Patent Publication Number: US-10325243-B1

Title: Systems and methods for identifying and ranking successful agents based on data analytics

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation application of U.S. patent application Ser. No. 14/576,900, filed Dec. 19, 2014, which claims priority to U.S. Provisional Patent Application Ser. No. 61/922,127, filed Dec. 31, 2013, and U.S. Provisional Patent Application Ser. No. 61/921,732, filed Dec. 30, 2013, which are hereby incorporated by reference in their entirety. 
     This application relates to U.S. Provisional Patent Application Ser. No. 61/921,718, filed on Dec. 30, 2013, and U.S. Provisional Patent Application Ser. No. 61/921,725, filed Dec. 30, 2013, which are hereby incorporated by reference in their entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     The present disclosure relates in general to systems and methods for career management and more specifically, to a method for identifying and ranking successful agents based on data analytics. 
     BACKGROUND 
     Insurance or financial companies usually spend significant amounts of time and resources in developing agents&#39; careers. For example, considering the cost of attrition, developing successful agents may include costs of in the excess of about $300 k over a span of at least 2 to 5 years. 
     For the foregoing reasons, there is a need for a system and method that identify and rank successful agents, provide support in the recruitment process, and assist in the career development of the agents. 
     SUMMARY 
     A system and a method for identifying prospective agents are disclosed herein. Embodiments of the system and method use one or more success factors or key attributes to identify and rank successful agents, therefore improving the development and likelihood of success of agents. 
     In one embodiment, a computer-implemented system for identifying successful agents includes an external database and an internal database operatively coupled with an analytics engine. In this embodiment, the analytics engine includes a profile analyzer module and a predictive identification module, among other software modules. The analytics engine is operatively coupled with an attributes analyzer. Attributes analyzer may feed key attributes into the analytics engine to identify and rank prospective agents. 
     In an embodiment, a computer-implemented method for predicting successful agents includes the following steps: of retrieving and extracting data from one or more external and internal databases, deriving one or more success factors or key attributes of successful agents, transforming data into agents&#39; profiles, analyzing agents&#39; profiles, assigning validation scores to attributes within agents&#39; profiles, associating a weighted coefficient to agents&#39; profiles and generating a list of one or more prospective agents. 
     In an embodiment, a computed implemented method for providing prospective agents to general agents includes the following steps: obtaining successful agents&#39; profile and ranking successful agents. If the agent is an active agent, then the system performs an assessment of the agent&#39;s profile. Otherwise, the system matches the prospective agent to a general agent. 
     The disclosed system and method automatically identify and rank successful agents and/or active agents, thereby assisting the recruitment process and the career development of agents. 
     In one embodiment, a computer-implemented method comprises retrieving, by a profile analyzer module of an analytics engine server, a profile record and related data associated with an active agent from an internal database; providing, by an attributes analyzer module of the analytics engine server, a key attribute for predicting a likelihood of success for a prospective or current agent; comparing, by a profile analyzer of the analytics engine server, the profile record of the active agent with key attributes by applying an algorithm based on the key attribute; assigning, by the profile analyzer of the analytics engine server, a validation score for the profile record of the active agent compared with each key attribute, wherein the validation score is based upon whether the profile record satisfies a predetermined criteria for the key attribute; estimating, by a ranking module of the analytics engine server, a weighted coefficient for the profile record based on the validation score, wherein the weighted coefficient is a summation of validation scores for the comparison of each key attribute of the profile record of the active agent; determining, by the ranking module of the analytics engine server, whether the weighted coefficient satisfies a predetermined threshold that indicates a likelihood of success of the active agent; generating, by the ranking module of the analytics engine server, a listing of active agents having a weighted coefficient that satisfies the predetermined threshold; and marking, by the ranking module of the analytics engine server, each profile record of active agents that do not have a weighted coefficient that satisfies the predetermined threshold. 
     In another embodiment, a system comprises retrieving, by a profile analyzer module of an analytics engine server configured to retrieve a profile record and related data associated with an active agent from an internal database; providing, by an attributes analyzer module of the analytics engine server configured to provide a key attribute for predicting a likelihood of success for a prospective or current agent; comparing, by a profile analyzer of the analytics engine server configured to compare the profile record of the active agent with key attributes by applying an algorithm based on the key attribute, and configured to assign a validation score for the profile record of the active agent compared with each key attribute, wherein the validation score is based upon whether the profile record satisfies a predetermined criteria for the key attribute; and estimating, by a ranking module of the analytics engine server configured to estimate a weighted coefficient for the profile record based on the validation score, wherein the weighted coefficient is a summation of validation scores for the comparison of each key attribute of the profile record of the active agent, configured to determine whether the weighted coefficient satisfies a predetermined threshold that indicates a likelihood of success of the active agent, configured to generate a listing of active agents having a weighted coefficient that satisfies the predetermined threshold, and configured to mark each profile record of active agents that do not have a weighted coefficient that satisfies the predetermined threshold. 
     Additional features and advantages can become apparent from the detailed descriptions which follow, taken in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure can be better understood by referring to the following figures. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the disclosure. In the figures, reference numerals designate corresponding parts throughout the different views. 
         FIG. 1  is a block diagram illustrating an exemplary system architecture for identifying prospective agents, according to an embodiment. 
         FIG. 2  is block diagram illustrating an exemplary computing device or server in which one or more embodiments of the present disclosure may operate. 
         FIG. 3  is a block diagram illustrating an exemplary sub-system of a portion of a system architecture pertaining to an analytics engine, according to an embodiment. 
         FIG. 4  is an exemplary flowchart generally illustrating a method to predict successful agents, according to an embodiment. 
         FIG. 5  is an exemplary flowchart generally illustrating a method to provide prospective agents to general agents, according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure is here described in detail with reference to embodiments illustrated in the drawings, which form a part here. Other embodiments may be used and/or other changes may be made without departing from the spirit or scope of the present disclosure. The illustrative embodiments described in the detailed description are not meant to be limiting of the subject matter presented here. 
     As used here, the following terms may have the following definitions: 
     “Agent” refers to an individual working for a company or as a broker with an interest in attracting new clients by analyzing their needs and wishes. An agent looks for prospects with high referral potential. In addition, an agent may represent the intermediary between an issuing company and a client. 
     “Analytics engine” refers to a software module that handles data integration; breaks data streams into atomic parts, executes rules, and performs data matching by using fuzzy logic, among others. 
     “Attributes” refers to one or more characteristics that may be relevant for evaluating the performance of agents. 
     “General agent” refers to an individual who supervises the work of agents and may be in charge of their recruitment and training. 
     “Metadata” refers to analyzed data that can be used for creating a potential agent profile. 
       FIG. 1  is a block diagram illustrating an exemplary system architecture for identifying and ranking prospective agents according to an embodiment. In  FIG. 1 , system architecture  100  includes external database  102 , internal database  104 , analytics engine  106 , and attributes analyzer  108 . In other embodiments, system architecture  100  includes additional, fewer, different, or differently arranged components than those illustrated in  FIG. 1 . 
     In  FIG. 1 , analytics engine  106  is wired/wirelessly coupled to and in wired/wireless communication with external database  102 , and is additionally wired/wirelessly coupled to and in wired/wireless communication with internal database  104 . In this embodiment, analytics engine  106  is wired/wirelessly coupled to and in wired/wireless communication with attributes analyzer  108 . In  FIG. 1 , the network connection (not shown in the  FIG. 1 ) permits components within system architecture  100  to communicate with each other. Examples of network connections are intranets, local area networks (LAN), virtual private networks (VPN), wireless area networks (WAN) and the internet among others. 
     In some embodiments, external database  102  stores information such as resumes, job history, recommendations, and social activities outside the work environment, among others. In these embodiments, external database  102  can be fed from websites, social media and networks, and referrals, among others. In one embodiment, information stored in external database  102  is supplied by recruitment or job agencies. In another embodiment, external database  102  is implemented through database management systems (DBMS) such as, for example, MySQL, PostgreSQL, SQLite, Microsoft SQL Server, Microsoft Access, Oracle, SAP, dBASE, FoxPro, IBM DB2, LibreOffice Base, FileMaker Pro and/or any other type of database that organizes collections of data. 
     In some embodiments, internal database  104  stores profiles and data associated with one or more active agents in the financial or insurance company. In these embodiments, active agents are considered current workers of the financial or insurance company who have dedicated profiles. 
     In a further embodiment, analytics engine  106  includes a processing unit for executing algorithms or computer-executable program instructions. In these embodiments, the processing unit may include a processor with computer-readable medium, such as a random access memory (RAM) (not shown) coupled to the processor. Examples of processor types include a microprocessor, application specific integrated circuit (ASIC), and field programmable object array (FPOA), among others. 
     In some embodiments, attributes analyzer  108  includes a processing unit for executing algorithms or computer-executable program instructions. In these embodiments, attributes analyzer  108  provides key attributes for predicting the likelihood of success for potential and current agents. Examples of key attributes include knowledge of the products to offer, university education and related professional licenses, people and communication skills, accessibility, intelligence quotient, personality, quality of service provided to clients, experience, and periodic evaluation reports, among others. 
     In some embodiments, analytics engine  106  is configured to retrieve information about external agents from external database  102  and coverts the data extracted from external database  102  into one or more agents&#39; profiles. In these embodiments, an agent profile includes university education, communication skills, experience, resume, creativity and analytical skills, among others 
     According to some embodiments, analytics engine  106  also retrieves from internal database  104 , profiles and other data associated with active agents within the financial or insurance company. In some embodiments, analytics engine  106  retrieves key attributes from attributes analyzer  108 , where each key attribute has a validation score based on a satisfying set of predetermined criteria. 
     In another embodiment, analytics engine  106  uses an algorithm to compare an agent profile with key attributes retrieved from attributes analyzer  108 . Analytics engine  106  assigns a validation score to each key attribute found within the agent profile. Analytics engine  106  obtains a weighted coefficient based on the weight of each validation score found within the agent profile and associates the weighted coefficient with the agent profile. The weighted coefficient may be a summation of the validation scores for each key attribute found in the agent profile. This process will be repeated for each agent profile from external database  102  and internal database  104 . Subsequently, analytics engine  106  determines whether each agent profile satisfies one or more success criteria using the weighted coefficient associated with each agent profile. If the agent profile satisfies the success criteria, then analytics engine  106  ranks the agent profile and adds the agent profile to a list of potentially successful agents. Otherwise the agent profile is discarded. This process is repeated with all the agents&#39; profiles. In some embodiments, the success criteria include a combination of metrics and/or goals that can be used for evaluating the performance and success of agents. The success criteria can vary according to internal business and human development considerations of the insurance or financial company, and it can also consider external factors such as market performance and competition. 
     In some embodiments, analytics engine  106  generates a list of potentially successful agents using information from one or more agent&#39;s profiles within external database. In these embodiments, analytics engine  106  ranks the potential agent&#39;s profile based on the weighted coefficient associated with each agent profile. In this embodiment, one or more general agents use this list of identified potential agents to expand operations and coverage, renew personnel, develop new products, and improve customer service, among other business activities. 
     In another embodiment, analytics engine  106  generates a list of potentially successful agents using information from active agents within external database  102  and internal database  104 . In these embodiments, analytics engine  106  ranks the successful agents profile based on the weighted coefficient associated with each agent profile. Furthermore, analytics engine  106  sends periodic ranking reports or performance assessment reports to one or more general agents. In some embodiments, general agents use these ongoing assessment reports of current agents for evaluating and following up their career developments. 
     In a further embodiment, analytics engine  106  matches the attributes of the identified potentially successful agents to the profiles of one or more general agents. For example, analytics engine  106  determines that certain potentially successful agents may be best suited for working with a particular general agent covering a region that includes clients with more complex or sophisticated needs. Analytics engine  106  sends a list of identified successful agents to one or more agent computing devices. In this embodiment, one or more general agents use this list of identified successful agents to expand operations and coverage, renew personnel, develop new products, and improve customer service, among other business activities. 
     In some embodiments, an application of the system described in  FIG. 1  is used by an insurance or financial company that needs to hire new agents. The insurance or financial company may use the system described in  FIG. 1  to determine prospective agents based on key attributes or success factors thereby assisting the career development of agents and the recruitment process. 
       FIG. 2  is a block diagram illustrating an exemplary computing device in which one or more embodiments of the implementation may operate. In  FIG. 2 , computing Device  200  includes Bus  202 , input/output (I/O) Device  204 , communication Interface  206 , Memory  208 , Storage Device  210  and Central Processing Unit  212 . In another embodiment, Computing Device  200  includes additional, fewer, different, or differently arranged components than those illustrated in  FIG. 2 . 
     In  FIG. 2 , Bus  202  is in physical communication with (I/O) Device  204 , Communication Interface  206 , Memory  208 , Storage Device  210 , and Central Processing Unit  212 . Bus  202  includes a path that permits components within Computing Device  200  to communicate with each other. Examples of (I/O) Device  204  include peripherals and/or other mechanisms that may enable an examiner or candidate to input information to Computing Device  200 , including a keyboard, computer mice, buttons, touch screens, touch-pad, voice recognition, biometric mechanisms, and the like. (I/O) Device  204  also includes a mechanism that outputs information to a user of Computing Device  200 , such as, for example a display, a microphone, a light emitting diode (LED), a printer, a speaker, orientation sensors and the like. Said orientation sensors include one or more accelerometers, one or more gyroscopes, one or more compasses, and the like. 
     Examples of Communication Interface  206  include mechanisms that enable Computing Device  200  to communicate with other computing devices and/or systems through network connections. Examples of Memory  208  include random access Memory  208  (RAM), read-only memory (ROM), flash memory, and the like. Examples of Storage Device  210  include magnetic and/or optical recording medium, ferro-electric RAM (F-RAM) hard disks, solid-state drives, floppy disks, optical discs, and the like. In one embodiment, Memory  208  and Storage Device  210  store information and instructions for execution by Central Processing Unit  212 . In another embodiment, Central Processing Unit  212  includes a microprocessor, an application specific integrated circuit (ASIC), or a field programmable object array (FPOA), and the like. In this embodiment, Central Processing Unit  212  interprets and executes instructions retrieved from Memory  208  and Storage Device  210 . 
     Examples of these implementations include servers, authorized computing devices, smartphones, desktop computers, laptop computers, tablet computers, PDAs, and any other type of processor-controlled device that may receive, process, transmit digital data, and the like. Additionally, Computing Device  200  may perform certain operations that are required for the proper operation of system architecture  100 . Computing Devices  200  may perform these operations in response to Central Processing Unit  212  executing software instructions contained in a computer-readable medium, such as Memory  208 . 
     In one embodiment, the software instructions of a system are read into Memory  208  from another memory location, such as Storage Device  210 , or from another Computing Device  200  via communication Interface  206 . In this embodiment, the software instructions contained within Memory  208  cause Central Processing Unit  212  to perform processes that will be described in  FIG. 3 , below. 
       FIG. 3  is a block diagram illustrating an exemplary subsystem of a portion of system architecture  100  of  FIG. 1 . In some embodiments, sub-system  300  is implemented as an analytics engine, such as, for example analytics engine  106 . In  FIG. 3  sub-system  300  includes an external database  302 , an internal database  304 , and analytics engine  106 . Analytics engine  106  further includes a profile analyzer module  306 , a ranking module  308  and a matching module  310 . It should be understood that analytics engine  106 , can include less components, more components, or different components depending on the desired analysis goals. In an example referring to  FIG. 1 , analytics engine  306  is implemented as analytics engine  106  within system architecture  100 . 
     In  FIG. 3 , analytics engine  106  is wired/wirelessly coupled to and in wired/wireless communication with external database  302 . Analytics engine  106  is additionally wired/wirelessly coupled to and in two-way wired/wireless communication with internal database  304 . Profile analyzer module  306  within analytics engine  106 , is wired/wirelessly coupled to and in wired/wireless communication with identification module  308 . In  FIG. 3 , the network connection (not shown in  FIG. 3 ) permits components within subsystem  300  to communicate with each other. Examples of network connections may be intranets, local area networks (LAN), virtual private networks (VPN), wireless area networks (WAN) and the internet among others. 
     In some embodiments, analytics engine  106  includes a processing unit for executing algorithms or computer-executable program instructions related to the operation of profile analyzer module  306 , ranking module  308  and matching module  310 . In one embodiment, each software module includes a separate processing unit for running related algorithms or computer executable program instructions. The processing unit includes a processor with a computer-readable medium, such as a random access memory (RAM) (not shown) coupled to the processor. Examples of processor types include a microprocessor, application specific integrated circuit (ASIC), and field programmable object array (FPOA), among others. 
     In some embodiments, profile Analyzer module  306  is configured to retrieve information about external agents from external database  302  and convert the data extracted from external database  302  into one or more agent profiles. In these embodiments, an agent profile includes university education, communication skills, experience, resume, creativity, analytical skills among others. According to some aspects of this embodiment, profile Analyzer module  306  also retrieves, from internal database  304 , profiles and related data of active agents from the financial or insurance company. In some embodiments, profile Analyzer module  306  retrieves key attributes from an attributes analyzer (not shown in  FIG. 3 ), where each key attribute has a validation score based on a satisfying set of predetermined criteria. In another embodiment, profile analyzer module  306  uses an algorithm to compare an agent profile with key attributes retrieved from the attributes analyzer. Profile analyzer module  306  assigns a validation score to each key attribute found within the agent profile. Validation scores can vary from 1-5 based on a satisfying set of predetermined criteria. 
     According to some aspects of this embodiment, ranking module  308  obtains a weighted coefficient based on the weight of each validation score found within the agent profile and associates the weighted coefficient with the agent profile. The weighted coefficient can be a summation of the validation scores assigned to each key attribute found in the agent profile. This process will be repeated for each agent profile from external database  302  and internal database  304 . Subsequently, ranking module  308  determines if the agent profile satisfies one or more success criteria using the weighted coefficient associated with each agent profile. If the agent profile satisfies the success criteria, then identification module  308  ranks the agent profile and adds the agent profile to a list of successful agents. Otherwise the agent profile is discarded. This process is repeated with all the agents&#39; profiles from external database  302  and internal database  304 . Finally, identification module  308  generates a list of potentially successful agents, where successful agents are ranked based on the weighted coefficient associated with each agent profile. In some embodiments, the success criteria include a combination of metrics and/or goals that can be used for evaluating the performance and success of agents. The success criteria can vary according to internal business and human development considerations of the insurance or financial company, and it may also consider external factors such as market performance and competition. 
     In some embodiments, ranking module  308  generates a list of potentially successful agents using information from agent&#39;s profiles within external database  302 . In these embodiments, ranking module  308  ranks the potential agent&#39;s profiles based on the weighted coefficient associated with each agent profile. In this embodiment, one or more general agents use this list of identified potential agents to expand operations and coverage, renew personnel, develop new products, and improve customer service, among other business activities. 
     In another embodiment, ranking module  308  generates a list of potentially successful agents using information from active agents within external database  302  and internal database  304 . In these embodiments, ranking module  308  ranks the successful agent&#39;s profiles based on the weighted coefficient associated with each agent profile. Furthermore, ranking module  308  sends periodic ranking reports or performance assessment reports to one or more general agents. In some embodiments, general agents use these ongoing assessment reports of current agents for evaluating and following up their career developments. 
     In a further embodiment, matching module  310  matches the attributes of the identified potentially successful agents to the profiles of one or more general agents. For example, matching module  310  determines that certain potentially successful agents may be best suited for working with a particular general agent covering a region that includes clients with more complex or sophisticated needs. Matching module  310  sends a list of identified successful agents to one or more agent computing devices. In this embodiment, one or more general agents use this list of identified successful agents to expand operations and coverage, renew personnel, develop new products, and improve customer service, among other business activities. 
       FIG. 4  is an exemplary flowchart generally illustrating a method  400  for identifying successful agents. A plurality of steps included in method  400  may be performed by one or more computing devices such as computing device  200  implementing/running one or more software modules of the exemplary operating environments of  FIGS. 1-3 . The steps of this exemplary method are embodied in a computer readable medium containing a computer readable code such that the steps are implemented when the computer readable code is executed by a computing device. In some implementations, certain steps of the method can be combined, performed simultaneously, or in a different order, without deviating from the objective of the method. 
     In  FIG. 4 , method  400  starts at step  402  when a profile analyzer within an analytics engine retrieves information about prospective agents from an external database. Examples of external data sources include websites, social media and referrals, among others. Information that can be gathered from external data sources includes resumes, job history, recommendations, demographics, social and behavioral data, among others. In some embodiments, the profile analyzer coverts the data extracted from the external database into one or more agent profiles. Method  400  advances to step  404 . 
     At step  404 , the profile analyzer retrieves, from an internal database, profiles and related data associated with active agents from a financial or insurance company. In some embodiments, the internal database includes information about active agents such as performance or historical sales data Method  400  advances to step  406 . 
     At step  406 , an attributes analyzer module feeds the analytics engine with one or more key attributes or success factors used for predicting the likelihood of success for prospective and current agents. Examples of key attributes or success factors include knowledge of the products to offer, college education and related professional licenses, people and communication skills, accessibility, intelligence quotient, personality, technical abilities, quality of service provided to clients, and experience, among others. Method  400  advances to step  408 . 
     At step  408 , a profile analyzer module uses an algorithm to compare agent profiles with key attributes. In this embodiment, the profile analyzer module assigns a validation score to each key attribute found within the agent profile. Further to this embodiment, the validation score is computed based on a particular key attribute reaching a predetermined criteria. For example, for a key attribute related to IQ, the validation score assigned to the agent profile may be higher if the IQ of the agent is close to a predetermined criteria of  140 . Method  400  advances to step  410 . 
     At step  410 , a ranking module estimates a weighted coefficient based on the validation score found within the agent profile. In some embodiments, the ranking module associates this weighted coefficient with the agent profile. The weighted coefficient is a summation of the validation scores for each key attribute found in the agent profile. Method  400  advances to step  412 . 
     At step  412 , the ranking module checks if there is any other agent profile that needs to be weighted according the validation scores. In one embodiment, if there are more agent profiles to be weighted, then method  400  advances to step  408 . In another embodiment, if there are no more agent profiles to be weighted, then method  400  advances to step  414 . 
     At step  414 , the ranking module checks if the weighted coefficient computed for the agent profile satisfies one or more success criteria. In some embodiments, the weighted coefficient varies according to logic variables, analog logic, digital inputs and rule based values, among others. According to some aspects of this embodiment, the success criteria include a combination of metrics, thresholds, and/or goals that can be used for evaluating the performance and success of agents. The success criteria vary according to internal business and human development considerations of the insurance or financial company. In one embodiment, if the weighted coefficient of the agent profile satisfies the success criteria or threshold, then method  400  advances to step  416 . In another embodiment, if the weighted coefficient of the agent profile does not satisfy the success criteria or threshold, then method  400  advances to step  418 . In this embodiment, step  414  is repeated with all the agents&#39; profiles from external database and internal database. 
     At step  416 , the ranking module adds the agent profile that satisfied the success criteria or threshold to a list of potential agents. At step  418 , the ranking module discards the agent profile that does not satisfy the success criteria or threshold. 
     By executing method  400  through the exemplary operating environments shown in  FIGS. 1-3 , big data analytics and data mining techniques can be implemented for a more efficient and faster processing of larger data sets. In this way, efficiencies are created by providing the financial or insurance company with ways to identify and rank successful agents. These features allow performing large work such as heavy calculations and time consuming analysis in a more efficient manner than other approaches such as manual work performed by humans. 
       FIG. 5  is an exemplary flowchart generally illustrating a method  500  for matching prospective agents to general agents, according to an embodiment. A plurality of steps included in method  500  may be performed by one or more computing devices such as computing device  200  implementing/running one or more software modules of the exemplary operating environments of  FIGS. 1 and 3 . The steps of this exemplary method are embodied in a computer readable medium containing a computer readable code such that the steps are implemented when the computer readable code is executed by a computing device. In some implementations, certain steps of the method can be combined, performed simultaneously, or in a different order, without deviating from the objective of the method. 
     In  FIG. 5 , the method starts at step  502 , when a ranking module generates a list of prospective successful agents. In some embodiments, the list of successful agents can be generated from the analysis of agent profiles obtained from an external database and an internal database as described in  FIG. 4 . In these embodiments, the list of successful agents includes active or prospective agents. Method  500  then advances to step  504 . 
     At step  504 , the ranking module ranks successful agent profiles from the list created at step  502 . In one embodiment, this ranking is based on a weighted coefficient associated with each successful agent profile. In this embodiment, the weighted coefficient is a summation of the validation scores assigned for each key attribute found in successful agent profiles. Method  500  then advances to step  506 . 
     At step  506 , a matching module determines whether the agent profile within the list of successful agents is an active agent within the financial or insurance company. In this embodiment, active agents are considered current workers of the financial or insurance company. In one embodiment, if the agent profile is an active agent, then method  500  advances to step  508 . In another embodiment, if the agent profiles is not an active agent, then method  500  advances to step  510 . 
     At step  508 , the matching module sends periodic ranking reports or performance assessment reports of active agents to one or more general agents. In some embodiments, general agents use these ongoing assessment reports of active agents for evaluating and following up their career developments. 
     At step  510 , the matching module matches the attributes of the identified potentially successful agents to the profiles of one or more general agents. In some embodiments the matching module determines that certain prospective successful agents may be best suited for working with a particular general agent covering a region that includes clients with more complex or sophisticated needs. In one example, the matching module determines that successful agents having a university degree from an Ivy School may be best suited for working with a general agent from Massachusetts or New York. In another example, the matching module determines that successful agents having life science backgrounds may be best suited for working with a general agent focusing on health insurance. Yet in another example, the matching module determines that successful agents fluent in Spanish may be best suited for working with a general agent covering a region with a high percentage of Hispanic population. 
     In these embodiments, matching module sends a list of identified successful agents to one or more agent computing devices. Further to these embodiments, one or more general agents can use the list of identified successful agents to expand operations and coverage, renew personnel, develop new products, and improve customer service, among other business activities. 
     The foregoing method descriptions and the process flow diagrams are provided merely as illustrative examples and are not intended to require or imply that the steps of the various embodiments must be performed in the order presented. As will be appreciated by one of skill in the art the steps in the foregoing embodiments may be performed in any order. Words such as “then,” “next,” etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the methods. Although process flow diagrams may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or the main function. 
     The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed here may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. 
     Embodiments implemented in computer software may be implemented in software, firmware, middleware, microcode, hardware description languages, or any combination thereof. A code segment or machine-executable instructions may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any means including memory sharing, message passing, token passing, network transmission, etc. 
     The actual software code or specialized control hardware used to implement these systems and methods is not limiting of the invention. Thus, the operation and behavior of the systems and methods were described without reference to the specific software code being understood that software and control hardware can be designed to implement the systems and methods based on the description here. 
     When implemented in software, the functions may be stored as one or more instructions or code on a non-transitory computer-readable or processor-readable storage medium. The steps of a method or algorithm disclosed here may be embodied in a processor-executable software module which may reside on a computer-readable or processor-readable storage medium. A non-transitory computer-readable or processor-readable media includes both computer storage media and tangible storage media that facilitate transfer of a computer program from one place to another. A non-transitory processor-readable storage media may be any available media that may be accessed by a computer. By way of example, and not limitation, such non-transitory processor-readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other tangible storage medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer or processor. Disk and disc, as used here, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and/or instructions on a non-transitory processor-readable medium and/or computer-readable medium, which may be incorporated into a computer program product. 
     When implemented in hardware, the functionality may be implemented within circuitry of a wireless signal processing circuit that may be used in a wireless receiver or mobile device. Such a wireless signal processing circuit may include circuits for accomplishing the signal measuring and calculating steps described in the various embodiments. 
     The hardware used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, some steps or methods may be performed by circuitry that is specific to a given function. 
     Any reference to claim elements in the singular, for example, using the articles “a,” “an” or “the,” is not to be construed as limiting the element to the singular. 
     The preceding description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the following claims and the principles and novel features disclosed herein.