Technology for behavioral matching in contact center systems

Abstract

Techniques for behavioral pairing in a contact center system are disclosed. In a specific embodiment, the techniques may be implemented as a method for behavioral pairing in a contact center system, including: determining, by at least one computer processor communicatively coupled and configured to operate in the contact center system, a plurality of agents available for connection to a contact; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the contact and the plurality of agents; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings based on a probability model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for the contact in the contact center system.

Description

[0001] The original application of this application is an invention patent application with the application date of April 5, 2018, the application number of "201880001943.2", the applicant of which is "European Afiniti Technologies Co., Ltd.", and the invention name of "Technology for Behavior Matching in Contact Center Systems". At the same time, this case is directed to the following divisional application, with the application date of April 5, 2018, the application number of which is 202010735874.7, the applicant of which is "Afiniti Co., Ltd.", and the invention name of "Technology for Behavior Matching in Contact Center Systems".

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This international patent application claims priority to U.S. Patent Application No. 15 / 582,223, filed on April 28, 2017, and claims priority to U.S. Patent Application No. 15 / 691,106, filed on August 30, 2017, which is a continuation-in-part of U.S. Patent Application No. 15 / 582,223, filed on April 28, 2017, the entire contents of which are hereby incorporated by reference into this document as if fully set forth herein. Technical Field

[0004] The present disclosure relates generally to pairing contacts and agents in contact centers and, more particularly, to techniques for behavior pairing in contact center systems. Background Art

[0005] A typical contact center algorithmically assigns contacts that arrive at the contact center to agents that are available to handle those contacts. Sometimes, a contact center may have agents available and waiting to be assigned to inbound or outbound contacts (e.g., phone calls, Internet chat sessions, emails). At other times, a contact center may have contacts waiting in one or more queues for agents to become available for assignment.

[0006] In some typical contact centers, contacts are assigned to agents sorted based on arrival time, and agents receive contacts based on that time sorting as they become available. This strategy may be referred to as a "first in, first out," "FIFO," or "round robin" strategy. In other typical contact centers, other strategies may be used, such as a "performance-based routing" or "PBR" strategy.

[0007] In other more advanced contact centers, contacts are paired with agents using a "behavior pairing" or "BP" strategy, under which contacts and agents may be intentionally (preferably) paired in a manner that enables assignment of subsequent contact-agent pairs, such that when the benefit of all assignments under the BP strategy is aggregated, the benefit of FIFO and other strategies such as a performance-based routing ("PBR") strategy may be exceeded. BP is designed to encourage balanced utilization (or a degree of utilization skew) of agents within skilled queues, while still improving overall contact center performance beyond what FIFO or PBR approaches would allow. This is a remarkable achievement because BP operates on the same calls and the same agents as FIFO or PBR approaches, providing approximately even utilization of agents as FIFO does, yet still improving overall contact center performance. BP is described, for example, in U.S. Patent No. 9,300,802, the entire contents of which are incorporated herein by reference. Additional information regarding these and other features of a pairing or matching module (sometimes also referred to as a "SATMAP," "routing system," "routing engine," etc.) is described in, for example, U.S. Patent No. 8,879,715, the entire contents of which are incorporated herein by reference.

[0008] The BP strategy can be combined with a diagonal strategy for determining preferred pairings, using a one-dimensional ranking of agents and contact types. However, this strategy can restrict or otherwise limit the type and number of variables that the BP strategy can optimize, or the amount of one or more variables that can be optimized given more degrees of freedom.

[0009] In view of the above, it can be appreciated that there is a need for a system that can improve the efficiency and performance of pairing strategies designed to select among multiple possible pairings, such as a BP strategy. Summary of the invention

[0010] Techniques for behavioral pairing in a contact center system are disclosed. In a specific embodiment, the techniques can be implemented as a method for behavioral pairing in a contact center system, comprising: determining, by at least one computer processor communicatively coupled and configured to operate in the contact center system, a plurality of agents available for connection to a contact; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the contact and the plurality of agents; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings based on a probability model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for contact in the contact center system.

[0011] According to other aspects of this particular embodiment, the probability model may be a network flow model for balancing agent utilization, a network flow model for applying agent utilization skew, or a network flow model for optimizing a total expected value of at least one contact center indicator. Meanwhile, the at least one contact center indicator may be at least one of revenue generation, customer satisfaction, and average handling time.

[0012] According to other aspects of this particular embodiment, the probability model may be a network flow model subject to constraints of agent skills and contact skill requirements. At the same time, the network flow model may be adjusted to minimize agent utilization imbalance according to the constraints of the agent skills and the contact skill requirements.

[0013] According to other aspects of this particular embodiment, the probabilistic model may incorporate an expected return value based on an analysis of at least one of historical contact-agent outcome data and contact attribute data.

[0014] In another specific embodiment, these techniques can be implemented as a system for behavior pairing in a contact center system, including at least one computer processor configured to operate in the contact center system, wherein the at least one computer processor is configured to perform the steps in the above method.

[0015] In another specific embodiment, these techniques can be implemented as an article of manufacture for behavior pairing in a contact center system, comprising a non-transitory computer processor readable medium; and instructions stored on the medium; wherein the instructions are configured to be read from the medium by at least one computer processor configured to operate in the contact center system, thereby causing the at least one computer processor to operate to perform the steps in the above method.

[0016] The present disclosure will now be described in more detail with reference to specific embodiments as shown in the accompanying drawings. Although the present disclosure is described hereinafter with reference to specific embodiments, it should be understood that the present disclosure is not limited thereto. Those of ordinary skill in the art who have access to the teachings herein will recognize additional embodiments, improvements and embodiments, and other fields of use within the scope of the present disclosure described herein, and the present disclosure may have important utility with respect to additional embodiments, improvements and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 represented by like numerals. These drawings should not be construed as limiting the present disclosure, but are intended to be used for illustration only.

[0018] Figure 1 A block diagram of a contact center according to an embodiment of the present disclosure is shown.

[0019] Figure 2 An example of a BP expenditure matrix according to an embodiment of the present disclosure is shown.

[0020] Figure 3 The naive BP utilization moment ( BP utilization matrix) matrix example.

[0021] Figure 4A An example of a BP skill-based expenditure matrix according to an embodiment of the present disclosure is shown.

[0022] Figure 4B An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0023] Figure 4C An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0024] Figure 4D An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0025] Figure 4E An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0026] Figure 4F An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0027] Figure 4G An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0028] Figure 5A An example of a BP skill-based expenditure matrix according to an embodiment of the present disclosure is illustrated.

[0029] Figure 5B An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0030] Figure 5C An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0031] Figure 5D An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0032] Figure 5E An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0033] Fig. 5F An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0034] Figure 5GAn example of a BP network flow according to an embodiment of the present disclosure is shown.

[0035] Figure 5H An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0036] Fig.5I An example of a BP network flow according to an embodiment of the present disclosure is shown.

[0037] Figure 6 A flow chart of a BP skill-based expenditure matrix method according to an embodiment of the present disclosure is illustrated.

[0038] Fig. 7A A flow chart of a BP network flow method according to an embodiment of the present disclosure is shown.

[0039] Figure 7B A flow chart of a BP network flow method according to an embodiment of the present disclosure is shown.

[0040] Figure 8 A flow chart of a BP network flow method according to an embodiment of the present disclosure is shown.

[0041] Fig. 9 A flow chart of a BP network flow method according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0042] A typical contact center algorithmically assigns contacts that arrive at the contact center to agents that are available to handle those contacts. Sometimes, a contact center may have agents available and waiting to be assigned to inbound or outbound contacts (e.g., phone calls, Internet chat sessions, emails). At other times, a contact center may have contacts waiting in one or more queues for agents to become available for assignment.

[0043] In some typical contact centers, contacts are assigned to agents sorted based on arrival time, and agents receive contacts based on that time sorting as they become available. This strategy may be referred to as a "first in, first out," "FIFO," or "round robin" strategy. In other typical contact centers, other strategies may be used, such as a "performance-based routing" or "PBR" strategy.

[0044] In other more advanced contact centers, contacts are paired with agents using a "behavioral pairing" or "BP" strategy, under which contacts and agents may be intentionally (preferably) paired in a manner that enables assignment of subsequent contact-agent pairs such that when the benefit of all assignments under the BP strategy is aggregated, the benefit of FIFO and other strategies, such as a performance-based routing ("PBR") strategy, may be exceeded. BP is designed to encourage balanced utilization (or utilization skewness) of agents within skill queues while still improving overall contact center performance beyond what FIFO or PBR approaches would allow. This is a remarkable achievement because BP operates on the same calls and the same agents as FIFO or PBR approaches, providing approximately even utilization of agents as FIFO does, yet still improving overall contact center performance. BP is described, for example, in U.S. Patent No. 9,300,802, the entire contents of which are incorporated herein by reference. Additional information regarding these and other features of a pairing or matching module (sometimes also referred to as a "SATMAP," "routing system," "routing engine," etc.) is described in, for example, U.S. Patent No. 8,879,715, the entire contents of which are incorporated herein by reference.

[0045] The BP strategy can be combined with a diagonal strategy for determining preferred pairings, using a one-dimensional ranking of agents and contact types. However, this strategy can restrict or otherwise limit the type and number of variables that the BP strategy can optimize, or the amount of one or more variables that can be optimized given more degrees of freedom.

[0046] In view of the above, it can be appreciated that there is a need for a system that can improve the efficiency and performance of pairing strategies that are designed to select among multiple possible pairings such as BP strategies. Such a system can provide a myriad of benefits, including, in some embodiments, optimization based on comparative advantage at runtime; maintaining uniform or near-uniform utilization of agents; merging models across skills into a single coherent model or a smaller number of coherent models; creating more comprehensive, more complex, and more capable models, etc. As described in detail below, these techniques can be multi-dimensional (e.g., multivariate) in nature, and linear programming, quadratic programming, or other optimization techniques can be used to determine preferred contact-agent pairings. Examples of these techniques are described in, for example, Cormen et al., Introduction to Algorithms, 3rd ed., at 708–68 and 843–897 (Ch. 26. “Maximum Flow” and Ch. 29 “Linear Programming”) (2009), and Nocedal and Wright, Numerical Optimization, at 448–96 (2006), the entire contents of which are incorporated herein by reference.

[0047] Figure 1 A block diagram of a contact center system 100 according to an embodiment of the present disclosure is shown. The specification herein describes network elements, computers and / or components for simulating systems and methods of contact center systems that may include one or more modules. As used herein, the term "module" may be understood to refer to computing software, firmware, hardware and / or various combinations thereof. However, a module should not be interpreted as software that is not implemented on hardware, firmware or recorded on a processor-readable and recordable storage medium (i.e., a module is not the software itself). Note that modules are exemplary. Modules may be combined, integrated, separated and / or replicated to support various applications. In addition, instead of or in addition to functions performed at a specific module, the functions described herein as being performed at a specific module may be performed at one or more other modules and / or by one or more other devices. In addition, modules may be implemented across multiple devices and / or other components that are local or remote to each other. In addition, a module may be removed from one device and added to another device, and / or may be included in two devices.

[0048] like Figure 1 As shown, the contact center system 100 may include a central switch 110. The central switch 110 may receive incoming contacts (e.g., callers) or support outbound connections to contacts via a telecommunications network (not shown). The central switch 110 may include contact routing hardware and software to facilitate routing contacts among one or more contact centers, or to facilitate one or more PBX / ACD or other queuing or switching components, including other Internet-based, cloud-based, or otherwise networked contact-agent hardware or software-based contact center solutions.

[0049] Central switch 110 may not be necessary, such as if there is only one contact center, or if there is only one PBX / ACD routing component in contact center system 100. If more than one contact center is part of contact center system 100, each contact center may include at least one contact center switch (e.g., contact center switches 120A and 120B). Contact center switches 120A and 120B may be communicatively coupled to central switch 110. In embodiments, various topologies of routing and network components may be configured to implement a contact center system.

[0050] Each contact center switch of each contact center may be communicatively coupled to a plurality of agents (or "pools"). Each contact center switch may support a certain number of agents (or "seats") logged in at the same time. At any given time, a logged in agent may be available and waiting to be connected to a contact, or a logged in agent may be unavailable for a variety of reasons, such as being connected to another contact, performing a post-call function such as recording information about a call, or taking a break.

[0051] exist Figure 1 In the example of FIG. 1 , the center switch 110 routes the contact to one of two contact centers via the contact center switch 120A and the contact center switch 120B, respectively. Each contact center switch 120A and 120B is shown as having two agents, respectively. Agents 130A and 130B can log in to the contact center switch 120A, while agents 130C and 130D can log in to the contact center switch 120B.

[0052] The contact center system 100 may also be communicatively coupled to integration services from, for example, third-party vendors. Figure 1 In an example of the present invention, the BP module 140 can be communicatively coupled to one or more switches in the switch system of the contact center system 100, such as the central switch 110, the contact center switch 120A, or the contact center switch 120B. In some embodiments, the switches of the contact center system 100 can be communicatively coupled to multiple BP modules. In some embodiments, the BP module 140 can be embedded within a component of the contact center system (e.g., embedded in a switch or otherwise integrated with a switch, or a "BP switch"). The BP module 140 can receive information about agents logged into the switch (e.g., agents 130A and 130B) and information about incoming contacts via another switch (e.g., the central switch 110) from a switch (e.g., the contact center switch 120A), or, in some embodiments, from a network (e.g., the Internet or a telecommunications network) (not shown).

[0053] The contact center may include multiple pairing modules (e.g., BP modules and FIFO modules) (not shown), and one or more pairing modules may be provided by one or more different suppliers. In some embodiments, one or more pairing modules may be components of the BP module 140 or one or more switches, such as the central switch 110 or the contact center switches 120A and 120B. In some embodiments, the BP module may determine which pairing module may handle the pairing of a particular contact. For example, the BP module may alternate between enabling pairing via the BP module and enabling pairing via the FIFO module. In other embodiments, a pairing module (e.g., a BP module) may be configured to emulate other pairing strategies. For example, a BP module or a BP component integrated with a BP component in the BP module may determine whether the BP module may use BP pairing or emulated FIFO pairing for a particular contact. In this case, "BP on" may refer to the time when the BP module is applying the BP pairing strategy, and "BP off" may refer to other times when the BP module is applying different pairing strategies (e.g., FIFO).

[0054] In some embodiments, regardless of whether the pairing strategies are handled by separate modules or if some pairing strategies are emulated within a single pairing module, a single pairing module can be configured to monitor and store information about pairings performed under any or all pairing strategies. For example, the BP module can observe and record data of FIFO pairings performed by the FIFO module, or the BP module can observe and record data of simulated FIFO pairings made by a BP module operating in FIFO emulation mode.

[0055] Figure 2 An example of a BP payout matrix 200 according to an embodiment of the present disclosure is shown. In this simplified, hypothetical computer-generated model of a contact center system, there are three agents (agents 201, 202, and 203), and there are three contact types (contact types 211, 212, and 213). Each cell of the matrix indicates the "payout" or expected outcome or expected value of a contact-agent interaction between a particular agent and a contact of the indicated contact type. In a real-world contact center system, there may be dozens of agents, hundreds of agents, or more, and there may be dozens of contact types, hundreds of contact types, or more.

[0056] In BP payout matrix 200, the payout for an interaction between agent 201 and a contact of contact type 211 is .30 or 30%. For agent 201, the payout for contact type 212 is .28, and for contact type 213 is .15. For agent 202, the payout for contact type 211 is .30, for contact type 212 is .24, and for contact type 213 is .10. For agent 203, the payout for contact type 211 is .25, for contact type 212 is .20, and for contact type 213 is .09.

[0057] The payout may represent the expected value of any of a variety of different metrics or optimization variables. Examples of optimization variables include sales conversion rate, customer retention rate, customer satisfaction rate, average handling time metric, etc., or a combination of two or more metrics. For example, if the BP payout matrix 200 models a hold queue in a contact center system, each payout may represent the likelihood that an agent will "hold" or hold a customer of a particular contact type, e.g., there is a .30 (or 30%) chance that agent 201 will hold a contact identified as contact type 211.

[0058] In some embodiments, the BP expenditure matrix 200 or other similar computer-generated model of the contact center system can be generated using historical contact-agent interaction data. For example, the BP expenditure matrix 200 can incorporate rolling windows of historical data for weeks, months, years, etc. to predict or otherwise estimate the expenditure of a given interaction between an agent and a contact type. As the agent staff changes, the model can be updated to reflect changes in the agent staff, including hiring new agents, firing existing agents, or training new skills for existing agents. Contact types can be generated based on information about expected contacts and existing customers, such as customer relationship management (CRM) data, customer attribute data, third-party customer data, contact center data, etc., which can include various types of data, such as demographic and psychographic data, and behavioral data such as past purchases or other historical customer information. The BP expenditure matrix 200 can be updated in real time or periodically, such as every hour, every night, every week, etc., to incorporate new contact-agent interaction data as it becomes available.

[0059] Figure 3 An example of a naive BP utilization matrix 300 according to an embodiment of the present disclosure is illustrated. For the BP expenditure matrix 200 ( Figure 2 ), this simplified, hypothetical computer-generated model of a contact center system, there are three agents (agents 201, 202, and 203), and there are three contact types (contact types 211, 212, and 213). In a real-world contact center system, there may be dozens of agents, hundreds of agents, or more, and there may be dozens of contact types, hundreds of contact types, or more.

[0060] Under the BP strategy, agents are preferentially paired with contacts of a specific contact type according to a computer-generated BP model. In an L1 environment, the contact queue is empty, and multiple agents are available, idle, or otherwise ready and waiting to contact contacts. For example, in a chat scenario, an agent may have the ability to chat with multiple contacts simultaneously. In these environments, an agent may be ready to connect to one or more additional contacts while multitasking in one or more other channels such as email and chat.

[0061] In some embodiments, when a contact arrives at a queue or other component of a contact center system, the BP policy analyzes information about the contact to determine the type of contact (e.g., a contact of contact type 211, 212, or 213). The BP policy determines which agents are available to connect to the contact, and selects, recommends, or otherwise outputs pairing instructions for the most preferred available agent.

[0062] In an L2 environment, multiple contacts are waiting in a queue to be connected to an agent, and no agent is available, idle, or otherwise ready to be connected to a contact. The BP policy analyzes information about each contact to determine the type of each contact (e.g., one or more of contact types 211, 212, or 213). In some embodiments, when an agent becomes available, the BP policy determines which contacts are available to connect to the agent, and selects, recommends, or otherwise outputs pairing instructions for the most preferred available contacts.

[0063] As shown in the header row of the naive BP utilization matrix 300, each agent has an expected availability or target utilization. In this example, the BP strategy targets a balanced agent utilization of 1 / 3 (".33") for each of the three agents 201, 202, and 203. Therefore, over time, each agent is expected to be utilized equally, or approximately equally. This configuration of BP is similar to FIFO in that both BP and FIFO target unskewed or balanced agent utilization.

[0064] This configuration of BP is different from performance-based routing (PBR) because PBR targets skewed or unbalanced agent utilization, intentionally allocating a disproportionate number of contacts to relatively higher performing agents. Other configurations of BP may be similar to PBR, in that other BP configurations may also target skewed agent utilization. Additional information regarding these and other features of skewed agent or contact utilization (e.g., "kappa" and "rho" functions) is described in, for example, U.S. Patent Application Nos. 14 / 956,086 and 14 / 956,074, the entire contents of which are incorporated herein by reference.

[0065] Each contact type has an expected availability (e.g., arrival frequency) or target utilization, as shown in the title bar of the naive BP utilization matrix 300. In this example, contacts of contact type 211 are expected to be available 50% (".50") of the time, contacts of contact type 212 are expected to be available 30% (".30") of the time, and contacts of contact type 212 are expected to be available the remaining 20% ​​(".20") of the time.

[0066] Each cell of the matrix indicates a target utilization or expected frequency of contact-agent interactions between a particular agent and contacts of the indicated contact type. In the example of the naive BP utilization matrix 300, it is desired to allocate agents equally to each contact type based on the frequency of each contact type. Contacts of contact type 211 are expected to get 50% of the time in the queue, with approximately 1 / 3 of the contacts being allocated to each of agents 201, 202, and 203. Overall, contact-agent interactions between contact type 211 and agent 201 are expected to occur approximately 16% (".16") of the time, between contact type 211 and agent 202 approximately 16% of the time, and between contact type 211 and agent 203 approximately 16% of the time. Similarly, contacts of contact type 212 (30% frequency) are expected to occur approximately 10% (".01") of the time with each of the interactions between agents 201-203, respectively, and contacts of contact type 213 (20% frequency) are expected to occur approximately 7% (".07") of the time with each of the interactions between agents 201-203.

[0067] The naive BP utilization matrix 300 also represents approximately the same distribution of contact-agent interactions that would occur under the FIFO pairing strategy, under which each contact-agent interaction would be equally likely (normalized for the frequency of each contact type). Under naive BP and FIFO, the target (and expected) utilization for each agent is equal: 1 / 3 of the contact-agent interactions for each of the three agents 201-203.

[0068] In summary, the BP expenditure matrix 200 ( Figure 2 ) and the naive BP utilization matrix 300 enable the expected total performance of the contact center system to be determined by calculating the average expenditure weighted by the frequency distribution of each contact-agent interaction shown in the naive BP utilization matrix 300: (.30+.30+.25)(.50)(1 / 3)+(.28+.24+.20)(.30)(1 / 3)+(.15+.10+.09)(.20)(1 / 3)≈0.24. Therefore, the expected performance of the contact center system under naive BP or FIFO is approximately 0.24 or 24%. If the expenditure represents, for example, a retention rate, the expected total performance would be a retention rate of 24%.

[0069] Figures 4A-4G An example of a more complex BP expenditure matrix and network flow is shown. In this simplified hypothetical contact center, agents or contact types can have different combinations of one or more skills (i.e., skill sets), and linear programming-based network flow optimization techniques can be applied to improve the overall contact center performance while maintaining balanced utilization of agents and contacts.

[0070] Figure 4A FIG. 4 shows an example of a BP skill-based expense matrix 400A according to an embodiment of the present disclosure. The hypothetical contact center system represented in the BP skill-based expense matrix 400A is similar to that in the BP expense matrix 200 ( Figure 2 ) as long as there are three agents (agents 401, 402, and 403) each having an expected availability / utilization of approximately 1 / 3 or .33, and there are three contact types (contact types 411, 412, and 413) having expected frequencies / utilizations of approximately 25% (.15+.10), 45% (.15+.30), and 30% (.20+.10), respectively.

[0071] However, in this example, each agent has been assigned, trained, or otherwise made available a specific skill (or in other exemplary contact center systems, a collection of multiple skills). Examples of skills include broad skills such as technical support, billing support, sales, retention, etc.; language skills such as English, Spanish, French, etc.; narrower skills such as "Level 2 Advanced Technical Support", technical support for Apple iPhone users, technical support for Google Android users, etc.; and a variety of other skills.

[0072] Agent 401 is available for contacts requiring at least skill 421 , agent 402 is available for contacts requiring at least skill 422 , and agent 403 is available for contacts requiring at least skill 423 .

[0073] Also in this example, each type of contact may require one or more of skills 421-423. For example, a caller to a call center may interact with an interactive voice response (IVR) system, a touch-tone menu, or a live operator to determine which skills a particular caller / contact requires for an upcoming interaction. Another way to think of "skills" for a contact type is the specific need of the contact, such as buying something from an agent with sales skills, or having a technical problem solved by an agent with technical support skills.

[0074] In this example, it is expected that .15 or 15% of contacts are of contact type 411 and require skill 421 or skill 422; it is expected that .15 or 15% of contacts are of contact type 412 and require skill 421 or 422; it is expected that .20 or 20% of contacts are of contact type 413 and require skill 421 or 422; it is expected that .10 or 10% of contacts are of contact type 411 and require skill 422 or skill 423; it is expected that .30 or 30% of contacts are of contact type 412 and require skill 422 or skill 423; it is expected that .10 or 10% of contacts are of contact type 413 and require skill 422 or skill 423.

[0075] In some embodiments, an agent may be required to have a union of all skills determined to be required for a particular contact (e.g., Spanish skills and iPhone technical support skills). In some embodiments, some skills may be preferred but not required (i.e., if no agent with iPhone technical support skills is available immediately or within a threshold time, the contact may be paired with an available Android technical support agent).

[0076] Each cell of the matrix indicates the spend for a contact-agent interaction between a particular agent with a particular skill or skill set and a contact with a particular type and need (skill) or need set (skill set). In this example, agent 401 with skill 421 can be paired with any contact type 411, 412, or 413 when the contact requires at least skill 421 (with spends of .30, .28, and .15, respectively). Agent 402 with skill 422 can be paired with any contact type 411, 412, or 413 when the contact requires at least skill 422 (with spends of .30, .24, .10, .30, .24, and .10, respectively). Agent 403 with skill 423 can be paired with any contact type 411, 412, or 413 when the contact requires at least skill 423 (with spends of .25, .20, and .09, respectively).

[0077] Empty cells represent combinations of contacts and agents that will not be paired under this BP pairing strategy. For example, agent 401 with skill 421 will not be paired with contacts that do not require at least skill 421. In this example, the 18-cell spend matrix includes 6 empty cells, and 12 non-empty cells represent 12 possible pairings.

[0078] Figure 4BAn example of a BP network flow 400B according to an embodiment of the present disclosure is shown. The BP network flow 400B shows agents 401-403 as "sources" on the left side of the network (or graph), and contact types 411-413 for each skill set as "sinks" on the right side of the network. Each edge in the BP network flow 400B represents a possible pairing between an agent and a contact with a particular type and set of requirements (skills). For example, edge 401A represents a contact-agent interaction between agent 401 and a contact of contact type 411 requiring skill 421 or skill 422. Edges 401B, 401C, 402A-F, and 403A-C represent other possible contact-agent pairings for their respective agents and contacts / skills as shown.

[0079] Figure 4C 400C according to an embodiment of the present disclosure. Figure 4A ) network / graph representation. BP network flow 400C and BP network flow 400B ( Figure 4B ), except that for clarity, the identifier of each edge is not shown, and instead the payout for each edge to agent 401 is shown, e.g., .30 on edge 401A, .28 on edge 401B, and .15 on edge 401C, and the corresponding payout for each edge to agents 402 and 403.

[0080] Figure 4D 400D according to an embodiment of the present disclosure. Figure 4C ) are the same as in the example, except that for clarity, the skills of each agent 401-403 are not shown, and instead the relative "supply" provided by each agent and the "demand" required for each contact / skill combination are shown. Each agent provides a "supply" equivalent to the expected availability or target utilization of each agent (1 / 3 of the total supply for 1 or 100%, respectively). Each contact type / skill demand is equivalent to the agent supply of the expected frequency or target utilization of each contact type / skill (.15, .15, .20, .10, .30, .10 for a total demand of 1 or 100%, respectively). In this example, the total supply and demand are normalized or otherwise configured to be equal to each other, and the capacity or bandwidth along each edge is considered to be infinite or otherwise unrestricted (i.e., an edge may describe "who can be paired with whom", rather than "how much" or "how many times"). In other embodiments, there may be a supply / demand imbalance, or there may be quotas or otherwise restricted capacity set on some or all edges.

[0081] Figure 4E400E according to an embodiment of the present disclosure. Figure 4D ) except that for ease of presentation, the supply and demand have been scaled by a factor of 3000. In doing so, the supply of each agent is shown as 1000 instead of 1 / 3, and the total supply is shown as 3000. Similarly, the relative demand for each contact type / skill set is scaled, also totaling 3000. In some embodiments, no scaling is performed. In other embodiments, the scaling amount may vary and be greater or less than 3000.

[0082] Figure 4F FIG. 4 shows an example of a BP network flow 400F according to an embodiment of the present disclosure. The BP network flow 400F and the BP network flow 400E ( Figure 4E ), except that for clarity, the expenditure along each edge is not shown, but one solution of the BP network flow 400F is shown. In some embodiments, a "maximum flow" or "max flow" algorithm or other linear programming algorithm can be applied to the BP network flow 400F to determine one or more solutions for optimizing the "flow" or "distribution" of supply (source) to meet demand (receiver), which can balance the utilization of seats and contacts.

[0083] In some embodiments, the goal may also be to maximize the total expected value of the metric to be optimized. For example, in a sales queue, the metric to be optimized may be conversion rate, and the maximum flow goal is to maximize the total expected conversion rate. In an environment where multiple maximum flow solutions are available, one technique for selecting a solution may be to select the "maximum cost" or "max cost" solution, i.e., the solution that results in the highest total return at maximum flow.

[0084] In this example, agents 401-403 represent sources, while contact types 411-413 with various skill set combinations represent recipients. In some contact center environments, such as L2 (contact surplus) environments, the network flow may be reversed so that contacts waiting in the queue are sources providing supply, and possible agents that may become available are recipients providing demand.

[0085] BP network flow 400F shows the optimal flow solution determined by a BP module or similar component. According to the solution, there can be several choices, or a random choice: edge 401A (from agent 401 to contact type 411 with skills 421 and 422) has an optimal flow of 0; edge 401B (from agent 401 to contact type 412 with skills 421 and 422) has an optimal flow of 400; and edge 401C (from agent 401 to contact type 413 with skills 421 and 422) has an optimal flow of 600. Similarly, the optimal flows of edges 402A-F for agent 402 are 450, 50, 0, 300, 200 and 0 respectively; and the optimal flows of edges 403A-C for agent 403 are 0, 700 and 300 respectively. As described in detail below, this optimal flow solution describes the relative proportions of contact-agent interactions (or the relative likelihood of selecting a particular contact-agent interaction) that will achieve target utilization of agents and contacts based on spend for each pair of agent and contact type / skill set, while also maximizing the expected overall performance of the contact center system.

[0086] Figure 4G FIG. 4 shows an example of a BP network flow 400G according to an embodiment of the present disclosure. BP network flow 400G and BP network flow 400F ( Figure 4F ), except that for clarity, edges for which the best flow solution is determined to be 0 have been removed. Under the BP strategy, agents will not preferably pair with contact types for which the best flow solution is determined to be 0, despite having complementary skills and non-zero payout.

[0087] In this example, edges 401A, 402C, 402F, and 403A have been removed. The remaining edges represent preferred pairings. Thus, in an L1 (surplus agent) environment, when a contact arrives, it may be preferably paired with one of the agents for which there is an available preferred pairing. For example, a contact of contact type 411 with skills 421 and 422 may always be preferably paired with agent 402, requiring a total supply (availability) of 450 units of agent 402. As another example, a contact of contact type 412 with skills 421 and 422 may be preferably paired with agent 401 at some times and with agent 402 at some times. The total demand for this contact is 450 (based on the expected frequency of arrival of this type of contact / skill), and 400 units of supply from agent 401 are required, with 50 units of supply from agent 402 remaining.

[0088] In some embodiments, when a contact of this type / skill comes in, the BP module or similar component may select agent 401 or 402 based on the relative demand (400 and 50) made by each agent. For example, a pseudo-random number generator may be used to randomly select agent 401 or 402, weighting the random selection based on the relative demand. Thus, for each contact of this type / skill, there is a 400 / 450 (≈89%) probability that agent 401 will be selected as the preferred pairing, and a 50 / 450 (≈11%) probability that agent 402 will be selected as the preferred pairing. Over time, many contacts of this type / skill have been paired using the BP strategy, with approximately 89% of them being preferably paired with agent 401, and the remaining 11% being preferably paired with agent 402. In some embodiments, the total target utilization of agents or the target utilization of each contact type / skill may be the "bandwidth" of agents for receiving a proportional percentage of contacts.

[0089] For BP network flow 400G with this solution, the total supply from all agents is expected to meet the total demand of all contacts. Therefore, the target utilization of all agents (herein, balanced utilization) can be achieved, while also achieving a higher expected overall performance in the contact center system based on the expenditures and the relative allocation of agents to contacts along the edges with these expenditures.

[0090] Figure 5A -I shows another example of a BP expenditure matrix and network flow. For certain configurations of agents with various skill sets and contact types, the optimal or maximum flow for a given BP network flow may not perfectly balance supply and demand. This example is similar to Figures 4A-4G , except that this configuration of agents and contact types initially results in an unbalanced supply and demand. In this simplified hypothetical contact center, a quadratic programming-based technique for adjusting target utilization can be applied in conjunction with a linear programming-based network flow optimization technique to improve overall contact center performance while maintaining an optimal skewed utilization between agents and contacts to accommodate the unbalanced configuration of agents and contact types.

[0091] Figure 5A An example of a BP skill-based spending matrix 500A according to an embodiment of the present disclosure is depicted. The hypothetical contact center system represented in the BP skill-based spending matrix 500A is similar to the contact center system represented in the BP skill-based spending matrix 400A (FIG. 4) in that there are three agents (agents 501, 502, and 503) with initial expected availability / utilization rates of approximately 1 / 3 or .33, respectively. There are two contact types (contact types 511 and 512) with expected frequencies / utilization rates of approximately 40% (.30+.10) and 60% (.30+.30), respectively.

[0092] In this example, agent 501 has been assigned, trained, or otherwise available for skills 521 and 522, and agents 502 and 503 are available only for skill 522. For example, if skill 521 represents a French skill, and skill 522 represents a German skill, agent 501 may be assigned to contacts requiring either French or German, while agents 502 and 503 may only be assigned to contacts requiring German and not to contacts requiring French.

[0093] In this example, .30 or 30% of contacts are expected to be contact type 511 and require skill 521; .30 or 30% of contacts are expected to be contact type 512 and require skill 521; .10 or 10% of contacts are expected to be contact type 511 and require skill 522; and .30 or 30% of contacts are expected to be contact type 512 and require skill 522.

[0094] In this example, agent 501 can be paired with any contact (with payouts of .30, .28, .30, and .28, as shown in BP skill-based payout matrix 500A). Agents 502 and 503, who only have skill 522, can be paired with contacts of contact types 511 and 512 that require at least skill 522 (with payouts of .30, .24, .25, and .20, as shown in BP skill-based payout matrix 500A). As shown by the empty cells in BP skill-based payout matrix 500A, agents 502 and 503 will not be paired with contacts that only require skill 521. The 12-cell payout matrix includes 4 empty cells, and 8 non-empty cells represent 8 possible pairings.

[0095] Figure 5B 400B ( Figure 4B ), BP network flow 500B shows agents 501-503 as sources on the left side of the network, and contact types 512 and 5123 for each skill set as receivers on the right side of the network. Each edge in BP network flow 500B represents a possible pairing between an agent and a contact with a particular type and set of requirements (skills). Edges 501A-D, 502A-B, and 503A-B represent possible contact-agent pairings for their respective agents and contact types / skills as shown.

[0096] Figure 5C 500C is an example of a BP network flow according to an embodiment of the present disclosure. The BP network flow 500C is a BP expenditure matrix 500A ( Figure 5A). For clarity, the identifier of each edge is not shown, but the expenditure of each edge is shown, for example, .30 on edges 501A and 501D, and .28 on edges 501B and 501C, and the corresponding expenditure on each edge for seats 502 and 503.

[0097] Figure 5D An example of a BP network flow 500D according to an embodiment of the present disclosure is shown. The BP network flow 500D shows the relative initial supply provided by each agent and the demand required by each contact type / skill combination. The total supply 1 is equal to the total demand 1.

[0098] Figure 5E An example of a BP network flow 500E according to an embodiment of the present disclosure is shown. For ease of representation, the supply and demand have been scaled by a factor of 3000, and for each edge, the maximum flow solution for the initial supply is shown. According to the solution, edge 501A (from agent 501 to contact type 511 with skill 521) has an optimal flow of 900; edge 501B (from agent 501 to contact type 512 with skill 521) has an optimal flow of 100; and edges 501C and 501D have an optimal flow of 0. Similarly, the optimal flows for agent 502 are 300 and 700, respectively; and the optimal flows for agent 503 are 0 and 200, respectively.

[0099] According to this solution, agent 503 is substantially underutilized relative to agents 501 and 502. Whereas agents 501 and 502 are optimized for a full supply of 1000 units each, agent 503 is expected to use 200 units, or 1 / 5 of the supply of agent 503. In a contact center environment, agent 503 may be assigned to fewer contacts and idle more of the time relative to agents 501 and 502, or the agent may be assigned to non-preferred contacts, resulting in contact center performance that is lower than that predicted by the maximum flow solution.

[0100] Similarly, according to this solution, contacts of contact type 512 requiring skill 521 are substantially underutilized (or "underserved") relative to other contact type / skill combinations. Whereas the other contact type / skill combinations are optimized for full demand of 900, 300, and 900 units, respectively, contact type 512 requiring skill 521 is expected to receive only 100 units, or 1 / 9 of the demand for that contact type / skill. In a contact center environment, this underutilized contact type / skill combination may experience longer wait times relative to other contact type / skill combinations, or contacts may be assigned to non-preferred agents, resulting in contact center performance that is lower than that predicted by the maximum flow solution.

[0101] The solution shown in BP network flow 500E still balances overall supply and demand, but agent 503 may be selected less often than other peers, and / or some contacts may have to wait longer for the preferred agent, and / or overall contact center performance may not achieve the total expenditure expected by the maximum flow solution.

[0102] Fig. 5F and 5G As shown in BP network flow 500E ( Figure 5E ) provide techniques for some embodiments of adjusting relative agent supply to improve the balance of agent and contact utilization in a contact center system where the maximum flow solution is unbalanced.

[0103] Fig. 5F An example of a BP network flow 500F according to an embodiment of the present disclosure is shown. In the BP network flow 500F, agents that share the same skill set have been "collapsed" into a single network node. In this example, agent 502 and agent 503 have been combined into a single node of skill 522, with a total supply of 2000.

[0104] Similarly, contact types that share the same skill set have been collapsed into a single network node. In this example, contact types 511 and 512 that require skill 521 have been combined into a single node for skill 521, with a total demand of 1800, and contact types 511 and 512 that require skill 522 have been combined into a single node for skill 522, with a total demand of 1200.

[0105] In addition, the edges have been collapsed. For example, the four edges emanating from seats 502 and 503 (such as Figure 5B The edges marked in 502A, 502B, 503A and 503B) have been collapsed into a single edge emanating from the "supernode" for agents with skill 522 to the "supernode" for contact types requiring skill 522.

[0106] At this point, in some embodiments, a quadratic programming algorithm or similar technique may be applied to the folded network to adjust the relative supply of agents.

[0107] Figure 5G An example of a BP network flow 500G according to an embodiment of the present disclosure is shown. The BP network flow 500G shows the adjusted seat supply according to a solution of a quadratic programming algorithm or similar technique. In this example, the supply of seat super nodes for skills 521 and 522 is changed from BP network flow 500F ( Fig. 5F ) is adjusted from 1000 to 1800, and the supply of agent super nodes for skill 522 is adjusted from 2000 to 1200 in BP network flow 500F.

[0108] The total supply may remain the same (e.g., 3000 in this example), but the relative supply of seats for various skill combinations has been adjusted. In some embodiments, the total supply of a single supernode may be evenly distributed among the seats within the supernode. In this example, the supply of 1200 units of the agent supernode for skill 522 may be evenly divided among the seats, allocating 600 units to each of seats 502 and 503.

[0109] Figure 5H FIG. 5 shows an example of a BP network flow 500H according to an embodiment of the present disclosure. The BP network flow 500H shows the use of the BP network flow 500G ( Figure 5G ). Agent 501 has an adjusted offer of 1800, agent 502 has an adjusted offer of 600, and agent 503 has an adjusted offer of 600. According to this solution, edge 501A (from agent 501 to contact type 511 with skill 521) still has an optimal flow of 900; edge 501B (from agent 501 to contact type 512 with skill 521) now has an optimal flow of 900; and edges 501C and 501D still have optimal flows of 0. Similarly, the optimal flows for agent 502 are now 300 and 300, respectively; and the optimal flows for agent 503 are now 0 and 600, respectively.

[0110] Fig.5I An example of a BP network flow 500I according to an embodiment of the present disclosure is shown. BP network flow 500I is identical to BP network flow 500H, except that for clarity, edges for which the best flow solution is determined to be 0 have been removed. In this example, edges 501C, 501D, and 503A have been removed.

[0111] Using the solution shown in BP network flows 500H and 5001, all contact type / skill combinations can now be fully utilized (fully serviced).

[0112] In addition, the total seat utilization will become more balanced. Figure 5E ), agent 503 may be used only one-fifth of the time as agents 501 and 502. Therefore, agents 501 and 502 are each assigned approximately 45% of the contacts, while agent 503 is only assigned the remaining approximately 10% of the contacts. Under BP network flow 500H, agent 501 may be assigned approximately 60% of the contacts, while agents 502 and 503 are each assigned approximately 20% of the remaining contacts. In this example, the busiest agent (agent 501) receives only three times as many contacts as the least busy agents (agents 502 and 503), instead of receiving five times as many contacts.

[0113] Figure 6 A flow chart of a BP skills-based spending matrix method 600 according to an embodiment of the present disclosure is illustrated. The BP skills-based spending matrix method 600 may begin at block 610 .

[0114] At block 610, historical contact-agent outcome data may be analyzed. In some embodiments, a rolling window of the historical contact-agent outcome data may be analyzed, such as a one-week, one-month, ninety-day, or one-year window. The historical contact-agent outcome data may include information about individual interactions between contacts and agents, including identifiers of which agent communicated with which contact, when the communication occurred, the duration of the communication, and the outcome of the communication. For example, in a telemarketing call center, the outcome may indicate whether a sale occurred or the amount of the sale, if any. In a customer retention queue, the outcome may indicate whether the customer was retained (or "saved") or the amount of any incentive offered to retain the customer. In a customer service queue, the outcome may indicate whether the customer's needs were met or the issue was resolved, or a score (e.g., a Net Promoter Score or NPS) or a representative rating indicating customer satisfaction with the contact-agent interaction. After or while analyzing the historical contact-agent outcome data, the BP skills-based expense matrix method 600 may proceed to block 620.

[0115] At block 620, the contact attribute data may be analyzed. The contact attribute data may include data stored in one or more customer relationship management (CRM) databases. For example, a wireless telecommunications provider's CRM database may include information about the type of mobile phone used by a customer, the type of contract signed by the customer, the duration of the customer's contract, the monthly price of the customer's contract, and the tenure of the customer's relationship with the company. For another example, a bank's CRM database may include information about the type and number of accounts held by a customer, the average monthly balance of the customer's account, and the tenure of the customer's relationship with the company. In some embodiments, the contact attribute data may also include third-party data stored in one or more databases obtained from a third party. After or while analyzing the contact attribute data, the BP skill-based expenditure matrix method 600 may proceed to block 630.

[0116] At block 630, a skill set may be determined for each agent and each contact type. Examples of skills include broad skills such as technical support, billing support, sales, retention, etc.; language skills such as English, Spanish, French, etc.; narrower skills such as "Level 2 Advanced Technical Support", technical support for Apple iPhone users, technical support for Google Android users, etc.; and various other skills. In some embodiments, there may not be any unique skills, or only one skill may be identified across all agents or all contact types. In these embodiments, there may be only a single "skill set".

[0117] In some embodiments, a given contact type may require different skill sets at different times. For example, during a first call to a call center, one type of contact may have a technical issue and require an agent with technical support skills, but during a second call, the same contact of the same type may have a billing issue and require an agent with customer support skills. In these embodiments, more than one occurrence of the same contact type may be included per contact type / skill combination. After determining the skill set, the BP skills-based spend matrix method 600 may proceed to block 640.

[0118] At block 640, a target utilization rate may be determined for each agent, and an expected rate may be determined for each contact type (or contact type / skill combination). In some L1 environments, it may be a goal to balance agent utilization rates so that each agent is expected to be assigned an approximately equal number of contacts over time. For example, if a contact center environment has four agents, each agent may have a target utilization rate of 1 / 4 (or 25%). As another example, if a contact center environment has n agents, each agent may have a target utilization rate of 1 / n (or an equivalent percentage of contacts).

[0119] Similarly, expected rates for each contact type / skill may be determined based on actual rates observed in, for example, historical contact-agent outcome data analyzed at block 610. After determining target utilization and expected rates, BP skill-based spend matrix method 600 may proceed to block 650.

[0120] At block 650, a payout matrix with expected payouts for each feasible contact-agent pairing may be determined. In some embodiments, a contact-agent pairing may be feasible if the agent and the contact type have at least one skill in common. In other embodiments, a contact-agent pairing may be feasible if the agent has at least all skills required for the contact type. In other embodiments, other heuristics for feasibility may be used.

[0121] An example of a spending matrix is ​​the one referenced above. Figure 4A BP skill-based expense matrix 400A is described in detail. BP skill-based expense matrix 400A includes a set of agents with associated skills and target utilization, a set of contact types (combined with various skill sets) with expected frequencies determined based on historical contact-agent outcome data and / or contact attribute data, and a set of non-zero expected expenses for each feasible contact-agent pairing. After determining the expense matrix, BP skill-based expense matrix method 600 can proceed to block 660.

[0122] At block 660, a computer processor generated model based on the expenditure matrix can be output. For example, a computer processor embedded in a contact center system or a component thereof (e.g., a BP module) or communicatively coupled to a contact center system or a component thereof can output an expenditure matrix model to be received by another component of the computer processor or the contact center system. In some embodiments, the expenditure matrix model can be recorded, printed, displayed, transmitted, or otherwise stored for other components of the contact center system or a human administrator. After outputting the expenditure matrix model, the BP skill-based expenditure matrix method 600 can end.

[0123] Fig. 7A A flow chart of a BP network flow method 700A according to an embodiment of the present disclosure is shown. The BP network flow method 700A may start at block 710 .

[0124] At block 710, a BP expenditure matrix may be determined. In some embodiments, the BP expenditure matrix method 600 or a similar method may be used to determine the BP expenditure matrix. In other embodiments, the BP expenditure matrix may be received from another component or module. After determining the BP expenditure matrix, the BP network flow method 700A may proceed to block 720.

[0125] At block 720, a target utilization rate may be determined for each agent, and an expected rate may be determined for each contact type. In other embodiments, the expenditure matrix determined at block 710 may incorporate or otherwise include target utilization rates and / or expected rates, such as the expenditure matrix output by the BP expenditure matrix method 600, or the BP skill-based expenditure matrix 400A ( Figure 4A After determining the target utilization and expected rate, the BP network flow method 700A may proceed to block 730, if necessary.

[0126] At block 730, the agent supply and contact type requirements may be determined. Figure 4D and 4E As described in detail, each agent may provide an "supply" equal to the expected availability or target utilization of each agent (e.g., in an environment with three agents, 1 / 3 of the total supply of 1 or 100%). Additionally, for a total demand of 1 or 100%, each contact type / skill may require an amount of agent supply equal to the expected frequency or target utilization of each contact type / skill. The total supply and demand may be normalized or otherwise configured to be equal to each other, and the capacity or bandwidth along each edge may be considered to be infinite or otherwise unrestricted. In other embodiments, there may be a supply / demand imbalance, or there may be quotas or otherwise restricted capacity set on some or all edges.

[0127] In some embodiments, the supply and demand may be scaled by some factor, such as 1000, 3000, etc. In doing so, the supply of each of the three agents may be shown as 1000 instead of 1 / 3, and the total supply may be shown as 3000. Similarly, the relative demand of each contact type / skill set may be scaled. In some embodiments, no scaling occurs. After determining the agent supply and contact type demand, the BP network flow method 700A may proceed to block 740.

[0128] At block 740, a preferred contact-agent pairing may be determined. Figure 4F and 4G As described in detail, one or more solutions for BP network flow can be determined. In some embodiments, a "maximum flow" or "max flow" algorithm or other linear programming algorithm can be applied to the BP network flow to determine one or more solutions for optimizing the "flow" or "distribution" of supply (source) to meet demand (receiver). In some embodiments, a "max cost" algorithm can be applied to select the best maximum flow solution.

[0129] In some contact center environments, such as L2 (contact surplus) environments, the network flow may be reversed so that contacts waiting in a queue are the source of the offer, and possible agents that may become available are the recipients of the demand.

[0130] The BP network flow may include an optimal flow solution determined by a BP module or similar component. According to the solution, where there may be several choices, or a random choice, some (feasible) edges may have an optimal flow of 0, indicating that such a feasible pairing is not a preferred pairing. In some embodiments, if a pairing is determined to be not a preferred pairing, the BP network flow may remove the edge representing the feasible pairing.

[0131] Other edges may have non-zero optimal flows, indicating that at least some of the time, this feasible pairing is preferred. As described in detail above, this optimal flow solution describes the relative proportion of contact-agent interactions (or the relative likelihood of selecting a particular contact-agent interaction) that will achieve the target utilization of agents and contacts while also maximizing the expected overall performance of the contact center system based on the expenditure per pair of agent and contact type / skill set.

[0132] For some solutions of certain BP network flows, a single contact type / skill may have multiple edges flowing into it from multiple agents. In these environments, the contact type / skill may have multiple preferred pairings. Given a selection among multiple agents, the BP network flow indicates the relative proportion or weighting that may be selected for one of several agents each time a contact of that contact type / skill arrives at the contact center. After determining the preferred contact-agent pairing, the BP network flow method 700A may proceed to block 750.

[0133] At block 750, a computer processor generated model based on the preferred contact-agent pairings may be output. For example, a computer processor embedded within a contact center system or a component therein (e.g., a BP module) or communicatively coupled to a contact center system or a component therein may output a preferred pairing model to be received by the computer processor or another component of the contact center system. In some embodiments, the preferred pairing model may be recorded, printed, displayed, transmitted, or otherwise stored for other components of the contact center system or a human administrator. After outputting the preferred pairing model, the BP network flow method 700A may end.

[0134] Figure 7B FIG. 7 is a flow chart of a BP network flow method 700B according to an embodiment of the present disclosure. The BP network flow 700B is similar to the above reference Fig. 7A The BP network flow 700A described above. The BP network flow method 700B can start at box 710. At box 710, a BP expenditure matrix can be determined. After the BP expenditure matrix is ​​determined, the BP expenditure matrix method 700B can proceed to box 720. At box 720, a target utilization rate can be determined for each agent, and an expected rate can be determined for each contact type. After determining the target utilization rate and the expected rate, if necessary, the BP network flow method 700B can proceed to box 730. At box 730, the agent supply and contact type demand can be determined. After determining the agent supply and contact type demand, the BP network flow method 700B can proceed to box 735.

[0135] At block 735, the agent supply and / or contact demand may be adjusted to balance agent utilization, or to improve the agent utilization balance. Fig. 5F and 5GAs described in detail, agents that share the same skill set can be "collapsed" into a single network node (or "supernode"). Similarly, contact types that share the same skill set can be collapsed into a single network node. In addition, edges are collapsed according to their corresponding supernodes. At this point, in some embodiments, a quadratic programming algorithm or similar technique can be applied to the collapsed network to adjust the relative supply of agents and / or the relative demand for contacts. After adjusting the agent supply and / or contact demand to balance agent utilization, the BP network flow method 700B can proceed to block 740.

[0136] At block 740, a preferred contact-agent pairing may be determined. After determining the preferred contact-agent pairing, the BP network flow method 700A may proceed to block 750. At block 750, a computer processor generated model based on the preferred contact-agent pairing may be output. After outputting the preferred pairing model, the BP network flow method 700B may end.

[0137] Figure 8 A flow chart of a BP network flow method 800 according to an embodiment of the present disclosure is shown. The BP network flow method 800 may begin at block 810 .

[0138] In block 810, available agents may be determined. In a real-world queue for a contact center system, there may be tens, hundreds, or thousands of agents or more employed. At any given time, a portion of these employed agents may be logged into the system or otherwise actively working a shift. Also at any given time, a small portion of the logged-in agents may be participating in a contact interaction (e.g., a call center call), recording the results of a recent contact interaction, resting, or otherwise unavailable for incoming contact. The remainder of the logged-in agents may be idle or otherwise available to be assigned. After determining the set of available agents, the BP network flow method 800 may proceed to block 820.

[0139] At block 820, a BP model for a preferred contact-agent pairing may be determined. In some embodiments, the BP network flow method 700A ( Fig. 7A ) or 700B( Figure 7B ) or similar methods to determine the preferred pairing model. In other embodiments, the preferred pairing model can be received from another component or module.

[0140] In some embodiments, the preferred pairing model may include all agents employed for the contact center queue. In other embodiments, the preferred pairing model may include only those agents logged into the queue at a given time. In other embodiments, the preferred pairing model may include only those agents determined to be available at block 810. For example, referring to Figure 4BIf agent 403 is not available, some embodiments may use a different preferred pairing model that ignores the node for agent 403 and includes only nodes for agent 401 and agent 402 that are available. In other embodiments, the preferred pairing model may include a node for agent 403 but may be modified to avoid creating a non-zero probability of assigning a contact to agent 403. For example, the capacity of a flow from agent 403 to each compatible contact type may be set to zero.

[0141] The preferred pairing model may be pre-computed (e.g., retrieved from a cache or other memory) or calculated in real time or near real time as agents become available or unavailable, and / or as various types of contacts with various skill requirements arrive at the contact center. After determining the preferred pairing model, the BP network flow method 800 may proceed to block 830.

[0142] At block 830, available contacts may be determined. For example, in an L1 environment, multiple agents may be available and waiting to be assigned to contacts, and the contact queue may be empty. When a contact arrives at the contact center, the contact may be assigned to one of the available agents without being kept waiting. In some embodiments, the preferred pairing model determined at block 820 may be determined for the first time, or updated after available contacts are determined at block 830. For example, referring to Figures 4A-4G , seats 401-403 may be three of the dozens or more available at a given moment. At this point, a BP skill-based expenditure matrix 400A ( Figure 4A ), and the BP network flow 400G can be determined for three immediately available agents based on the expenditure matrix 400A of the BP skills ( Figure 4G ). Therefore, for those three available seats, the preferred pairing model can be determined at that time.

[0143] In some embodiments, the preferred pairing model may take into account some or all expected contact type / skill combinations, as in, for example, BP network flow 400G, even if the particular contact to be paired is already known to the BP network flow method 800 because the contact has been determined at block 830. After the available contacts have been determined at block 830 (and, in some embodiments, the preferred pairing model has been generated or updated), the BP network flow method 800 may proceed to block 840.

[0144] At block 840, at least one preferred contact-agent pairing between available agents and available contacts may be determined. Figure 4G), if the available contact is of contact type 411 and requires skills 421 or 422, the preferred pairing is agent 402. Similarly, if the available contact is of contact type 412 and requires skills 421 or 422, the preferred pairing would be agent 401 (best flow 400) or agent 402 (best flow 50). After determining at least one preferred contact-agent pairing, the BP network flow method 800 may proceed to block 850.

[0145] At block 850, one of the at least one preferred contact-agent pairings may be selected. In some embodiments, the selection may be random, such as by using a pseudo-random number generator. The likelihood (or probability) of selecting a given one of the at least one preferred contact-agent pairings may be based on the statistical likelihood described by the BP model. For example, as in BP network flow 400G ( Figure 4G ), if the available contact is of contact type 412 and requires skills 421 or 422, the probability of selecting agent 401 is 400 / 450≈89%, and the probability of selecting agent 402 is 50 / 450≈11%.

[0146] If there is only one preferred contact-agent pairing, then in some embodiments, random selection may not be required because the selection may be trivial. For example, as in BP network flow 400G ( Figure 4G ), if the available contact is contact type 411 and requires skills 421 or 422, the preferred pairing is always agent 402, and the probability of selecting agent 402 is 450 / 450=100%. After selecting one of the at least one preferred contact-agent pairings, the BP network flow method 800 can proceed to box 860.

[0147] At block 860, the selected pairing may be output for connection in the contact center system. For example, a computer processor such as a BP module embedded within or communicatively coupled to a contact center system or a component thereof may output a preferred pairing selection (or a recommended pairing or pairing instruction) to be received by the computer processor or another component of the contact center system. In some embodiments, the preferred pairing selection may be recorded, printed, displayed, transmitted, or otherwise stored for other components of the contact center system or a human administrator. The receiving component may use the preferred pairing selection to connect the selected agent to the contact that requested or otherwise determined the pairing. After outputting the preferred pairing instruction, the BP network flow method 800 may end.

[0148] Fig. 9A flow chart of a BP network flow method 900 according to an embodiment of the present disclosure is shown. In some embodiments, the BP network flow method 900 is similar to the BP network flow method 800. While the BP network flow method 800 shows an L1 environment (surplus seats), the BP network flow method 900 shows an L2 environment (contacts in queues). The BP network flow method 900 can start at block 910.

[0149] In box 910, available contacts can be determined. In the real-world queue of a contact center system, there may be dozens, hundreds, etc. of agents employed. In an L2 environment, all logged-in agents are involved in contact interactions or are otherwise unavailable. When a contact arrives at a contact center, the contact may be required to wait in a reserved queue. At a given time, there may be dozens or more contacts that remain waiting. In some embodiments, the queue may be sorted in order of arrival time, with the longest waiting contact at the head of the queue. In other embodiments, the queue may be sorted at least in part based on the priority rating or status of each contact. For example, a contact designated as "high priority" may be located at or near the head of the queue, ahead of other "normal priority" contacts that have been waiting longer. After determining the set of available contacts waiting in the queue, the BP network flow method 900 may proceed to box 920.

[0150] At block 920, a BP model for a preferred contact-agent pairing may be determined. In some embodiments, a BP network flow method similar to 700A ( Fig. 7A ) or 700B( Figure 7B ) method to determine the preferred pairing model, just wait for the contact to provide the supply source, and become available to provide the recipient of the demand. In other embodiments, the preferred pairing model can be received from another component or module.

[0151] In some embodiments, the preferred pairing model may include all contact types expected to arrive in the contact center queue. In other embodiments, the preferred pairing model may include only those contact type / skill combinations that are present and waiting in the queue when the model is requested. For example, consider a contact center system that expects three types of contacts, X, Y, and Z, but only contacts of types X and Y are currently waiting in the queue. Some embodiments may use a different preferred pairing model that omits nodes for contact type Z and includes nodes only for waiting contacts of types X and Y. In other embodiments, the preferred pairing model may include a node for contact type Z, but the model is modified to avoid generating a non-zero probability of assigning an agent to a contact of contact type Z. For example, the capacity of the flow from contact type Z to each compatible agent may be set to zero.

[0152] The preferred pairing model may be pre-computed (e.g., retrieved from a cache or other memory) or calculated in real time or near real time as agents become available or unavailable, and / or as various types of contacts with various skill requirements arrive at the contact center. After determining the preferred pairing model, the BP network flow method 900 may proceed to block 930.

[0153] At box 930, available seats can be determined. For example, in an L2 environment, multiple contacts are waiting and available for assignment to seats, and all seats are occupied. When an agent becomes available, the agent can be assigned to one of the waiting contacts without remaining idle. In some embodiments, the preferred pairing model determined at box 920 can be determined for the first time, or updated after the available seats are determined at box 830. For example, there may be three contacts waiting in a queue, each with different skills and types. A BP skills-based expenditure matrix can be determined for the three immediate waiting contacts, and a BP network flow can be determined for the three immediate waiting contacts based on the BP skills expenditure matrix. Therefore, for those three waiting contacts, a preferred pairing model can be determined at that time.

[0154] In some embodiments, the preferred pairing model may consider some or all potentially available agents, even if the particular agent to be paired is already known to the BP network flow method 900 because the agent has been determined at block 930. After the available agents have been determined at block 930 (and, in some embodiments, the preferred pairing model has been generated or updated), the BP network flow method 900 may proceed to block 940.

[0155] At block 940 , at least one preferred contact-agent pairing among the available agents and the available contacts may be determined. After at least one preferred contact-agent pairing is determined, the BP network flow method 900 may proceed to block 950 .

[0156] At block 950, one of the at least one preferred contact-agent pairings may be selected. In some embodiments, the selection may be random, such as by using a pseudo-random number generator. The likelihood (or probability) of selecting a given one of the at least one preferred contact-agent pairings may be based on the statistical likelihood described by the BP model. If there is only one preferred contact-agent pairing, random selection may not be required in some embodiments because the selection may be trivial. After selecting one of the at least one preferred contact-agent pairings, the BP network flow method 900 may proceed to block 960.

[0157] At block 960, the selected pairing may be output for connection in the contact center system. For example, a computer processor such as a BP module embedded within or communicatively coupled to a contact center system or a component thereof may output a preferred pairing selection (or a recommended pairing or pairing instruction) to be received by the computer processor or another component of the contact center system. In some embodiments, the preferred pairing selection may be recorded, printed, displayed, transmitted, or otherwise stored for other components of the contact center system or a human administrator. The receiving component may use the preferred pairing selection to connect the selected agent to the contact for which the pairing was requested or otherwise determined. After outputting the preferred pairing instruction, the BP network flow method 900 may end.

[0158] In some embodiments, the BP expenditure matrix and network flow model can be used in an L3 environment (i.e., multiple agents are available and multiple contacts are waiting in a queue). In some embodiments, the network flow model can be used to batch match multiple contact-agent pairs simultaneously. BP pairing in an L3 environment is described in detail in, for example, U.S. Patent Application No. 15 / 395,469, the entire contents of which are incorporated herein by reference. In other embodiments, the BP network flow model can be used when the contact center system operates in an L1 and / or L2 environment, and an alternative BP pairing strategy can be used when the contact center system operates in an L3 (or L0) environment.

[0159] In the above example, the BP network flow model targets balanced agent utilization (or as close to balanced as possible for a particular contact center environment). In other embodiments, skewed or otherwise unbalanced agent utilization may be targeted (e.g., the "Kappa" technique), and / or skewed or otherwise unbalanced contact utilization may be targeted (e.g., the "Rho" technique). Examples of these techniques, including the Kappa and Rho techniques, are described in detail in, for example, the aforementioned U.S. Patent Applications 14 / 956,086 and 14 / 956,074, the entire contents of which are incorporated herein by reference.

[0160] In some embodiments, such as those in which a BP module (e.g., BP module 140) is fully embedded or otherwise integrated within a contact center switch (e.g., center switch 110, contact center switch 120A, etc.), the switch can perform the BP technique without separate pairing requests and responses between the switch and the BP module. For example, when a need arises, the switch can determine its own cost function or functions to apply to each possible pairing, and the switch can automatically minimize (or, in some configurations, maximize) the cost function accordingly. The switch can reduce or eliminate the need for skill queues or other hierarchical settings for agents or contacts; instead, the switch can operate across a collection of agents in one or more virtual agent groups or a larger pool of agents within the contact center system. Some or all aspects of the BP pairing method can be implemented by the switch as needed, including data collection, data analysis, model generation, network flow optimization, etc.

[0161] In some embodiments, such as those that optimize virtual agent groups, the model of agent nodes in the network flow can represent a set of agents with one or more agent skill / type combinations for agents found anywhere within the contact center system, regardless of whether the contact center system assigns the agents to one or more skill queues. Figure 4B-4G The nodes for agents 401, 402, and 403 in FIG. 4 may represent virtual agent groups rather than individual agents, and contacts assigned to the virtual agent groups may subsequently be assigned to individual agents within the virtual agent groups (e.g., randomly, round-robin, model-based behavioral pairing, etc.). In these embodiments, before contacts are filtered or otherwise assigned to individual skill queues and / or agent groups (e.g., Figure 1 The BP may be applied to a contact center system (eg, a contact center switch 120A or a contact center switch 120B). Figure 1 A higher level connection within the central switch 101).

[0162] Applying BP earlier in the process can be advantageous because it avoids scripts and other prescriptive techniques that traditional central switches use to decide which queue / switch / VDN a contact should be assigned to. These scripts and other prescriptive techniques can be inefficient and suboptimal in terms of optimizing overall contact center performance and achieving desired target agent utilization (e.g., balanced agent utilization, minimal agent utilization imbalance, specified amount of agent utilization skew).

[0163] At this point, it should be noted that the behavior pairing in the contact center system according to the present disclosure as described above may involve processing input data and generating 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 used in a behavior pairing module or similar or related circuits to implement the functions associated with behavior pairing in the contact center system according to the present disclosure as described above. Alternatively, one or more processors operating on instructions can implement the functions associated with behavior pairing in the contact center system according to the present disclosure as described above. If this is the case, it is also within the scope of the present disclosure that these instructions can be stored on one or more non-transitory processor-readable storage media (e.g., disks or other storage media), or transmitted to one or more processors via one or more signals embedded in one or more carriers.

[0164] The present disclosure is not limited to the scope of the specific embodiments described herein. In fact, in addition to those described herein, other various embodiments of the present disclosure and improvements thereof will be apparent to those of ordinary skill in the art from the above description and the accompanying drawings. Therefore, these other embodiments and improvements are intended to fall within the scope of the present disclosure. In addition, although the present disclosure is described herein in the context of at least one specific embodiment in at least one specific environment for at least one specific purpose, it will be appreciated by those of ordinary skill in the art that its usefulness is not limited thereto, and for various purposes, the present disclosure can be advantageously implemented in a variety of environments. Therefore, the following claims should be interpreted in view of the full scope and spirit of the present disclosure as described herein.

Claims

1. A method for pairing model evaluation in a contact center system, comprising: determining, by at least one computer processor communicatively coupled to the contact center system and configured to perform pairing operations in the contact center system, a plurality of agents and a plurality of contact types; analyzing, by the at least one computer processor, historical contact-agent outcome data to determine outcome estimates for a plurality of feasible combinations of agents and contact types from the plurality of agents and contact types; determining, by the at least one computer processor, a pairing model based on the outcome estimates of the plurality of possible combinations; determining, by the at least one computer processor, an expected performance of the contact center system using the pairing model; outputting, by the at least one computer processor, the expected performance of the contact center system using the pairing model; as well as In a switching module of the contact center system, a communication channel between a contact and an agent is established based at least in part on the pairing model.

2. The method according to claim 1, wherein: The pairing model may be represented as a spending matrix.

3. The method according to claim 1, further comprising: An expected frequency for each contact type in the plurality of contact types is determined, by the at least one computer processor, wherein the pairing model is further based on the expected frequency for each contact type.

4. The method according to claim 1, further comprising: A target utilization rate for each of the plurality of agents is determined, by the at least one computer processor, wherein the pairing model is further based on the target utilization rate for each agent.

5. The method according to claim 4, wherein: The pairing model targets balanced agent utilization.

6. The method according to claim 1, wherein: Each feasible combination of the plurality of feasible combinations includes an agent having at least a first skill and a contact type having at least a first requirement corresponding to the first skill.

7. The method according to claim 1, further comprising: A probabilistic network flow model is determined, by the at least one computer processor, based on the pairing model.

8. The method according to claim 7, wherein: The probabilistic network flow model includes: supply nodes, wherein each supply node is an agent of the plurality of agents or a group of agents including a subset of agents of the plurality of agents; Requirement nodes, wherein each requirement node is a contact type from the plurality of contact types or a group of contact types that includes a subset of the contact types from the plurality of contact types.

9. The method according to claim 8, wherein: The probabilistic network flow model includes: An offer value is associated with each of the offer nodes, the offer value corresponding to a target utilization for at least one agent of the plurality of agents.

10. The method according to claim 8, wherein: The probabilistic network flow model includes: A demand value is associated with each of the demand nodes, the demand value corresponding to an expected frequency of at least one contact type of the plurality of contact types.

11. The method according to claim 7, wherein: The probabilistic network flow model includes: A plurality of edges between the supply node and the requirement node, wherein each edge of the plurality of edges indicates one of each feasible combination of the plurality of feasible combinations.

12. The method according to claim 7, wherein: The probabilistic network flow model includes: A plurality of edge weights, wherein each edge weight of the plurality of edge weights indicates an expected value of a corresponding feasible combination of agents and contact types of the plurality of feasible combinations.

13. The method according to claim 1, wherein: The pairing model includes: The BP strategy uses the ranking of agents and contact types in combination with the diagonal strategy to determine the preferred pairings.

14. A system for pairing model evaluation in a contact center system, comprising: at least one computer processor communicatively coupled to the contact center system and configured to perform pairing operations in the contact center system, wherein the at least one computer processor is further configured to: Identify multiple agents and multiple contact types; analyzing the historical contact-agent outcome data to determine outcome estimates for a plurality of feasible combinations of agents and contact types from the plurality of agents and contact types; Determining a pairing model based on the result estimates of the plurality of feasible combinations; using the pairing model, determining an expected performance of the contact center system; Using the pairing model, outputting the expected performance of the contact center system; and In a switching module of the contact center system, a communication channel between a contact and an agent is established based at least in part on the pairing model.

15. The system of claim 14, wherein: The pairing model may be represented as a spending matrix.

16. The system of claim 14, wherein: The at least one computer processor is further configured to: An expected frequency for each contact type in the plurality of contact types is determined, wherein the pairing model is further based on the expected frequency for each contact type.

17. The system of claim 14, wherein: The at least one computer processor is further configured to: A target utilization rate for each of the plurality of agents is determined, wherein the pairing model is further based on the target utilization rate for each agent.

18. The system of claim 17, wherein: The pairing model targets balanced agent utilization.

19. The system of claim 14, wherein: Each feasible combination of the plurality of feasible combinations includes an agent having at least a first skill and a contact type having at least a first requirement corresponding to the first skill.

20. The system of claim 14, wherein: The at least one computer processor is further configured to: Based on the pairing model, a probabilistic network flow model is determined.

21. The system of claim 20, wherein: The probabilistic network flow model includes: supply nodes, wherein each supply node is an agent of the plurality of agents or a group of agents including a subset of agents of the plurality of agents; Requirement nodes, wherein each requirement node is a contact type from the plurality of contact types or a group of contact types that includes a subset of the contact types from the plurality of contact types.

22. The system of claim 21, wherein: The probabilistic network flow model includes: An offer value is associated with each of the offer nodes, the offer value corresponding to a target utilization for at least one agent of the plurality of agents.

23. The system of claim 21, wherein: The probabilistic network flow model includes: A demand value is associated with each of the demand nodes, the demand value corresponding to an expected frequency of at least one contact type of the plurality of contact types.

24. The system of claim 20, wherein: The probabilistic network flow model includes: A plurality of edges between the supply node and the requirement node, wherein each edge of the plurality of edges indicates one of each feasible combination of the plurality of feasible combinations.

25. The system of claim 20, wherein: The probabilistic network flow model includes: A plurality of edge weights, wherein each edge weight of the plurality of edge weights indicates an expected value of a corresponding feasible combination of agents and contact types of the plurality of feasible combinations.

26. The system of claim 14, wherein: The pairing model includes: The BP strategy uses the ranking of agents and contact types in combination with the diagonal strategy to determine the preferred pairings.

27. An article of manufacture for pairing model evaluation in a contact center system, comprising: at least one non-transitory processor-readable medium; as well as 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 communicatively coupled to the contact center system and configured to perform pairing operations in the contact center system, and thereby cause the at least one computer processor to operate to: Identify multiple agents and multiple contact types; analyzing the historical contact-agent outcome data to determine outcome estimates for a plurality of feasible combinations of agents and contact types from the plurality of agents and contact types; Determining a pairing model based on the result estimates of the plurality of feasible combinations; using the pairing model, determining an expected performance of the contact center system; Using the pairing model, outputting the expected performance of the contact center system; and In a switching module of the contact center system, a communication channel between a contact and an agent is established based at least in part on the pairing model.

28. The article of claim 27, wherein: The pairing model includes: The BP strategy uses the ranking of agents and contact types in combination with the diagonal strategy to determine the preferred pairings.

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