost consumers, wherein the determining the group of boost consumers comprises:

evaluating the transactions across a subset of the pool of merchants associated with the interest, wherein the subset of the pool of merchants have a score above of threshold established for the interest; and

retrieving the consumers that have the transactions with two or more distinct merchants from a list of updated merchants to create the group of boost consumers.

11. The method of claim 1, wherein the interest is defined by:

a list of initial merchants retrieved from a merchant database, wherein the list of initial merchants result from at least one of the keyword association, industry codes, or explicit queries; and

the consumer having transaction data and social data associated with a keyword associated with the interest.

12. The method of claim 1, further comprising:

normalizing, by the computer based system, the scoring, based on a set of at least one of the merchants or the consumers; and

ranking, by the computer based system, the pool of merchants based on the normalizing.

13. 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, cause the computer based system to perform operations comprising:

creating, by the computer based system, a group containing a plurality of merchants based on a common trait within a time period,

wherein the common trait includes at least one of a keyword association, similar industry code, criteria, items sold, offers, behaviors, or transaction information;

determining, by the computer based system, a plurality of consumer transaction accounts that were used to initiate transactions at the plurality of merchants;

assigning, by the computer based system, a score to each of the plurality of merchants based on connectivity, activity, common consumers and merchant over-index,

wherein the merchant over-index includes a ratio of consumers with the transactions at a merchant compared to a baseline population of the consumers;

removing, by the computer based system and from the group, a portion of the plurality of merchants with the score below a threshold;

selecting, by the computer based system, an interest,

wherein the interest is associated with a persona, and

wherein at least one of the interest or the persona include parameters that define the at least one of the interest or the persona;

evaluating, by the computer based system, the group containing the plurality of merchants to determine a seed merchant based on the interest;

determining, by the computer based system, a pool of merchants comprising a first subset of merchants that are associated with the seed merchant and a second subset of merchants of which the seed customer has transacted;

receiving, by the computer based system and from a consumer, a request for recommendations of merchants from the pool of merchants based on the persona presented to the consumer;

detecting, by the computer based system via a real time system, a location of a mobile device of the consumer, in response to the receiving the request for recommendations from the mobile device of the consumer;

monitoring, by the computer based system and via the real time system, the location of the mobile device of the consumer;

adjusting, in real time and by the computer based system via the real time system, a list of recommended merchants associated with the persona based on at least one of the monitoring the location of the mobile device of the consumer, a time of day, or a percentage of the transactions at a meal-time exceeding a percentage of overall transactions;

presenting, by the computer based system and via the real time system and to the mobile device, the list of recommended merchants in response to the adjusting; and

associating, by the computer based system, a consumer profile with the persona and in response to the request for recommendations of the merchants from the consumer.

14. A system comprising:

a processor; and

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 perform operations comprising;

creating, by the processor, a group containing a plurality of merchants based on a common trait within a time period,

wherein the common trait includes at least one of a keyword association, similar industry code, criteria, items sold, offers, behaviors, or transaction information;

determining, by the processor, a plurality of consumer transaction accounts that were used to initiate transactions at the plurality of merchants;

assigning, by the processor, a score to each of the plurality of merchants based on connectivity, activity, common consumers and merchant over-index,

wherein the merchant over-index includes a ratio of consumers with the transactions at a merchant compared to a baseline population of the consumers;

removing, by the processor and from the group, a portion of the plurality of merchants with the score below a threshold;

selecting, by the processor, an interest,

wherein the interest is associated with a persona, and

wherein at least one of the interest or the persona include parameters that define the at least one of the interest or the persona;

evaluating, by the processor, the group containing the plurality of merchants to determine a seed merchant based on the interest;

determining, by the processor, a pool of merchants comprising a first subset of merchants that are associated with the seed merchant and a second subset of merchants of which the seed customer has transacted;

receiving, by the processor and from a consumer, a request for recommendations of merchants from the pool of merchants based on the persona presented to the consumer;

detecting, by the processor via a real time system, a location of a mobile device of the consumer, in response to the receiving the request for recommendations from the mobile device of the consumer;

monitoring, by the processor and via the real time system, the location of the mobile device of the consumer;

adjusting, in real time and by the processor via the real time system, a list of recommended merchants associated with the persona based on at least one of the monitoring the location of the mobile device of the consumer, a time of day, or a percentage of the transactions at a meal-time exceeding a percentage of overall transactions;

presenting, by the processor and via the real time system and to the mobile device, the list of recommended merchants in response to the adjusting; and

associating, by the processor, a consumer profile with the persona and in response to the request for recommendations of the merchants from the consumer.