HK1252195B - Techniques for case allocation - Google Patents

Description

Techniques used for case assignment

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application claims priority to U.S. Provisional Patent Application No. 62/261,780, filed December 1, 2015, the entire contents of which are incorporated herein by reference.

Technical Field

The present disclosure relates generally to customer service/contact center case assignment and, more particularly, to techniques for assigning cooperative and non-cooperative cases to agents using behavior pairing.

Background Art

In some customer service centers, cases can be assigned to agents (e.g., analysts, experts) for service. For example, insurance claims can be assigned to insurance adjusters or other agents for subrogation or other processing; patients or other insured persons can be assigned to nurses, pharmacists or other clinical support experts; debt collectors can be assigned to debtor cases; and so on. These cases can be assigned in various ways. In some customer service centers (including, for example, workflow, case management or transaction processing services or support organizations), cases can be assigned to agents based on arrival time. This strategy can be called a "first in, first out," "FIFO," or "round-robin" strategy. In some customer service centers, management (e.g., managers or supervisors) can assign cases to agents (including other types of experts (such as those mentioned above)) based on information known to management (such as information about the skills or historical performance of the agent) using specific basic principles. For some cases, management may be less confident in their allocation or lack relevant information to make the best allocation.

Furthermore, in some customer contact centers, cases or contacts can be assigned to agents for service. For example, a "lead list" of contacts can be generated for each agent who receives a contact (e.g., using an outbound dialer). These contacts can be assigned to agents using a FIFO strategy. In other environments, contacts can be assigned to agents using other methods (such as administrative-based assignment).

In view of the foregoing, it can be appreciated that there may be significant problems and shortcomings associated with current FIFO or other strategies for managing allocations.

Summary of the Invention

Techniques for case assignment are disclosed. In one embodiment, the techniques can be implemented as a method for case assignment, comprising: receiving, by at least one computer processor, at least one case assignment assigned using a first pairing strategy; and then reallocating, by the at least one computer processor, the at least one case assignment using behavioral pairing.

In accordance with other aspects of this particular embodiment, the first pairing strategy is assigned by administration.

In accordance with other aspects of this particular embodiment, the first pairing strategy is a first-in-first-out (FIFO) pairing strategy.

In accordance with further aspects of this particular embodiment, a subsequent reassignment of the at least one case assignment using the first pairing strategy may be received by the at least one computer processor.

In accordance with other aspects of this particular embodiment, a subsequent restoration of the at least one case assignment using the first pairing strategy may be received by the at least one computer processor.

According to other aspects of this particular embodiment, a plurality of case assignments assigned using the first pairing strategy are received by the at least one computer processor, the plurality of case assignments can be split by the at least one computer processor into at least a first portion of cases and a second portion of cases, and the second portion of case assignments can be reallocated by the at least one computer processor using behavior pairing without reallocating the first portion of case assignments.

In accordance with other aspects of this particular embodiment, a performance difference between the first portion of case assignments and the second portion of case assignments may be determined by the at least one computer processor.

In accordance with other aspects of this particular embodiment, segmenting the plurality of cases is based at least in part on at least one rationale from management for at least one of the plurality of case assignments.

In accordance with other aspects of this particular embodiment, segmenting the plurality of cases is based at least in part on at least one confidence level from management for at least one of the plurality of case assignments.

In another specific embodiment, the technology can be implemented as a system for case assignment, the system comprising: at least one computer processor configured to receive at least one case assignment assigned using a first pairing strategy, and then reallocate the at least one case assignment using behavior pairing. The system can also include at least one memory coupled to the at least one computer processor and configured to provide instructions to the at least one computer processor.

In another particular embodiment, the technology can be implemented as an article of manufacture for case assignment, the article of manufacture comprising: at least one non-transitory computer processor-readable medium and instructions stored on the at least one medium, wherein the instructions are configured to be readable from the at least one medium by at least one computer processor and thereby cause the at least one computer processor to operate to receive at least one case assignment assigned using a first pairing strategy and then reallocate the at least one case assignment using behavioral pairing.

The present disclosure will now be described in more detail with reference to specific embodiments thereof as shown in the accompanying drawings. Although the present disclosure is described below with reference to specific embodiments, it should be understood that the present disclosure is not limited thereto. Those of ordinary skill in the art having access to the teachings herein will recognize additional embodiments, modifications and examples, as well as other areas of use, which are within the scope of the present disclosure as described herein and may have significant utility with respect to the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate a more complete understanding of the present disclosure, reference is now made to the accompanying drawings, in which like elements are referenced with like reference numerals. These drawings should not be construed as limiting the present disclosure, but are intended to be illustrative only.

FIG1 shows a flowchart of a collaborative distribution system according to an embodiment of the present disclosure.

FIG2 shows a flow chart of a collaborative allocation method according to an embodiment of the present disclosure.

FIG3 shows a schematic representation of case segmentation according to an embodiment of the present disclosure.

FIG4 shows a flowchart of a non-cooperative allocation system according to an embodiment of the present disclosure.

FIG5 shows a flowchart of a non-cooperative allocation method according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

In some customer service centers, cases can be assigned to agents (e.g., analysts, experts) for service. For example, insurance claims can be assigned to insurance adjusters or agents for subrogation or other processing; patients or other insured persons can be assigned to nurses, pharmacists or other clinical support experts; debt collectors can be assigned to debtor cases; and so on. These cases can be assigned in various ways. In some customer service centers (including, for example, workflow, case management or transaction processing services or support organizations), cases can be assigned to agents based on arrival time. This strategy can be called a "first in, first out", "FIFO" or "round robin" strategy. In some customer service centers, management (e.g., managers or supervisors) can assign cases to agents (including other types of experts (such as those mentioned above)) based on information known to management (such as information about the skills or historical performance of the agent) using specific basic principles. For some cases, management may be less confident in their allocation or lack relevant information to make the best allocation.

Furthermore, in some customer contact centers, cases or contacts can be assigned to agents for service. For example, a "lead list" of contacts can be generated for each agent to whom a contact is made (e.g., using an outbound dialer). These contacts can be assigned to agents using a FIFO strategy. In other environments, contacts can be assigned to agents using other methods (such as administrative-based assignment).

In some embodiments, administrative allocation can use an automated case allocation system (such as a behavioral pairing module as described in U.S. Patent No. 14/871,658, filed on September 30, 2015, now U.S. Patent No. 9,300,802, issued on March 29, 2016, and incorporated herein by reference). In this way, a collaborative allocation system can optimize case allocation using a big data, artificial intelligence pairing scheme (e.g., a behavioral pairing module) with administrative experts (e.g., an administrative allocation module), which results in increased performance in a customer service center. For example, collaborative allocation of cases or other uses of behavioral pairing can result in increased subrogation for insurance claims, improved care for medical patients, improved debt collection, and the like. In other embodiments, behavioral pairing and administrative-based pairing can be performed separately in a non-collaborative manner.

In some embodiments, behavior matching can be performed "offline" (eg, not in real time) to assign cases, generate lead lists, or perform other types of contact assignments using collaborative or non-collaborative techniques.

Furthermore, the increased performance of the cooperative or non-cooperative allocation cases compared to the managed allocation case can be precisely measurable as a gain (e.g., 1%, 3%, 5%, etc.) In some embodiments, the gain can be precisely measured using a benchmarking module as described in U.S. patent application Ser. No. 15/131,915, filed on April 18, 2016.

FIG1 depicts a workflow of a collaborative distribution system 100 according to some embodiments of the present disclosure.

Cases for assignment 110 may be received, for example, by an administration assignment module 120 at a customer service center. Administration assignment module 120 may be provided solely by a customer service center (including other types of customer service centers and the aforementioned support organizations), or it may be provided in whole or in part as a component of a collaborative assignment system.

The management assignment module 120 can output initial assignment data 130. The initial assignment data 130 can include pairings of cases with agents (including other types of agents and the aforementioned experts), and it can include management rationales for these pairings. For example, each pairing can have an associated score that represents the confidence (e.g., certainty) of the management in a particular pairing. In some embodiments, each pairing can have one or more associated reason codes or other codes that indicate the reason for the management of a particular pairing (e.g., well-suited to the agent's skills or personality, given information about the agent known to be managing). The pairing can include an expected level of time or effort (e.g., intensity) required to resolve the case. The pairing can also take into account balancing caseloads across agents, including the ability of agents to take on additional cases with different requirements for time or effort.

Initial assignment data 130 may be analyzed by a behavioral matching module 140 (or similar matching engine). At this point, some cases may be excluded (i.e., held or frozen) by management. For example, if management has expressed a high confidence level or specific reason code for a case, or if the behavioral matching module 140 has determined a low ability to improve upon the initial assignment, the behavioral matching module 140 will not consider reassigning the case.

The remaining cases can be split into cases that can be reallocated (e.g., an optimization or "on" group) and cases that cannot be reallocated (e.g., a control or "off" group). This split can be done according to any of a number of possible splitting strategies. For example, management can provide a seed to a pseudo-random number generator, which can be used to randomly distribute the cases to one group or the other. In some embodiments, the cases will be divided evenly between the groups. In other embodiments, an uneven distribution of cases can be used. For example, 80% of the cases available for reallocation can be split into the optimization group, while 20% of the cases available for reallocation can be split into the control group. The techniques used to split the cases between the groups can be designed to ensure transparency and fairness when benchmarking performance.

After the cases are split, the cases in the optimized group can be reallocated by the behavior matching module 140 or similar automatic matching technology. In some embodiments, the behavior matching module 140 may include data about agents and management (e.g., agent survey data 150A, management survey data 150B, historical data 150C). The survey may include self-assessment questions (e.g., What types of cases are you most proficient in? What types of cases do you prefer to handle? Which stage of the case are you most proficient in? Which stage of the case do you prefer to handle?). For management, the survey questions can be aimed at understanding the rationale for assigning specific types of cases or cases at specific stages to managers of specific agents. Historical data may include information such as historical case assignments and results, case "scores," or other case evaluations and other baseline performance measurements before assignment. The behavior matching module 140 may also search/analyze/process other data sources that may be relevant to optimizing assignments and creating artificial intelligence models. The behavior matching module 140 may consider any stage of the case management process (such as workflow, case management, transaction processing, etc.) to optimize case assignments.

The behavior pairing module 140 may output reallocation data 160, which may include pairs from the optimized group that have been reallocated to different agents. In some embodiments, the reallocation data 160 may be reviewed by the management assignment module 120, and the management assignment module 120 may optionally output revised reallocation data 170. For example, the revised reallocation data 170 may optionally "undo," restore, or otherwise change some of the reallocated pairs, for example, based on information known to management.

Subsequently, the benchmarking module 180 can measure the gains in performance attributable to the collaboration between management and the behavioral pairing module 140. The benchmarking module 180 can process the results of each pairing to determine the relative performance of the cases in the optimized or "on" group of collaborative assignments relative to the performance of the cases in the control or "off" group assigned only by management. The benchmarking module 180 can output a performance measurement 190 (e.g., a gain) or other information about the performance of the collaborative assignment system 100.

When new cases for assignment (e.g., case for assignment 110) arrive or otherwise become ready to be assigned among agents, the collaborative assignment system 100 can repeat the process. In some embodiments, the management assignment module 120 or the behavior pairing module can process the results from the previous iteration to improve the management process (e.g., train managers on specific rationales that are more or less effective than other rationales) or the behavior pairing process (e.g., train or update artificial intelligence algorithms or models).

In some embodiments, the collaborative assignment system 100 can operate "online" (e.g., in real time) as cases arrive in a queue or as administrative assignments are made. In other embodiments, the collaborative assignment system 100 can operate "offline" (e.g., not in real time) so that groups of cases can be reassigned or otherwise assigned together.

2 shows a flow chart of a collaborative allocation method 200 according to an embodiment of the present disclosure. At block 210, the collaborative allocation method 200 may begin.

At block 210, preparation information for collaborative assignment can be processed. For example, an assignment or pairing module (e.g., behavioral pairing module 140) can receive agent survey data, management survey data, historical data, or other information to process in preparation for reassigning or otherwise assigning a case to an agent. The collaborative assignment method 200 can proceed to block 220.

At block 220 , initial allocation data (eg, initial management allocation data) may be received. In some embodiments, a rationale for managing allocations may also be received. The collaborative allocation method 200 may proceed to block 230 .

At block 230, a portion of the case may be split off for reallocation, while another portion of the case may be excluded from potential reallocation (reserved, frozen, or otherwise blocked). In some embodiments, these cases may also be excluded from the benchmark results. The collaborative allocation method 200 may proceed to block 240.

At block 240, a portion of the case that was split for reallocation can be reallocated. In some embodiments, the reallocation can be performed by a pairing module (such as behavioral pairing module 140). In some embodiments, the reallocation data can be output or otherwise returned for management review or further allocation. In some embodiments, a portion of the case that was split for reallocation can be assigned to a control group and will not be reallocated. The collaborative allocation method 200 can proceed to block 250.

At block 250, revisions to the reallocation, if any, may be received. In some embodiments, at block 240, management may revise, revert, or otherwise alter the reallocation performed by the pairing module. The revision or revert case may be included or excluded from the benchmark results. The collaborative allocation method 200 may proceed to block 260.

The relative performance of the collaboratively assigned cases and the managed assigned cases can be benchmarked or otherwise measured at block 260. In some embodiments, the results from the comparison can be used to improve the pairing module (e.g., the artificial intelligence model of the behavioral pairing module 140) or the rationale for subsequent management of managed assignments, or both.

After block 260, the collaborative assignment method 210 may end. In some embodiments, the collaborative assignment method 200 may return to block 210 to begin assigning additional cases.

Figure 3 depicts a schematic representation of case segmentation in accordance with an embodiment of the present disclosure. As shown in Figure 3, seven agents can be assigned up to nine cases. Some cases can be designated as "in progress" (e.g., cases that were previously assigned but not yet completed). "Excluded" (i.e., frozen or blocked) cases are cases assigned to an agent that are determined to be unavailable for reallocation. "Management" cases are cases assigned to an agent that are available for reallocation but are assigned to a control group. "Joint" cases are cases assigned to an optimization group that are jointly/collaboratively reallocated and/or revised by management.

In the example of Figure 3, seven agents (labeled 1 to 7 in the "Agent" column) have a directory or queue of nine cases (labeled "Case 1" or "Case 9" in the header row). The first case for agent 1 ("Case 1") is identified by an "O" for ongoing, and cases 2-9 have been split for allocation or co-allocation: cases 3 and 8 ("E") have been excluded from co-allocation and can optionally be excluded from any benchmarking or relative performance analysis. Cases 4, 5, and 7 ("M") have been allocated by management and can be benchmarked as part of a control or shutdown cycle. Cases 2, 6, and 9 ("J") have been allocated by an automated pairing strategy (such as behavioral pairing) and can be benchmarked as part of an optimization or opening cycle. In the case of co-allocation, optimized pairing can be performed in conjunction with management. In other embodiments (such as non-cooperative allocation), optimized pairing can be made independently by a pairing strategy (such as behavioral pairing) without revisions or reallocations caused by management. The remaining agents (Agents 2 through 7) have been assigned or reallocated up to nine available cases in a similar manner. As agents close cases in their catalogs or queues, and as more cases become available for assignment, these new cases can be divided for assignment or reallocation among the available agents, depending on the cooperative or non-cooperative assignment technique used for the group of agents.

The results of each case can be associated with whether the case was ongoing, excluded, managed allocation, or co-allocated using a pairing strategy (such as behavioral pairing). The relative performance of different allocation methods can be benchmarked or otherwise measured. For example, the performance gain attributable to co-allocated cases using behavioral pairing over managed allocation cases can be benchmarked.

FIG4 depicts a workflow of a non-cooperative allocation system 400 according to some embodiments of the present disclosure.

Cases for allocation 110 can be received at a contact center. Cases can be divided into two or more groups for allocation by different strategies. In some embodiments, a portion of the case can be allocated randomly, on a FIFO basis, by management, or other case allocation techniques. The second portion of the case is allocated using a pairing strategy (such as behavioral pairing). In some embodiments, as in the example of FIG4 , the first portion of the case can be received by the management allocation module 120, and the second portion of the case can be received by the behavioral pairing module 140.

The management assignment module 120 can output management assignment data 410. The management assignment data 410 can include pairings of cases with agents, and it can include management rationales for these pairings. For example, each pairing can have an associated score that represents the confidence (e.g., certainty) of the management in the particular pairing. In some embodiments, each pairing can have one or more associated reason codes or other codes that indicate the reason for the management for the particular pairing (e.g., taking into account information about the agent known to the management, the agent's skills or personality). The pairing can include an expected level of time or effort (e.g., intensity) required to resolve the case. The pairing can also take into account balancing caseloads across agents, including the ability of agents to take on additional cases with different requirements for time or effort.

The behavior matching module 140 can output behavior matching assignment data 420. In some embodiments, the behavior matching module 140 can include data about agents and management (e.g., agent survey data 150A, management survey data 150B, historical data 150C). The survey can include self-assessment questions (e.g., What types of cases are you most proficient in? What types of cases do you prefer to handle? What stage of the case are you most proficient in? What stage of the case do you prefer to handle?). For management, the survey questions can be directed to understanding the rationale for assigning specific types of cases or cases at specific stages to specific agents. Historical data can include information such as historical case assignments and outcomes, case "scores," or other case evaluations prior to assignment and other baseline performance measurements. The behavior matching module 140 can also search/analyze/process other data sources that may be relevant to optimizing assignments and creating artificial intelligence models.

Subsequently, the benchmarking module 180 can measure the performance gain attributable to the behavioral pairing module 140 compared to the administrative assignment module (or other assignment processes such as random or FIFO processes). The benchmarking module 180 can process the results of each pairing to determine the relative performance of the cases in the optimized group using only behavioral pairing assignments, relative to the performance of the cases in the control group using only administrative assignments. The benchmarking module 180 can output performance measurements 190 or other information about the performance of the non-cooperative assignment system 400.

As new cases for assignment (e.g., case for assignment 110) arrive or otherwise become ready to be assigned among agents, the non-collaborative assignment system 400 can repeat the process. In some embodiments, the management assignment module 120 or the behavior pairing module 140 can process the results of earlier iterations to improve the management process (e.g., training managers on specific rationales that are more or less effective than other rationales) or the behavior pairing process (e.g., training or updating artificial intelligence algorithms or models).

In some embodiments, the non-collaborative assignment system 400 can operate "online" (e.g., in real time) as cases arrive in a queue or as administrative assignments are made. In other embodiments, the non-collaborative assignment system 400 can operate "offline" (e.g., in non-real time) so that groups of cases can be reassigned or otherwise assigned together.

5 shows a flow chart of a non-cooperative allocation method according to an embodiment of the present disclosure.At block 510, the non-cooperative allocation method 210 may begin.

At block 510, preparation information for non-collaborative assignment can be processed. For example, an assignment or pairing module (e.g., behavioral pairing module 140) can receive agent survey data, management survey data, historical data, or other information for processing in preparation for assigning or otherwise allocating a case to an agent. The non-collaborative assignment method 500 can proceed to block 520.

At block 520 , the case may be segmented into a first portion and a second portion of the one or more cases. The non-collaborative assignment method 500 may proceed to block 530 .

At block 530 , allocation data may be received for a portion of the case that was partitioned for management allocation (or, for example, random or FIFO allocation). The non-cooperative allocation method 500 may proceed to block 540 .

At block 540 , the second portion of the case may be assigned using a pairing strategy, such as behavioral pairing (BP). The non-collaborative assignment method 500 may proceed to block 550 .

The relative performance of the behavioral pairing assignment cases and the management assignment cases can be benchmarked or otherwise measured at block 550. In some embodiments, the results from the comparison can be used to improve the pairing module (e.g., the artificial intelligence model of the behavioral pairing module 140) or the rationale for the management of subsequent management assignments, or both.

After block 550, the non-collaborative allocation method 210 can end. In some embodiments, the non-collaborative allocation method 500 can return to block 510 to begin allocating additional cases.

At this point, it should be noted that the cooperative and non-cooperative allocation of the use behavior pairing according to the present disclosure as described above can include the processing of input data and the generation of output data to some extent. The input data processing and output data generation can be implemented in hardware or software. For example, specific electronic components can be adopted in cooperative and non-cooperative allocation modules, behavior pairing modules, benchmarking modules and/or similar or related circuits for realizing the functions associated with the cooperative and non-cooperative allocation of the use behavior pairing (such as in the workflow management system, contact center system, case management system, etc. according to the present disclosure as described above). Alternatively, one or more processors operating according to instructions can realize the functions associated with the cooperative and non-cooperative allocation of the use behavior pairing according to the present disclosure as described above. If this is the case, then within the scope of the present disclosure, such instructions can be stored on one or more non-transitory computer processor readable storage media (for example, disks or other storage media), or transmitted to one or more processors via one or more signals implemented in one or more carriers.

The present disclosure will not be limited in scope by the specific embodiments described herein. In fact, in addition to those described herein, various other embodiments and modifications of the present disclosure will be apparent to those of ordinary skill in the art from the foregoing description and accompanying drawings. Therefore, such other embodiments and modifications are intended to fall within the scope of the present disclosure. Further, although the present disclosure has been described herein in the context of at least one specific embodiment in at least one specific environment for at least one specific purpose, those of ordinary skill in the art will recognize that its usefulness is not limited thereto, and the present disclosure can be beneficially implemented in any number of environments for any number of purposes. Therefore, the claims set forth below should be interpreted in light of the full breadth and spirit of the present disclosure as described herein.

Claims (27)

1. A method for assigning cases to agents in a service center system, the method being performed by an assignment system including a management assignment module and a behavior matching module, both the management assignment module and the behavior matching module being communicatively coupled to and configured to operate in the service center system, the method comprising: The management allocation module receives at least one case allocation previously assigned using the first pairing strategy; The behavior matching module selects a case assignment for reassignment from the at least one case assignment; The behavior matching module reassigns selected cases to agents based on behavior matching strategies to optimize the performance of the service center system. The behavioral matching strategy utilizes one or more of the survey data and historical data. Wherein, the behavior matching strategy differs from the first matching strategy, and The optimized performance of the service center system can be attributed to the reallocation of the selected cases using the behavior matching strategy. The behavior matching module outputs case allocation data for reallocation in the service center system; and In the exchange module of the service center system, a connection is established between the case and the agent based on the redistributed data. Wherein, the case is associated with the selected case assignment, and The connection between the case and the agent is a reassignment of cases previously assigned using the first pairing strategy. 2. The method of claim 1, wherein the first pairing strategy is assigned by an administrative body. 3. The method according to claim 1, wherein the first pairing strategy is a first-in-first-out (FIFO) pairing strategy. 4. The method of claim 1, further comprising receiving, through the management allocation module, a subsequent reallocation of the at least one case allocation using the first pairing strategy. 5. The method according to claim 1, further comprising outputting subsequent recovery of the redistributed data using the first pairing strategy through the management allocation module. 6. The method of claim 1, further comprising: The management allocation module receives multiple case allocations previously assigned using the first pairing strategy. The behavior matching module assigns and divides the multiple cases into at least a first part of the cases and a second part of the cases; and The behavior matching module reassigns the second part of the case assignment without reassigning the first part of the case assignment using the behavior matching strategy. 7. The method of claim 6, wherein the allocation system further includes a benchmarking module communicatively coupled to and configured to operate in the service center system, the method further comprising: determining, through the benchmarking module, a performance difference between the first portion of the case allocation and the second portion of the case allocation. 8. The method of claim 6, wherein the segmentation of the plurality of case portions is based on at least one fundamental principle from an administrative body for allocating at least one of the plurality of case portions. 9. The method of claim 6, wherein the segmentation of the plurality of cases is based on at least one confidence level from a management body for at least one of the plurality of cases allocations. 10. A system for assigning cases to agents in a service center system, comprising: A management allocation module and a behavior matching module, both communicatively coupled to and configured to operate within the service center system, wherein the module for allocating cases to agents within the service center system is configured as follows: The management allocation module receives at least one case allocation previously assigned using the first pairing strategy; The behavior matching module selects a case assignment for reassignment from the at least one case assignment; The behavior matching module reassigns selected cases to agents based on behavior matching strategies to optimize the performance of the service center system. The behavioral matching strategy utilizes one or more of the survey data and historical data. Wherein, the behavior matching strategy differs from the first matching strategy, and The optimized performance of the service center system can be attributed to the reallocation of the selected cases using the behavior matching strategy. The behavior matching module outputs case allocation data for reallocation in the service center system; and In the exchange module of the service center system, a connection is established between the case and the agent based on the redistributed data. Wherein, the case is associated with the selected case assignment, and Wherein, the connection between the case and the agent is the reassignment of cases previously assigned using the first pairing strategy, and At least one memory is coupled to the management allocation module and configured to provide instructions to the management allocation module. 11. The system of claim 10, wherein the first pairing strategy is assigned by a management authority. 12. The system of claim 10, wherein the first pairing strategy is a first-in-first-out (FIFO) pairing strategy. 13. The system of claim 10, wherein the management allocation module is further configured to receive subsequent reallocation of the at least one case allocation using the first pairing strategy. 14. The system of claim 10, wherein the management allocation module is further configured to output subsequent recovery of the redistributed data using the first pairing strategy. 15. The system of claim 10, further configured as follows: The management allocation module receives multiple case allocations previously assigned using the first pairing strategy. The behavior matching module assigns and divides the multiple cases into at least a first part of the cases and a second part of the cases; and The behavior matching module reassigns the second part of the case assignment without reassigning the first part of the case assignment using the behavior matching strategy. 16. The system of claim 15, further comprising a benchmarking module, the benchmarking module being communicatively coupled to and configured to operate in the service center system. The benchmark module is further configured to: determine the performance difference between the first part of the case allocation and the second part of the case allocation. 17. The system of claim 15, wherein the segmentation of the plurality of cases is based on at least one fundamental principle from an administrative body for at least one of the plurality of cases allocation. 18. The system of claim 15, wherein the segmentation of the plurality of cases is based on at least one confidence level from a management body for at least one of the plurality of cases allocations. 19. A non-transitory computer processor-readable storage medium for assigning cases to agents in a service center system, comprising instructions, wherein the instructions are configured to be readable from the storage medium by at least one computer processor communicatively coupled to and configured to operate in the service center system, and thereby cause the at least one computer processor to operate such that: A management allocation module communicatively coupled to the service center system and configured to operate in the service center system receives at least one case allocation previously allocated using a first pairing strategy. A behavior matching module, communicatively coupled to and configured to operate in the service center system, selects a case assignment for reassignment from the at least one case assignment; The behavior matching module reassigns selected cases to agents based on behavior matching strategies to optimize the performance of the service center system. The behavioral matching strategy utilizes one or more of the survey data and historical data. Wherein, the behavior matching strategy differs from the first matching strategy, and The optimized performance of the service center system can be attributed to the reallocation of the selected cases using the behavior matching strategy. The behavior matching module outputs case allocation data for reallocation in the service center system; and In the exchange module of the service center system, a connection is established between the case and the agent based on the redistributed data. Wherein, the case is associated with the selected case assignment, and The connection between the case and the agent is a reassignment of cases previously assigned using the first pairing strategy. 20. The storage medium of claim 19, wherein the first pairing policy is assigned by a management authority. 21. The storage medium according to claim 19, wherein the first pairing strategy is a first-in-first-out (FIFO) pairing strategy. 22. The storage medium of claim 19, wherein the at least one computer processor is further configured to: receive, via the management allocation module, a subsequent reallocation of the at least one case allocation using the first pairing strategy. 23. The storage medium of claim 19, wherein the at least one computer processor is further configured to: output subsequent recovery of the reallocated data using the first pairing strategy via the management allocation module. 24. The storage medium of claim 19, wherein the at least one computer processor is further configured to: The management allocation module receives multiple case allocations previously assigned using the first pairing strategy. The behavior matching module assigns and divides the multiple cases into at least a first part of the cases and a second part of the cases; and The behavior matching module reassigns the second part of the case assignment without reassigning the first part of the case assignment using the behavior matching strategy. 25. The storage medium of claim 24, wherein the at least one computer processor is further configured to: determine a performance difference between the first portion of the case allocation and the second portion of the case allocation. 26. The storage medium of claim 24, wherein the partitioning of the plurality of case portions is based on at least one fundamental principle from an administrative body for at least one of the plurality of case allocations. 27. The storage medium of claim 24, wherein the partitioning of the plurality of case portions is based on at least one confidence level from a management authority for at least one of the plurality of case allocations.