Patent Publication Number: US-2015081387-A1

Title: Method and system for identifying influencers from payment data

Description:
FIELD 
     The present disclosure relates to the identification of influential consumers using payment data, specifically identifying consumers who are ahead of commercial trends as trendsetters based on payment transaction data. 
     BACKGROUND 
     Many people and entities closely watch consumer trends. Some consumers pay attention to trends in an effort to do or wear what is considered “hot.” Merchants and manufacturers often keep apprised of trends in order to develop future products, advertise, and reach out to consumers. However, these entities can often not identify a trend until after it has gained significant traction as a trend. As a result, efforts taken to capitalize on the trend may not be as fruitful as they could be if the efforts were taken earlier. Thus, there is a need for a technical solution to provide earlier identification of trends. 
     Some consumers that are at the forefront of a particular trend may be well-known, such as actors, actresses, and sports figures. However, there may be a number of other consumers that are trendsetters that are not easily identifiable using traditional ways, such as human observation. Identification of these consumers as trendsetters may enable an entity to quickly identify new trends based on the actions of these trendsetting consumers. Thus, there is a further need for a technical solution to provide identification of these trendsetters, in order to achieve earlier identification of trends. 
     SUMMARY 
     The present disclosure provides a description of systems and methods for identifying a trendsetting consumer. 
     A method for identifying a trendsetting consumer includes: storing, in a database, a plurality of transaction data entries, wherein each transaction data entry includes data related to a payment transaction including at least a consumer identifier, a transaction date, and a plurality of transaction characteristics each having a characteristic value; receiving, by a receiving device, at least two trend identifiers, wherein each trend identifier includes at least a trend date and a transaction characteristic value and is associated with a specific trend; identifying, in the database, at least one trending consumer where a first transaction data entry includes a transaction date corresponding to or before the trend date and one of the plurality of transaction characteristics has a characteristic value corresponding to the transaction characteristic value of one of the at least two identified trends, a second transaction data entry includes a transaction date corresponding to or before the trend date and one of the plurality of transaction characteristics has a characteristic value corresponding to the transaction characteristic value of another one of the at least two identified trends, and the first transaction data entry and the second transaction data entry include a common consumer identifier associated with the respective at least one trending consumer; and associating, by a processing device, the identified at least one trending consumer with the specific trend associated with each of the at least two trend identifiers. 
     A system for identifying a trendsetting consumer includes a database, a receiving device, and a processing device. The database is configured to store a plurality of transaction data entries, wherein each transaction data entry includes data related to a payment transaction including at least a consumer identifier, a transaction date, and a plurality of transaction characteristics each having a characteristic value. The receiving device is configured to receive at least two trend identifiers, wherein each trend identifier includes at least a trend date and a transaction characteristic value and is associated with a specific trend. The processing device is configured to: identify, in the database, at least one trending consumer where a first transaction data entry includes a transaction date corresponding to or before the trend date and one of the plurality of transaction characteristics has a characteristic value corresponding to the transaction characteristic value of one of the at least two identified trends, a second transaction data entry includes a transaction date corresponding to or before the trend date and one of the plurality of transaction characteristics has a characteristic value corresponding to the transaction characteristic value of another one of the at least two identified trends, and the first transaction data entry and the second transaction data entry include a common consumer identifier associated with the respective at least one trending consumer; and associate the identified at least one trending consumer with the specific trend associated with each of the at least two trend identifiers. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       The scope of the present disclosure is best understood from the following detailed description of exemplary embodiments when read in conjunction with the accompanying drawings. Included in the drawings are the following figures: 
         FIG. 1  is a high level architecture illustrating a system for identifying trendsetting consumers in accordance with exemplary embodiments. 
         FIG. 2  is a block diagram illustrating the processing server of  FIG. 1  for the identification of trendsetting consumers in accordance with exemplary embodiments. 
         FIG. 3  is a flow diagram illustrating a method for identifying trends associated with a specific consumer in accordance with exemplary embodiments. 
         FIG. 4  is a flow diagram illustrating a method for identifying consumers associated with specific trends in accordance with exemplary embodiments. 
         FIG. 5  is a diagram illustrating matching trend data to transaction data in accordance with exemplary embodiments. 
         FIG. 6  is a flow chart illustrating an exemplary method for identifying a trendsetting consumer in accordance with exemplary embodiments. 
         FIG. 7  is a block diagram illustrating a computer system architecture in accordance with exemplary embodiments. 
     
    
    
     Further areas of applicability of the present disclosure will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description of exemplary embodiments are intended for illustration purposes only and are, therefore, not intended to necessarily limit the scope of the disclosure. 
     DETAILED DESCRIPTION 
     Definition of Terms 
     Payment Network—A system or network used for the transfer of money via the use of cash-substitutes. Payment networks may use a variety of different protocols and procedures in order to process the transfer of money for various types of transactions. Transactions that may be performed via a payment network may include product or service purchases, credit purchases, debit transactions, fund transfers, account withdrawals, etc. Payment networks may be configured to perform transactions via cash-substitutes, which may include payment cards, letters of credit, checks, financial accounts, etc. Examples of networks or systems configured to perform as payment networks include those operated by MasterCard®, VISA®, Discover®, American Express®, etc. 
     System for Identifying Trendsetting Consumers 
       FIG. 1  illustrates a system  100  for identifying trendsetting consumers based on payment transaction data. 
     The system  100  may include a consumer  102 , that may be a trendsetter for consumer trends. The consumer  102  may conduct a payment transaction with a merchant  104  that may be indicative of a trend. As part of the processing of the payment transaction, the merchant  104  (e.g., or an acquirer on behalf of the merchant  104 ) may generate and submit an authorization request for the transaction to a payment network  106 . The payment network  106  may process the payment transaction based on the authorization request using methods and systems that will be apparent to persons having skill in the relevant art. 
     After processing the payment transaction, the payment network  106  may provide a copy of the transaction data to a processing server  108 , discussed in more detail below. The processing server  108  may store the received transaction data in a transaction database  110 . The transaction data may include a consumer identifier associated with the consumer  102  and a plurality of transaction characteristics. Each transaction characteristic may have a corresponding characteristic value, and may correspond to one or more pieces of transaction data related to the payment transaction. For example, transaction characteristics may include merchant name, geographic location, geographic municipality, merchant category, product data, product name, product code, merchant brand, product size, and/or transaction amount. 
     The processing server  108  may then use trend data to identify the consumer  102  as associated with one or more trends based on transaction data corresponding to the consumer  102  and stored in the transaction database  110 . The trend data may be entered by users of the processing server  108 , or may be received from a third party  112 . The trend data may be any data suitable for identifying a specific trend, and may include at least a date and a characteristic value for a transaction characteristic. The processing server  108  may match transaction data to the trend data using methods discussed in more detail below. 
     Once the processing server  108  has matched a transaction to a trend, the processing server  108  may associate the consumer  102  involved in the transaction with the specific trend. In some embodiments, the consumer  102  may not be considered associated with a trend if the consumer  102  has a prior transaction history matching the trend transaction characteristic value. If the consumer  102  is associated with more than one trend, then the consumer  102  may be considered to be a trendsetter. 
     The processing server  108  may then identify future transactions involving the trendsetting consumer  102  as potential trends. The third party  112  may request information regarding the consumer  102 , which may be provided by the processing server  108  or the consumer  102  based on the permission of the consumer  102 . For example, the third party  112  may be a clothing manufacturer that may be interested in recreating trendy fashion designs, which may be identified based on the shopping patterns of the consumer  102 . In such an instance, the consumer  102  may not only be a part of previous trends, but may actively influence future trends based on their transaction behavior. 
     In other instances, the third party  112  may provide the processing server  108  with trend data, and may request information regarding consumers associated with the particular trend. The processing server  108  may identify the consumers  102  associated with the trend based on the trend data, and provide relevant information to the third party  112 . It will be apparent to persons having skill in the relevant art that information associated with the consumer  102  may only be provided to the third party  112  with the consent of the consumer  102 , or that the third party  112  may only be provided non-personally identifiable information. 
     Processing Device 
       FIG. 2  illustrates an embodiment of the processing server  108  of the system  100 . It will be apparent to persons having skill in the relevant art that the embodiment of the processing server  108  illustrated in  FIG. 2  is provided as illustration only and may not be exhaustive to all possible configurations of the processing server  108  suitable for performing the functions as discussed herein. For example, the computer system  700  illustrated in  FIG. 7  and discussed in more detail below may be a suitable configuration of the processing server  108 . 
     The processing server  108  may include a receiving unit  202 . The receiving unit  202  may be configured to receive data from one or more networks via one or more network protocols. The receiving unit  202  may receive transaction data from the payment network related to payment transactions involving a plurality of consumers. The processing server  108  may also include a processing unit  204 . The processing unit  204  may be configured to store the transaction data in the transaction database  110  as a plurality of transaction data entries  212 . 
     Each transaction data entry  212  may include at least a consumer identifier, a transaction date, and a plurality of transaction characteristics each having a transaction value. The consumer identifier may be a unique value associated with a particular consumer  102 , such as an identification number, username, payment account number, etc. The transaction date may be the date on which the related payment transaction was conducted (e.g., initiated, processed, cleared, etc.). The transaction characteristics, as discussed above, may correspond to data associated with the related payment transaction, such as merchant name, geographic location, geographic municipality, merchant category, product data, product name, product code, merchant brand, product size, and/or transaction amount. 
     The receiving unit  202  of the processing server  108  may also be configured to receive trend data from a third party  112 . The trend data may include at least a trend date and a transaction characteristic value. For example, the trend data may be corresponding to a trend where consumers shopped at a particular merchant. The trend data may thus include a trend date corresponding to the start of the trend and the transaction characteristic value may be the merchant name of the particular merchant. In some embodiments, the trend data may be input into the processing server  108  via an input device  208 . The input device  208  may be a keyboard, mouse, touch screen, click wheel, microphone, camera, or other suitable input device that will be apparent to persons having skill in the relevant art. 
     The processing unit  204  may also be configured to match transaction data entries  212  to trend data. The processing unit  204  may match transaction data entries  212  to trend data by identifying transaction data entries  212  whose transaction date correspond to the trend date and who have a characteristic value that corresponds to the transaction characteristic value included in the trend data. For example, if the trend is for shopping at a particular merchant starting at a particular date, matched transaction data entries may include a transaction date before or close to the particular trend date and may include a merchant name characteristic having the particular merchant as a characteristic value. 
     Once a transaction data entry  212  has been matched to a trend, the processing unit  204  may associate the consumer  102  involved in the transaction with the trend. In some embodiments, the processing server  108  may include a consumer database. Consumer-trend associations may be stored in the consumer database, such as in a consumer profile associated with the respective consumer. The processing unit  204  may also identify consumers that are associated with more than one trend, and may identify (e.g., in the consumer database) those consumers as trendsetters. 
     The processing server  108  may also include a transmitting unit  206 . The transmitting unit  206  may be configured to transmit data across one or more networks via one or more network protocols. In some embodiments, the transmitting unit  206  may be configured to transmit consumer, transaction, and/or trend data (e.g., to the third party  112 ). In embodiments where the transmitting unit  206  may transmit consumer data, the processing unit  204  may be configured to remove any personally identifiable information from the consumer data using systems and methods that will be apparent to persons having skill in the relevant art. 
     In some embodiments, the processing server  108  may also include a display unit  210 . The display unit  210  may be configured to display data to a user, such as trend data, transaction data, consumer data, or a combination thereof. The display unit  210  may be any type of display suitable for performing the functions as disclosed herein, such as a liquid crystal display, light emitting diode display, capacitive touch display, etc. 
     Methods for Identifying Trendsetting Consumers 
       FIG. 3  illustrates a method for identifying trendsetting consumers using the processing server  108 . 
     In step  302 , the processing server  108  may store transaction data for a plurality of payment transactions in the transaction database  110  as a plurality of transaction data entries  212 . In step  304 , the processing server  108  may receive trend data, such as from the third party  112  or via the input device  208 . In step  306 , the processing server  108  may identify a consumer  102  for whom associated trends are to be identified. 
     In step  308 , the processing server  108  may determine if all potential trends corresponding to the received trend data have been analyzed for association with the consumer  102 . If not, then, in step  310 , the processing unit  204  of the processing server  108  may identify the next trend to analyze. In step  312 , the processing unit  204  may match transaction data for transactions involving the consumer  102  to trend characteristics include in the received trend data for the identified trend. In step  314 , the processing unit  204  may determine if there is at least one payment transaction that matches the specific trend. If not, then the process may return to step  308  to determine if more tends are to be analyzed. 
     If there is a match between the trend and transaction data, then, in step  316 , the processing unit  204  may identify any prior transaction history of the consumer  102  with respect to the specific transaction characteristic value associated with the trend. In step  318 , the processing unit  204  may determine if any prior transaction history has been identified. If there is no prior history, then, in step  320 , the processing unit  204  may associate the consumer  102  with the specific trend. If the consumer  102  has a prior history, then the analysis for the specific trend may be completed. In such an instance, the prior history of the consumer  102  may indicate that the consumer  102  is not participating in the trend as the consumer  102  may have conducted the matching transaction regardless of a current or burgeoning trend. 
     Once all of the trends have been analyzed, then, in step  322 , the processing unit  204  may determine if the consumer  102  has been associated with multiple trends. If the consumer  102  has not, then the process may finish. If the consumer  102  has been associated with multiple trends, then, in step  324 , the consumer may be identified as a trendsetter. In such an instance, the processing server  108  may only identify the consumer  102  as a trendsetter if the consumer  102  is associated with multiple trends to eliminate the possibility of the consumer  102  “stumbling upon” a particular trend, or participating in a single trend without normally following trends. 
       FIG. 4  illustrates an alternative method for identifying consumers as trendsetters based on payment transaction data. 
     In step  402 , the processing server  108  may store transaction data for a plurality of payment transactions involving consumers in the transaction database  110  as a plurality of transaction data entries  212 . In step  404 , the processing server  108  may receive trend data for one or more trends. In step  406 , the processing unit  204  may determine if all trends corresponding to the received trend data have been analyzed. If not all trends have been analyzed, then, in step  408 , the processing unit  204  may identify the next trend to be analyzed. 
     In step  410 , the processing unit  204  may match transaction data entries  212  to the trend based on the trend date and transaction characteristic value associated with the specific trend. In step  412 , the processing unit  204  may identify consumers  102  that are associated with the matched transaction data entries  212 . In step  414 , the processing unit  204  may determine, for each identified consumer  102 , if the respective identified consumer  102  has a prior transaction history corresponding to the transaction characteristic value. 
     If the consumer  102  does not have a prior transaction history, then, in step  416 , the consumer  102  may be associated with the specific trend. If the consumer  102  has a prior transaction history, then the consumer  102  may not be associated with the trend. Once all of the trends have been analyzed, the processing unit  204  may identify all consumers  102  associated with multiple trends, in step  418 . Then, in step  420 , the processing unit  204  may set each of the identified consumers  102  as a trendsetter. 
       FIGS. 3 and 4  illustrate methods for identifying trendsetting consumers, by identifying a consumer and matching the specific consumer to trends, or, alternatively, by identifying trends and then finding consumers that match to those specific trends. In either instance, the processing server  108  may be configured to identify consumers that are trendsetters. In such an instance, the processing server  108  may be able to quickly and effectively identify trendsetting consumers, including consumers that may be unknown as trendsetters using traditional systems and methods. In addition, it may enable the processing server  108  to identify trends before or as they become trends by following the purchase habits of known trendsetters. 
     Matching Trend Data to Transaction Data 
       FIG. 5  illustrates the matching of transaction data for payment transactions to trend data. 
     The processing unit  204  of the processing server  108  may be configured to match trend data  502  corresponding to multiple trends, illustrated in  FIG. 5  as trend data  502   a ,  502   b , and  502   c . Each trend data  502  may include at least a trend date and a transaction characteristic value. In some instances, the trend data may be a start date, a specific date range, or a fluid date range. For example, as illustrated in  FIG. 5 , trend data  502   a  may correspond to a trend of shopping at Fashion Co in New York City, which became popular from Jan. 1, 2013. Trend data  502   b  may correspond to a trend of vacationing in Cartagena, Colombia, which was popular between March and May of 2013. Trend data  502   c  may correspond to a trend of attending the Cannes Film Festival, which occurred between May 1 and May 8 of 2013. 
     The processing unit  204  may identify transaction data entries  212  for consumers  102  for whom associated trends are to be identified. As illustrated in  FIG. 5 , the processing unit  204  may identify a first subset of transaction data entries  504   a  corresponding to a first consumer  102 , John Doe, and a second subset of transaction data entries  504   b  corresponding to a second consumer  102 , Jane Doe. The processing unit  204  may then match the transaction data entries  212  in each of the subsets  504   a  and  504   b  with the trend data  502  using transaction dates  506  and transaction characteristics, such as merchant name  508  and merchant city  510 . 
     For example, the subset  504   a  associated with John Doe may be matched to a single trend, trend  502   b . As illustrated in  FIG. 5 , John Doe may have transacted in Cartagena, Colombia during the time at which vacationing in Cartagena was popular. Accordingly, the transaction may be matched to trend  502   b , and John Doe may thereby be associated with the trend. Although John Doe also transacted at Fashion Co in New York City after Jan. 1, 2013, because John Doe regularly shops at Fashion Co (e.g., and thus has a prior history), John Doe may not be considered to be associated with the trend and may instead simply be a normal regular customer at Fashion Co. 
     As a result, John Doe may only be associated with the trend  502   b  of vacationing in Cartagena. As John Doe is only associated with a single trend, John Doe may, in some instances, not be considered a trendsetter. On the other hand, Jane Doe may be considered a trendsetter as she may be associated with all three trends  502   a ,  502   b , and  502   c . As illustrated in  FIG. 5 , Jane Doe has a transaction history in the subset  504   b  including transactions at Fashion Co after Jan. 1, 2013, transactions in Cartagena during the March to May 2013 period, and transactions in Cannes during the Cannes Film Festival. Jane Doe may thus be associated with each of the trends, and thereby considered a trendsetter. 
     Method for Identifying a Trendsetting Consumer 
       FIG. 6  illustrates a method  600  for identifying a trendsetting consumer. 
     In step  602 , a plurality of transaction data entries (e.g., transaction data entries  212 ) may be stored in a database (e.g., the transaction database  110 ), wherein each transaction data entry  212  includes data related to a payment transaction including at least a consumer identifier, a transaction date, and a plurality of transaction characteristics each having a characteristic value. In some embodiments, the plurality of transaction characteristics may include at least one of: merchant name, geographic location, geographic municipality, merchant category, product data, product name, product code, merchant brand, product size, and transaction amount. 
     In step  604 , a receiving device (e.g., the receiving unit  202  or the input device  208 ) may receive at least two trend identifiers, wherein each trend identifier includes at least a trend date and a transaction characteristic value and is associated with a specific trend. 
     In step  606 , at least one trending consumer (e.g., the consumer  102 ) may be identified where: a first transaction data entry  212  includes a transaction date corresponding to or before the trend date and one of the plurality of transactions characteristics has a characteristic value corresponding to the transaction characteristic value of one of the at least two identified trends; a second transaction data entry includes a transaction date corresponding to the trend date and one of the plurality of transaction characteristics has a characteristic value corresponding to the transaction characteristic value of another one of the at least two identified trends; and the first transaction data entry and the second transaction data entry include a common consumer identifier associated with the respective at least one trending consumer. In one embodiment, the transaction characteristic value for a first of the at least two trend identifiers may be a specific merchant (e.g., the merchant  104 ), and the at least one trending consumer may not have a history of payment transactions with the specific merchant  104 . 
     In step  608 , a processing device (e.g., the processing unit  204 ) may associate the identified at least one trending consumer with each specific trend associated with the at least two trend identifiers. In one embodiment, the at least one trending consumer may be identified as a trendsetter. In a further embodiment, the method  600  may further include: receiving, by the receiving device  202 , transaction data for a payment transaction including at least the consumer identifier corresponding to the at least one trending consumer identified as a trendsetter and a merchant identifier; and identifying, by the processing device  204 , a merchant associated with the merchant identifier included in the transaction data as a new trend. 
     In some embodiments, if more than a predetermined number of trending consumers is identified, then the method  600  may further include identifying, by the processing device  204 , the predetermined number of trending consumers of the identified at least one consumer based on the transaction date included in each of the respective first and second transaction data entries. In such an instance, the processing server  108  may limit the identification of an overwhelming or diluted number of trending consumers, and may identify the earliest trendsetters up to the specific predetermined number. 
     In another embodiment, the method  600  may further include: receiving, by the receiving device  202 , a request for trending consumers, wherein the request for trending consumers includes one of the at least two trend identifiers; and transmitting, by a transmitting device (e.g., the transmitting unit  206 ), the identified at least one trending consumer associated with the specific trend associated with the one of the at least two trend identifiers. In some embodiments, the method  600  may further include transmitting, by the transmitting device  206 , a notification to the identified at least one trending consumer, indicating the association with the specific trend associated with each of the at least two trend identifiers. In another embodiment, the method  600  may further include identifying, by the processing device  204 , an audience of consumers identified as trendsetters including at least the identified at least one trending consumer associated with the specific trend associated with each of the at least two trend identifiers. 
     Computer System Architecture 
       FIG. 7  illustrates a computer system  700  in which embodiments of the present disclosure, or portions thereof, may be implemented as computer-readable code. For example, the processing server  108  of  FIG. 1  may be implemented in the computer system  700  using hardware, software, firmware, non-transitory computer readable media having instructions stored thereon, or a combination thereof and may be implemented in one or more computer systems or other processing systems. Hardware, software, or any combination thereof may embody modules and components used to implement the methods of  FIGS. 3 ,  4 , and  6 . 
     If programmable logic is used, such logic may execute on a commercially available processing platform or a special purpose device. A person having ordinary skill in the art may appreciate that embodiments of the disclosed subject matter can be practiced with various computer system configurations, including multi-core multiprocessor systems, minicomputers, mainframe computers, computers linked or clustered with distributed functions, as well as pervasive or miniature computers that may be embedded into virtually any device. For instance, at least one processor device and a memory may be used to implement the above described embodiments. 
     A processor device as discussed herein may be a single processor, a plurality of processors, or combinations thereof. Processor devices may have one or more processor “cores.” The terms “computer program medium,” “non-transitory computer readable medium,” and “computer usable medium” as discussed herein are used to generally refer to tangible media such as a removable storage unit  718 , a removable storage unit  722 , and a hard disk installed in hard disk drive  712 . 
     Various embodiments of the present disclosure are described in terms of this example computer system  700 . After reading this description, it will become apparent to a person skilled in the relevant art how to implement the present disclosure using other computer systems and/or computer architectures. Although operations may be described as a sequential process, some of the operations may in fact be performed in parallel, concurrently, and/or in a distributed environment, and with program code stored locally or remotely for access by single or multi-processor machines. In addition, in some embodiments the order of operations may be rearranged without departing from the spirit of the disclosed subject matter. 
     Processor device  704  may be a special purpose or a general purpose processor device. The processor device  704  may be connected to a communication infrastructure  706 , such as a bus, message queue, network, multi-core message-passing scheme, etc. The network may be any network suitable for performing the functions as disclosed herein and may include a local area network (LAN), a wide area network (WAN), a wireless network (e.g., WiFi), a mobile communication network, a satellite network, the Internet, fiber optic, coaxial cable, infrared, radio frequency (RF), or any combination thereof. Other suitable network types and configurations will be apparent to persons having skill in the relevant art. The computer system  700  may also include a main memory  708  (e.g., random access memory, read-only memory, etc.), and may also include a secondary memory  710 . The secondary memory  710  may include the hard disk drive  712  and a removable storage drive  714 , such as a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, etc. 
     The removable storage drive  714  may read from and/or write to the removable storage unit  718  in a well-known manner. The removable storage unit  718  may include a removable storage media that may be read by and written to by the removable storage drive  714 . For example, if the removable storage drive  714  is a floppy disk drive, the removable storage unit  718  may be a floppy disk. In one embodiment, the removable storage unit  718  may be non-transitory computer readable recording media. 
     In some embodiments, the secondary memory  710  may include alternative means for allowing computer programs or other instructions to be loaded into the computer system  700 , for example, the removable storage unit  722  and an interface  720 . Examples of such means may include a program cartridge and cartridge interface (e.g., as found in video game systems), a removable memory chip (e.g., EEPROM, PROM, etc.) and associated socket, and other removable storage units  722  and interfaces  720  as will be apparent to persons having skill in the relevant art. 
     Data stored in the computer system  700  (e.g., in the main memory  708  and/or the secondary memory  710 ) may be stored on any type of suitable computer readable media, such as optical storage (e.g., a compact disc, digital versatile disc, Blu-ray disc, etc.) or magnetic tape storage (e.g., a hard disk drive). The data may be configured in any type of suitable database configuration, such as a relational database, a structured query language (SQL) database, a distributed database, an object database, etc. Suitable configurations and storage types will be apparent to persons having skill in the relevant art. 
     The computer system  700  may also include a communications interface  724 . The communications interface  724  may be configured to allow software and data to be transferred between the computer system  700  and external devices. Exemplary communications interfaces  724  may include a modem, a network interface (e.g., an Ethernet card), a communications port, a PCMCIA slot and card, etc. Software and data transferred via the communications interface  724  may be in the form of signals, which may be electronic, electromagnetic, optical, or other signals as will be apparent to persons having skill in the relevant art. The signals may travel via a communications path  726 , which may be configured to carry the signals and may be implemented using wire, cable, fiber optics, a phone line, a cellular phone link, a radio frequency link, etc. 
     Computer program medium and computer usable medium may refer to memories, such as the main memory  708  and secondary memory  710 , which may be memory semiconductors (e.g., DRAMs, etc.). These computer program products may be means for providing software to the computer system  700 . Computer programs (e.g., computer control logic) may be stored in the main memory  708  and/or the secondary memory  710 . Computer programs may also be received via the communications interface  724 . Such computer programs, when executed, may enable computer system  700  to implement the present methods as discussed herein. In particular, the computer programs, when executed, may enable processor device  704  to implement the methods illustrated by  FIGS. 3 ,  4 , and  6 , as discussed herein. Accordingly, such computer programs may represent controllers of the computer system  700 . Where the present disclosure is implemented using software, the software may be stored in a computer program product and loaded into the computer system  700  using the removable storage drive  714 , interface  720 , and hard disk drive  712 , or communications interface  724 . 
     Techniques consistent with the present disclosure provide, among other features, systems and methods for providing characteristic payments data. While various exemplary embodiments of the disclosed system and method have been described above it should be understood that they have been presented for purposes of example only, not limitations. It is not exhaustive and does not limit the disclosure to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing of the disclosure, without departing from the breadth or scope.