MERCHANT AGGREGATION THROUGH CARDHOLDER BRAND LOYALTY

• US 20150006358A1
• Filed: 07/01/2013
• Published: 01/01/2015
• Est. Priority Date: 07/01/2013
• Status: Abandoned Application

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Oshry, Steven Bruce, Howe, Justin Xavier
• Application Number:
  US13/932,560
• Publication Number:
  US 20150006358A1
• Time in Patent Office: Days
• US Class Current: 705/39
• CPC Class Codes: G06Q 20/202 Interconnection or interaction of plural electronic cash registers [ECR] or to host computer, e.g. network details, transfer of information from host to ECR or from ECR to ECR
• CPC Class Codes: G06Q 20/3278 RFID or NFC payments by means of M-devices
• CPC Class Codes: G06Q 20/34 using cards, e.g. integrated circuit [IC] cards or magnetic cards
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A system and method of aggregating merchant data from transaction data, including retrieving a transaction data set from a data warehouse. The transaction data set includes a merchant location identifier and the corresponding merchant'"'"'s Doing Business As (DBA) name and address data. A data set is then formed from the transaction data, having merchant locations exhibiting at least a threshold level of common cardholder patronage. A metric is calculated related to the textual similarity between a merchant location'"'"'s DBA name for each pair of merchant locations within the data set. Each pair of merchant locations having a metric related to the textual similarity between the merchant locations'"'"' DBA names that exceeds a predetermined threshold are aggregated with each other, where the merchant locations making up the pair do not share an address. The aggregation between merchant locations is recorded in the data warehouse.

Claims

1. A method of aggregating merchant data from transaction data, the method comprising:

retrieving a transaction data set from a data warehouse, the transaction data set including a merchant location identifier and the corresponding merchant'"'"'s Doing Business As (DBA) name and address data;

forming a data set having therein merchant locations exhibiting at least a threshold level of common cardholder patronage;

calculating a metric related to the textual similarity between a merchant location'"'"'s DBA name for each pair of merchant locations within the data set;

responsive to each pair of merchant locations having a metric related to the textual similarity between the merchant locations'"'"' DBA names exceeding a predetermined threshold, aggregating the merchant locations making up the pair with each other where the merchant locations making up the pair do not share an address; and

recording the aggregation between merchant locations in the data warehouse.

2. The method according to claim 1, further comprising pre-processing the merchant DBA name to remove common, generic or descriptive terms.

3. The method according to claim 2, wherein the common, generic or descriptive terms removed from the merchant DBA name are related to the goods or services sold by the merchant, or to the geographic location of the merchant.

4. The method according to claim 1, wherein the transaction data set comprises transactions occurring within at least one of a predetermined time period, a predetermined geographical location, and involving predetermined merchant characteristics.

5. The method according to claim 1, further comprising graphically representing the transaction data set as an interconnected network, wherein the merchant locations correspond to nodes of the network, and the nodes are connected by edges which correspond to at least one cardholder patronizing the merchant location nodes on either side of the edge.

6. The method according to claim 1, wherein the threshold level of common cardholder patronage is related to at least one of a number of cardholders patronizing both merchant locations of a pair, a number of transactions with both merchants each cardholder makes, a percentage of common cardholders as a portion of the client base for each connected merchant location independently, the product of the percentage of cardholders overlapping from each location independently, or some combination of these.

7. The method according to claim 1, wherein the metric related to the textual similarity between the merchant locations'"'"' DBA names comprises at least one of inverse document frequency measurement, Levenshtein Distance, a Soundex comparison, and a value related to each common substring of any length between the respective merchant locations'"'"' DBA names.

8. The method according to claim 1, further comprising identifying for aggregation merchant locations which are constituents of a fully connected subgraph.

9. A system for aggregating merchant data from transaction data, the system comprising:

a processor;

a non-transitory machine-readable storage medium, storing thereon a program of instruction that, when executed by the processor, causes the processor to carry out a method including

retrieving transaction data set from a data warehouse, the transaction data set including a merchant location identifier and the corresponding merchant'"'"'s Doing Business As (DBA) name and address data;

forming a data set having therein merchant locations exhibiting at least a threshold level of common cardholder patronage;

calculating a metric related to the textual similarity between a merchant location'"'"'s DBA name for each pair of merchant locations within the data set;

responsive to each pair of merchant locations having a metric related to the textual similarity between the merchant locations'"'"' DBA names exceeding a predetermined threshold, aggregating the merchant locations making up the pair with each other where the merchant locations making up the pair do not share an address; and

recording the aggregation between merchant locations in the data warehouse.

10. The system according to claim 9, wherein the a program of instruction that, when executed by the processor, further causes the processor to

pre-process the merchant DBA name to remove common, generic or descriptive terms.

11. The system according to claim 10, wherein the common, generic or descriptive terms removed from the merchant DBA name are related to the goods or services sold by the merchant, or to the geographic location of the merchant.

12. The system according to claim 9, wherein the transaction data set comprises transactions occurring within at least one of a predetermined time period, a predetermined geographical location, and involving predetermined merchant characteristics.

13. The system according to claim 9, wherein the a program of instruction that, when executed by the processor, further causes the processor to

graphically represent the transaction data set as an interconnected network, wherein the merchant locations correspond to nodes of the network, and the nodes are connected by edges which correspond to at least one cardholder patronizing the merchant location nodes on either side of the edge.

14. The system according to claim 9, wherein the threshold level of common cardholder patronage is related to at least one of a number of cardholders patronizing both merchant locations of a pair, a number of transactions with both merchants each cardholder makes, a percentage of common cardholders as a portion of the client base for each connected merchant location independently, the product of the percentage of cardholders overlapping from each location independently, or some combination of these.

15. The system according to claim 9, wherein the metric related to the textual similarity between the merchant locations'"'"' DBA names comprises at least one of inverse document frequency measurement, Levenshtein Distance, a Soundex comparison, and an value related to each common substring of any length between the respective merchant locations'"'"' DBA names.

16. The system according to claim 9, wherein the a program of instruction that, when executed by the processor, further causes the processor to

identify for aggregation merchant locations which are constituents of a fully connected subgraph.

17. A non-transitory machine-readable storage medium, storing thereon a program of instruction that, when executed by a processor, causes the processor to carry out a method including

retrieving transaction data set from a data warehouse, the transaction data set including a merchant location identifier and the corresponding merchant'"'"'s Doing Business As (DBA) name and address data;

forming a data set having therein merchant locations exhibiting at least a threshold level of common cardholder patronage;

calculating a metric related to the textual similarity between a merchant location'"'"'s DBA name for each pair of merchant locations within the data set;

responsive to each pair of merchant locations having a metric related to the textual similarity between the merchant locations'"'"' DBA names exceeding a predetermined threshold, aggregating the merchant locations making up the pair with each other where the merchant locations making up the pair do not share an address; and

recording the aggregation between merchant locations in the data warehouse.

18. The non-transitory machine-readable storage medium according to claim 17, wherein the a program of instruction that, when executed by the processor, further causes the processor to

pre-process the merchant DBA name to remove common, generic or descriptive terms.

19. The non-transitory machine-readable storage medium according to claim 18, wherein the common, generic or descriptive terms removed from the merchant DBA name are related to the goods or services sold by the merchant, or to the geographic location of the merchant.

20. The non-transitory machine-readable storage medium according to claim 17, wherein the transaction data set comprises transactions occurring within at least one of a predetermined time period, a predetermined geographical location, and involving predetermined merchant characteristics.

21. The non-transitory machine-readable storage medium according to claim 17, wherein the a program of instruction that, when executed by the processor, further causes the processor to

graphically represent the transaction data set as an interconnected network, wherein the merchant locations correspond to nodes of the network, and the nodes are connected by edges which correspond to at least one cardholder patronizing the merchant location nodes on either side of the edge.

22. The non-transitory machine-readable storage medium according to claim 17, wherein the threshold level of common cardholder patronage is related to at least one of a number of cardholders patronizing both merchant locations of a pair, a number of transactions with both merchants each cardholder makes, a percentage of common cardholders as a portion of the client base for each connected merchant location independently, the product of the percentage of cardholders overlapping from each location independently, or some combination of these.

23. The non-transitory machine-readable storage medium according to claim 17, wherein the metric related to the textual similarity between the merchant locations'"'"' DBA names comprises at least one of inverse document frequency measurement, Levenshtein Distance, a Soundex comparison, and an value related to each common substring of any length between the respective merchant locations'"'"' DBA names.

24. The non-transitory machine-readable storage medium according to claim 17, wherein the a program of instruction that, when executed by the processor, further causes the processor to

identify for aggregation merchant locations which are constituents of a fully connected subgraph.