Techniques for determining behavioral pairing in task distribution system

Abstract

The invention relates to techniques for determining behavioral pairing in a task distribution system. In one particular embodiment, the technique may be implemented as a method for determining behavioral pairing in a task distribution system, comprising: determining, by at least one computer processor communicatively coupled with and configured to operate in the task distribution system, behavioral pairing in the task distribution system; determining a plurality of possible task-agent pairs among the at least one task waiting for allocation and the at least one agent available for allocation; and selecting, by the at least one computer processor, a first task-agent pair of the plurality of possible task-agent pairs for allocation in the task allocation system based at least in part on a first set of offers to be proposed by the agent or a first compensation to be received by the agent.

Description

[0001] This application is a divisional application of a patent application filed on December 25, 2020, with an application date of September 15, 2020, application number 202080003629.5 (PCT / US2020 / 050850), and invention name “Technology for determining behavior pairing in a task assignment system”.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This international patent application claims priority to U.S. patent application No. 16 / 576,434, filed on September 19, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0004] The present disclosure relates generally to behavior pairing, and more particularly to techniques for deciding behavior pairing in a task allocation system. Background Art

[0005] A typical task allocation system algorithmically allocates tasks that arrive at a task allocation center to agents that are available to handle those tasks. Sometimes, a task allocation center may be in an "L1 state" and have agents available and waiting to be assigned to tasks. At other times, a task allocation center may be in an "L2 state" and have tasks waiting in one or more queues for agents to become available for assignment.

[0006] In some typical task distribution centers, tasks are assigned to agents in an order based on arrival time, and agents receive tasks in an order based on the time when these agents become available. This strategy may be referred to as a "first in, first out," "FIFO," or "round-robin" strategy. For example, in an L2 environment, when an agent becomes available, the task at the head of the queue will be selected to be assigned to the agent.

[0007] In other typical task distribution centers, a performance-based routing (PBR) strategy for prioritizing task distribution to higher performing agents may be implemented. Under PBR, for example, the highest performing agent among the available agents receives the next available task. Other PBR and PBR-like strategies may use specific information about the agents for distribution.

[0008] The “behavioral pairing” or “BP” strategy for assigning tasks to agents improves upon traditional assignment methods. The goal of BP is to balance agent utilization while improving overall task assignment center performance that is likely to exceed what FIFO or PBR methods can achieve in practice.

[0009] In some task allocation systems, it may be advantageous to consider suboptimal actions for tasks assigned to a particular agent. Therefore, it can be appreciated that a decision-making BP strategy may be needed that considers suboptimal actions for task-agent pairings to optimize the overall performance of the task allocation system. Summary of the invention

[0010] A technique for determining a behavior pairing in a task allocation system is disclosed. In a specific embodiment, the technique can be implemented as a method for determining a behavior pairing in a task allocation system, comprising: determining, by at least one computer processor that is communicatively coupled to the task allocation system and configured to operate in the task allocation system, a plurality of possible task-agent pairings among at least one task waiting to be assigned and at least one agent available for assignment; and selecting, by at least one computer processor, a first task-agent pairing from among the plurality of possible task-agent pairings for assignment in the task allocation system based at least in part on a first quote set to be provided by the agent or a first compensation to be received by the agent.

[0011] According to other aspects of this particular embodiment, the task assignment system may be a contact center system.

[0012] According to other aspects of this particular embodiment, selecting the first task-agent pairing may be based at least in part on both the first set of bids and the first compensation.

[0013] According to other aspects of this particular embodiment, the method may further include: selecting, by at least one computer processor, a first set of bids from a plurality of potential sets of bids.

[0014] In accordance with other aspects of this particular embodiment, the method may further include selecting, by at least one computer processor, a first compensation from a plurality of potential compensations.

[0015] According to other aspects of this particular embodiment, selecting the first task-agent pairing may be based on at least one of a first ranking of the plurality of tasks and a second ranking of the plurality of agents, and wherein at least one of the first ranking and the second ranking is expressed as a percentile or a percentile range.

[0016] According to other aspects of this particular embodiment, the method may further include adjusting, by at least one computer processor, the first set of bids or the first offset to adjust the first ranking or the second ranking.

[0017] In another specific embodiment, the technology can be implemented as a system for determining behavior pairing in a task allocation system, the task allocation system comprising at least one computer processor, the at least one computer processor being communicatively coupled to the task allocation system and configured to operate in the task allocation system, wherein the at least one computer processor is further configured to perform the steps in the above method.

[0018] In another specific embodiment, the technology can be implemented as an article for determining behavior pairing in a task allocation system, which task allocation system includes a non-transitory processor-readable medium and instructions stored on the medium; wherein the instructions are configured to be readable from the medium by at least one computer processor, and the at least one computer processor is communicatively coupled to the task allocation system and is configured to operate in the task allocation system, so that the at least one computer processor operates to perform the steps in the above method.

[0019] The present invention will now be described in more detail with reference to specific embodiments of the invention as shown in the accompanying drawings. Although the present disclosure is described below with reference to specific embodiments, it should be understood that the present disclosure is not limited thereto. Those of ordinary skill in the art who have access to the teachings of this article will recognize additional embodiments, modifications and embodiments within the scope of the present disclosure described herein, as well as other fields of use, and with respect to which the present disclosure may have significant utility. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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.

[0021] Figure 1 A block diagram of a task allocation center according to an embodiment of the present disclosure is shown.

[0022] Figure 2 A block diagram of a task allocation system according to an embodiment of the present disclosure is shown.

[0023] Figure 3 A flowchart of a task allocation method according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0024] A typical task allocation system algorithmically allocates tasks that arrive at a task allocation center to agents that are available to handle those tasks. Sometimes, a task allocation system may be in an "L1 state" and have agents available and waiting to be assigned to tasks. At other times, a task allocation center may be in an "L2 state" and have tasks waiting in one or more queues for agents to become available for assignment. At other times, a task allocation system may be in an "L3" state and have multiple available agents and multiple tasks waiting to be assigned. An example of a task allocation system is a contact center system that receives contacts (e.g., phone calls, Internet chat sessions, emails, etc.) to be assigned to agents.

[0025] In some traditional task distribution centers, tasks (e.g., callers) are assigned to agents in an order based on arrival time, and agents receive tasks in an order based on the time when these agents become available. This strategy may be referred to as a "first in, first out," "FIFO," or "round robin" strategy. For example, in an L2 environment, when an agent becomes available, a task at the head of the queue is selected to be assigned to the agent. In other traditional task distribution centers, a performance-based routing (PBR) strategy for prioritizing task assignments to higher-performing agents may be implemented. Under PBR, for example, the highest-performing agent among the available agents receives the next available task.

[0026] The present disclosure relates to optimized strategies, such as "behavior pairing" or "BP" strategies, for assigning tasks to agents, which improve upon traditional assignment methods. The goal of BP is to balance agent utilization while improving overall task assignment center performance that is likely to exceed what FIFO or PBR methods can achieve in practice. This is a remarkable achievement because BP acts on the same tasks and the same agents as FIFO or PBR methods, approximately balancing the utilization of agents as provided by FIFO, while improving overall task assignment center performance that exceeds what FIFO or PBR can provide in practice. BP improves performance by assigning agents and task pairs in a way that takes into account potential subsequent assignments of agent and task pairs, such that when the benefits of all assignments are aggregated, they are likely to exceed the benefits of FIFO and PBR strategies.

[0027] Various BP strategies may be used, such as a diagonal model BP strategy or a network flow (or "off-diagonal") BP strategy. These task allocation strategies and others are described in detail in the contact center context, for example, in U.S. Patent Nos. 9,300,802, 9,781,269, 9,787,841, and 9,930,180, all of which are incorporated herein by reference. The BP strategy may be applied to an L1 environment (agent remaining, one task; selecting among multiple available / idle agents), an L2 environment (task remaining, one available / idle agent; selecting among multiple tasks in a queue), and an L3 environment (multiple agents and multiple tasks; selecting among paired arrangements).

[0028] The various BP strategies discussed above can be viewed as two-dimensional (2-D), where one dimension is related to agents and the second dimension is related to tasks (e.g., callers), and the various BP strategies take into account information about agents and tasks to pair them. As described in detail below, embodiments of the present disclosure are directed to deciding on a BP strategy that takes into account higher dimensional assignments. For a three-dimensional (3-D) example, a BP strategy may assign an agent to a task and a set of quotes that the agent can make or a set of actions that the agent can take during task assignment. For another 3-D example, a BP strategy may assign an agent to both a task and a (monetary or non-monetary) reward to be given to the agent for a given task assignment. For a four-dimensional (4-D) example, a BP strategy may assign an agent to a task, a set of quotes, and a reward.

[0029] These decision-making BP strategies may also consider historical outcome data, such as agent-task-quote, agent-task-reward, or agent-task-quote-reward pairings, to build BP models and apply BP strategies to “paired” tasks and agents and specific quote sets and / or agent compensation (throughout the specification, the noun and verb “pairing” and other forms such as “behavior pairing” may be used to describe triplets and higher-dimensional groupings).

[0030] Figure 1A block diagram of a task distribution center 100 according to an embodiment of the present disclosure is shown. The specification of this article describes a network element, computer and / or component of a system and method for pairing strategies in a task distribution system 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 non-transient 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 the 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, a module 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.

[0031] like Figure 1 As shown, the task distribution center 100 may include a central switch 210. The central switch 110 may receive incoming tasks (e.g., telephone calls, Internet chat sessions, emails, etc.) or support outbound connections to contacts via a dialer, telecommunications network, or other module (not shown). The central switch 110 may include routing hardware and software to help route tasks between one or more sub-centers or to route tasks to one or more private branch exchanges ("PBX") or automatic call distribution (ACD) routing components or other queuing or switching components within the task distribution center 100. If there is only one sub-center, or if there is only one PBX or ACD routing component in the task distribution center 100, the central switch 110 may not be required.

[0032] If more than one sub-center is part of the task distribution center 100, each sub-center may include at least one switch (e.g., switches 120A and 120B). Switches 120A and 120B may be communicatively coupled to the central switch 110. Each switch in each sub-center may be communicatively coupled to a plurality of agents (or "agent pools"). Each 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 task, or a logged in agent may be unavailable for a variety of reasons (such as being connected to another contact, performing certain post-call functions such as logging information about the call, or taking a break). In Figure 1In the example of FIG. 1 , the central switch 110 routes tasks to one of the two sub-centers via switches 120A and 120B, respectively. FIG. 1 shows that each of switches 120A and 120B has two agents, respectively. Agents 130A and 130B can log in to switch 120A, while agents 130C and 130D can log in to switch 120B.

[0033] The task distribution center 100 may also be communicatively coupled to integration services from, for example, third-party vendors. Figure 1 In an example, the behavior pairing module 140 can be communicatively coupled to one or more switches in the switch system of the task distribution center 100, such as the central switch 110, the switch 120A, and the switch 120B. In some embodiments, the switches of the task distribution center 100 can be communicatively coupled to multiple behavior pairing modules. In some embodiments, the behavior pairing module 140 can be embedded in a component of the task distribution center 100 (e.g., embedded in a switch or otherwise integrated with a switch).

[0034] The behavior pairing module 140 can receive information about agents (e.g., agents 130A and 130B) logged into the switch (e.g., switch 120A) and information about inbound tasks via another switch (e.g., central switch 110), or in some embodiments, receive information from a network (e.g., the Internet or a telecommunications network) (not shown). The behavior pairing module 140 can process the information to determine which agents should be paired (e.g., matched, assigned, distributed, routed) with which tasks and other dimensions (e.g., quotes, actions, channels, non-monetary rewards, monetary rewards or compensation, etc.).

[0035] For example, in the L1 state, multiple agents are available and waiting for connection with contacts, and tasks arrive at the task distribution center 100 via the network or central switch 110. As described above, without the behavior matching module 140, the switch will usually automatically distribute new tasks to: under the FIFO strategy, an available agent who has waited the longest time; or under the PBR strategy, an available agent determined to be the highest performing agent. Under the behavior matching module 140, contacts and agents can be assigned scores (e.g., percentiles or percentile ranges / bandwidths) based on a matching model or other artificial intelligence data model so that tasks can be matched, paired, or otherwise connected to preferred agents. In the following, the high-dimensional analysis of BP decisions will be described in more detail.

[0036] In the L2 state, multiple tasks are available and waiting to be connected to an agent, and an agent becomes available. These tasks can be queued in a switch such as a PBX or ACD device. In the absence of a behavioral pairing module 140, when the agent selection is unavailable, such as in a FIFO strategy or a PBR strategy, the switch will typically connect the newly available agent to the task that has been waiting the longest in the queue. In some task allocation centers, as described above, priority queues can also be merged. When there is a behavioral pairing module 140 in this L2 case, as in the above-mentioned L1 state, percentiles (or percentile ranges / bandwidths, etc.) can be assigned to tasks and agents based on, for example, a model such as an artificial intelligence model, so that the agent that becomes available can be matched, paired, or otherwise connected to the preferred task. In the following, the high-dimensional analysis of BP decisions will be explained in more detail.

[0037] Figure 2 A block diagram of a task allocation system 200 according to an embodiment of the present disclosure is shown. The task allocation system 200 can be included in a task allocation center (e.g., the task allocation center 100), or incorporated into a component or module (e.g., the behavior pairing module 140) of the task allocation center to help allocate agents between various tasks and other dimensions for grouping.

[0038] The task assignment system 200 may include a task assignment module 210 configured to pair (eg, match, assign) an incoming call task with an available agent. (In the following, the high-dimensional analysis of BP decision making will be described in more detail.) Figure 2 In the example of , m tasks 220A-220m are received in a given time period, and during the given time period, n seats 230A-230n are available. Each of the m tasks can be assigned to one of the n seats for service or other types of task processing. Figure 1 In the example of , m and n can be arbitrarily large finite integers greater than or equal to 1. In a real-world task distribution center (such as a contact center), there may be tens, hundreds, etc. of agents logged into the contact center to interact with contacts during a shift, and during a shift, the contact center may receive tens, hundreds, thousands of contacts (e.g., phone calls, Internet chat sessions, emails, etc.), etc.

[0039] In some embodiments, the task assignment strategy module 240 can be communicatively coupled to the task assignment system 200 and / or configured to operate in the task assignment system 200. The task assignment strategy module 240 can implement one or more task assignment strategies (or "pairing strategies") for assigning a single task to a single agent (e.g., pairing a contact with a contact center agent). The task assignment strategy module 240 can design and implement a variety of different task assignment strategies. In some embodiments, a FIFO strategy can be implemented, where, for example, the agent with the longest waiting time receives the next available task (in an L1 environment) or the task with the longest waiting time is assigned to the next available agent (in an L2 environment). In other embodiments, a PBR strategy for prioritizing higher performance agents for task assignment can be implemented. For example, under PBR, the highest performance agent among the available agents receives the next available task. In other embodiments, a BP strategy can be used to optimally assign tasks to agents using information about tasks or agents or both. Various BP strategies can be used, such as a diagonal model BP strategy or a network flow ("off-diagonal") BP strategy. See U.S. Patent Nos. 9,300,802, 9,781,269, 9,787,841, and 9,930,180.

[0040] In some embodiments, when a task is assigned to a specific agent, the task assignment strategy module 240 can implement a decision-making BP strategy that considers suboptimal actions for the task. For a task-agent pair, the decision-making BP strategy can also assign available actions or action sets to the agent to complete the task. In the context of a contact center system, an action or action set may include a quote or quote set that the agent can present to the customer. For example, in a contact center system, a decision-making BP strategy can pair a contact with an agent and a quote or quote set that the agent can make for the customer based on the expected results of the contact-agent interaction using the particular quote or quote set. By influencing the choices or options among the quotes available to the agent, the decision-making BP strategy goes beyond pairing the contact with the agent by optimizing the results of a single interaction between the agent and the contact.

[0041] For example, if agent 230A loves sports, agent 230A may be better at selling sports packages. Therefore, sports packages can be included in the quotation set of agent 230A for some or all contact types. On the other hand, agent 230B may like movies and may be better at selling premium movie packages; therefore, premium movie packages can be included in the quotation set of agent 230B for some or all contact types. In addition, based on artificial intelligence processing such as machine learning, the decision BP model can automatically divide customers according to various variables and data types. For example, the decision BP model can suggest providing a package that includes sports, suitable for a specific age range, income range or other demographic or psychological factors to a first type of customer ("customer type 1"). The decision BP model can suggest providing a premium movie package that can be suitable for different age ranges, income ranges or other demographic or psychological factors to a second type of customer ("customer type 2"). The decision BP strategy can preferably pair customer type 1 with agent 230A and a quotation set with a sports package, and pair customer type 2 with agent 230B and a quotation set with a premium movie package.

[0042] As with the previously disclosed BP strategies, the decision-making BP strategy optimizes the overall performance of the task allocation system, rather than optimizing each individual instant task-agent pairing. For example, in some embodiments, the decision-making BP system will not always offer sports to customer type 1, nor will it always give agent 230A the option of quoting a deal based on a sports package. This situation may occur when the marketing department of the company running the contact center system may have a limited budget, a limited number of deals (e.g., a limited number of discounted sports packages), other constraints on the frequency of certain quotations, restrictions on the total amount of discounts given in a given time period (e.g., for any discount or discount package), etc. Similarly, sports-based deals may sometimes be offered to customer type 2, while agent 230B may sometimes be given the option of quoting a deal based on a sports package.

[0043] To optimize the overall performance of the task assignment system, the decision BP strategy may take into account all types of customers waiting in the queue, the agents available for the customers, and any other dimensions used for pairing (such as the number and type of remaining quotes, agent compensation or other non-monetary incentives, suboptimal actions, etc.). In some embodiments, a probability distribution may be assigned based on the likelihood that an inbound task or customer type will accept a given quote level based on the agent being paired with the task or customer.

[0044] For example, for customer type 1, if the discount on the quote is 0%, the customer has a 0% chance of accepting the quote from the average agent, and specifically a 0% chance of accepting the quote from agent 230A and 230B. For a 20% discount quote, the customer may have a 30% chance of accepting the quote from the average agent, a 60% chance of accepting the quote from agent 230A, and a 25% chance of accepting the quote from agent 230B. In the case where the average agent, agent 230A, and agent 230B are all assigned to a queue and available, it is possible that agent 230A performs much better than the average agent or agent 230B.

[0045] In some embodiments, output volume measurements may be attached to each task before and after an interaction with an agent. For example, revenue figures may be attached to each caller before and after a call. A decision-making BP system may measure changes in revenue and the influence of a caller based on a quote or set of quotes provided by an agent. For example, customer type 1 is more likely to renew their existing plan regardless of the proposed discount or regardless of the capabilities of a single agent. Customer type 2 may be preferably assigned to a lower performing agent with a higher discount cap in the quote set. Conversely, if customer type 2 is paired with a higher performing agent or an agent authorized to offer a larger discount, she is more likely to upgrade her plan.

[0046] In some embodiments, the decision BP strategy can perform sequential pairing of one or more dimensions in any order. For example, the decision BP strategy can first pair agents with tasks, then pair quote sets with agent-task pairs, and then pair rewards with agent-task-quote set pairs, and so on.

[0047] In other embodiments, the decision BP strategy may perform "fully coupled" simultaneous multi-dimensional pairing. For example, the decision BP strategy may consider all dimensions at once to select the best 4D agent-task-offer-reward pair.

[0048] The same task may arrive at the task assignment system multiple times (e.g., the same caller calls the call center multiple times). In these "multi-touch" scenarios, in some embodiments, the task assignment system can always assign the same items to one or more dimensions to promote consistency. For example, if a task is paired with a specific quote set when it first arrives, then each time the task arrives at a subsequent time (e.g., for a given issue, within a given time period, etc.), the task is paired with the same quote set.

[0049] In some embodiments, the historical assignment module 250 can be communicatively coupled to the task assignment system 200 and / or configured to operate in the task assignment system 200 via other modules such as the task assignment module 210 and / or the task assignment strategy module 240. The historical assignment module 250 may be responsible for various functions, such as monitoring, storing, retrieving and / or outputting information about the task-agent assignments and higher dimensional assignments that have been performed. For example, the historical assignment module 250 can monitor the task assignment module 210 to collect information about the task assignments within a given time period. Each record of the historical task assignment may include information such as an agent identifier, a task or task type identifier, a quote or quote set identifier, result information, or a pairing strategy identifier (i.e., an identifier indicating whether a BP strategy, a decision BP strategy, or some other pairing strategies such as a FIFO or PBR pairing strategy are used for task assignment).

[0050] In some embodiments and for certain contexts, additional information may be stored. For example, in the context of a call center, the historical assignment module 250 may also store information about the call start time, the call end time, the phone number dialed, and the caller's phone number. For another example, in the context of a dispatch center (e.g., a "truck roll"), the historical assignment module 250 may also store information about the time the driver (i.e., field agent) left the dispatch center, the recommended route, the route taken, the estimated travel time, the actual travel time, the amount of time spent at the customer site handling customer tasks, and the like.

[0051] In some embodiments, the historical allocation module 250 can generate a pairing model, a decision BP model, or a similar computer processor-generated model based on a set of historical allocations within a period of time (e.g., the past week, the past month, the past year, etc.), which can be used by the task allocation strategy module 240 to make task allocation recommendations or instructions to the task allocation module 210.

[0052] In some embodiments, instead of relying on a predetermined quotation set in generating a decision-making BP model, the historical allocation module 250 can analyze historical results data to create or determine a new or different quotation set, which is then incorporated into the decision-making BP model. This method is preferred when there are budget constraints or other restrictions on the number of specific quotation sets that may be made. For example, the marketing department may limit the contact center system to 500 discounted sports packages and 500 discounted movie packages per month, and the company may want to optimize total revenue regardless of how many sports and movie packages are sold with or without discounts. In this case, the decision-making BP model may be similar to the previously disclosed BP diagonal model, except that in addition to the "task or contact percentile" (CP) dimension and the "agent percentile" (AP) dimension, there is a third "income or quotation set percentile" dimension. In addition, all three dimensions can be normalized or processed by mean regression (e.g., Bayesian mean regression (BMR) or hierarchical BMR).

[0053] In some embodiments, the history allocation module 250 can generate a decision BP model that optimizes task-agent-quote set pairings based on a single channel or multi-channel interactions. The history allocation module 250 can handle different channels differently. For example, the decision BP model can pair a contact with different agents or quotation sets depending on whether the contact calls a call center, initiates a chat session, sends an email or text message, enters a retail store, etc.

[0054] In some embodiments, the task assignment strategy module 240 can proactively create tasks or other actions (e.g., recommend outbound contact interactions, suboptimal actions, etc.) based on information about contacts or customers, available agents, and available quote sets. For example, the task assignment system 200 can determine that a customer's contract is set to expire, the customer's usage is declining, or the customer's credit rating is declining. The task assignment system 200 can further determine that the customer is unlikely to renew the contract at the customer's current rate (e.g., based on information from the historical assignment module 250). The task assignment system 200 can determine that the suboptimal action is to call the customer (contact selection, channel selection, and time selection), contact a specific agent (agent selection), and give the agent the option to propose a downgrade at a specific discount or discount range (quote set selection). If the customer does not reach an agreement during the call, the task assignment system 200 can further determine that the customer is more likely to accept the downgrade discount offer (multi-channel selection and optimization) if the agent follows up with a text message with information about the discount and how to confirm.

[0055] In some embodiments, similar to how the Kappa (κ) parameter is used to adjust / bias agent percentiles or percentile ranges (see U.S. Pat. No. 9,781,269) and how the Rho (ρ) parameter is used to adjust / bias task or contact percentiles or percentile ranges (see U.S. Pat. No. 9,787,841), the task assignment strategy module 240 can apply the Iota (ι) parameter to a third (or higher) dimension, such as a quote set percentile or percentile range in a decision BP strategy. Using the Iota parameter, the task assignment strategy module 240 can, for example, adjust the quote set percentile or percentile range (or other dimensions) to bias the task-agent-quote set pairing toward higher performing quotes and unbalanced quote set availability. The Iota parameter can be applied in either L1 or L2 environments and can be used in conjunction with the Kappa or Rho parameter, or can be applied together with both the Kappa and Rho parameters in an L3 environment. For example, if the task assignment strategy module 240 determines that the expected wait time for a contact has exceeded 100 seconds (high congestion), it can apply Iota parameters so that the agent is more likely to have a higher discount available for a quote, which can be sold more quickly to reduce congestion and expected wait time. More generous quotes can speed up the task-agent interaction, thereby reducing the average handle time (AHT). When congestion is low, the expected wait time is also low, and the Iota parameters can be adjusted to make only less generous quotes available. These calls may take longer and the AHT may increase, but sales and revenue may also increase.

[0056] In some embodiments, the task allocation strategy module 240 can optimize performance by optimizing for multiple business goals or other objectives simultaneously. In the case of competing goals (e.g., discount amount and retention rate), the module 240 can balance the trade-offs between the two goals. For example, the task allocation strategy module 240 can balance between increasing (or maintaining) revenue and maintaining or minimizing retention rate, or can balance between reducing (or maintaining) AHT and increasing (or maintaining) customer satisfaction, etc.

[0057] In some embodiments, the task assignment strategy module 240 can implement a decision-making BP strategy that considers agent compensation instead of a quote or quote set. The framework is similar to the description above, except that the decision-making BP strategy influences the agent's performance through the compensation that the agent can receive instead of influencing the customer through a quote or quote set. In other words, instead of a three-dimensional pairing of task-agent-quote set, the decision-making BP strategy makes a three-dimensional pairing of task-agent-reward. In some embodiments, the decision-making BP strategy can make a four-way pairing of task-agent-quote-reward.

[0058] A decision-making BP strategy that can provide variable agent compensation based on task-agent pairings may result in greater transparency and fairness. For example, some task assignment (e.g., contact center) systems may experience a mix of more challenging and less challenging contacts and employ a mix of higher performing and lower performing agents. Under a FIFO strategy or a PBR strategy, agents of any ability are equally likely to be paired with more or less challenging contacts. Under a FIFO strategy, the overall performance of the contact center system may be lower, but average agent compensation may be transparent and fair. Under a PBR strategy, agent utilization may be skewed, and compensation may also be skewed toward higher performing agents. Under the previously disclosed BP strategy, more challenging contact types may be preferentially paired with higher performing agents, while less challenging contact types may be preferentially paired with lower performing agents. For example, if high performing agents experience more difficult calls on average, this "call type skew" may result in a decrease in conversion rates and a decrease in compensation for high performing agents.

[0059] Therefore, adjusting the compensation of the agent up or down for a given task (or contact) type may improve the fairness of compensation under the previously disclosed BP strategy. When the agent is paired with a task or contact, the decision-making BP strategy can inform the agent of the contact's expectations for the contact center and / or how much commission or other non-monetary rewards the agent will receive for handling the contact or obtaining a specific result. The decision-making BP strategy may influence the behavior of the agent by proposing variable compensation. For example, if the decision-making BP strategy determines that the agent should handle the task or contact quickly (lower AHT, lower income), the decision-making BP strategy may choose a lower compensation. Therefore, the agent may not be motivated to spend a lot of time to earn a relatively low commission. On the contrary, if the decision-making BP strategy determines that the agent should devote energy to higher value calls (higher AHT, higher income), it may choose a higher compensation. Such a decision-making BP strategy can maximize the agent's reward while improving the overall performance of the task allocation system 200.

[0060] The historical allocation module 250 can model the decision-making BP model based on historical information about task types, seats, and compensation amounts, so that simultaneous selection of tasks, seats, and rewards can be made. In order to improve the overall performance of the task allocation system 200, the amount of change in compensation upward or downward can vary and depends on each combination of a single seat and contact type.

[0061] Similar to applying Iota parameters to the bid set or suboptimal action percentile or percentile range dimensions, and as described above, the task assignment strategy module 240 can apply Iota parameters to other dimensions, such as skewing agent compensation to a greater or lesser degree, or to generally higher or generally lower values. In some embodiments, the amount and type of Iota parameters applied to agent compensation or other non-monetary incentives can be based at least in part on factors in the task assignment system 200 (e.g., the expected wait time for callers to be answered in a call center).

[0062] In some embodiments employing a strategy similar to the diagonal model BP strategy, the variable compensation may be viewed as temporarily influencing the available agent's active agent percentile (AP) higher or lower in order to move the available contact-agent pairings closer to the optimal diagonal. Similarly, adjusting the value of the offer higher or lower may be viewed as influencing the active contact percentile (CP) of the waiting contacts higher or lower in order to move the available contact-agent pairings closer to the optimal diagonal.

[0063] In some embodiments, the benchmarking module 260 can be communicatively coupled to the task allocation system 200 via other modules such as the task allocation module 210 and / or the historical allocation module 250 and / or be configured to operate in the task allocation system 200. The benchmarking module 260 can benchmark the relative performance of two or more pairing strategies (e.g., FIFO, PBR, BP, decisional BP, etc.) using historical allocation information received from, for example, the historical allocation module 250. In some embodiments, the benchmarking module 260 can perform other functions, such as establishing a benchmarking schedule for cycling between various pairing strategies, tracking groups (e.g., base groups and measurement groups of historical allocations), etc. Benchmarking is described in detail for a contact center context in, for example, U.S. Patent No. 9,712,676, which is incorporated herein by reference.

[0064] In some embodiments, the benchmark module 260 can output or otherwise report or use relative performance measures. The relative performance measures can be used to evaluate the quality of the task allocation strategy to determine, for example, whether a different task allocation strategy (or a different pairing model) should be used, or to measure the overall performance (or performance gain) achieved within the task allocation system 200 when the task allocation system 200 is optimized or otherwise configured to use one task allocation strategy instead of another task allocation strategy.

[0065] In some embodiments, the benchmarking module 260 can benchmark the decision BP strategy against one or more alternative pairing strategies (such as FIFO) in conjunction with the availability of the offer set. For example, in a contact center system, agents may have a matrix of nine offers - three levels of service level, each with three discount levels. During the "off" call, the agent with the longest waiting time may be connected to the caller who has been waiting the longest, and the agent can offer any of the nine offers. Higher performing agents are more likely to sell higher levels of service at higher prices, while lower performing agents may not try so hard and instead offer the biggest discount immediately. During the "on" call, the decision BP strategy can pair contacts with agents, but restrict agents to a subset of the nine available offers. For example, for certain contact types, if the task assignment strategy module 240 determines based on the decision BP model that the overall performance of the contact center system can be optimized by selectively restricting the offer set in a given manner for a given contact-agent pairing, then higher performing agents may be authorized to make any of the nine offers, while lower performing agents may be restricted to only proposing smaller discounts for certain levels. Additionally, if a provider (eg, a vendor) that provides a decision-making BP strategy to a task assignment system uses a benchmarking and revenue sharing business model, the provider can contribute a share of the revenue gain from benchmarking to the agent compensation pool.

[0066] In some embodiments, the task assignment system 200 may provide dashboards, visualizations, or other analytics and interfaces to improve the overall performance of the system. For each agent, the analytics provided may change based on the relative capabilities or behavioral characteristics of the agent. For example, competitive or higher performing agents may benefit from ranking tools or other "gamification" elements (e.g., badges or achievements that unlock points and scoreboards, notifications when an agent surpasses another agent in the rankings, etc.). On the other hand, less competitive or lower performing agents may benefit from regular messages of encouragement, recommendations for training / educational courses, etc.

[0067] Figure 3 A task allocation method 300 according to an embodiment of the present disclosure is shown.

[0068] The task assignment method 300 may begin at block 310. At block 310, the task assignment method 300 may rank a plurality of tasks that may be queued in a task assignment system (e.g., the task assignment system 200). The task assignment method 300 may rank the plurality of tasks by assigning a percentile or a range of percentiles to the plurality of tasks according to, for example, a model such as an artificial intelligence model. In a contact center system, contacts may be ranked according to how long each contact has waited relative to other contacts to be assigned to an agent, or how much each contact contributes relative to other contacts to the performance of the contact center system according to some metric. In other embodiments, the plurality of tasks may be analyzed according to an "off-diagonal" model (e.g., a network traffic model).

[0069] The task assignment method 300 may then proceed to block 320. At block 320, the task assignment method 300 may rank the plurality of agents available in the task assignment system. The task assignment method 300 may rank the plurality of agents by giving the plurality of agents percentiles or percentile ranges according to a model, such as an artificial intelligence model. In a contact center system, the agents may be ranked according to how long each agent has waited to be assigned to a contact relative to other agents, or how much each agent contributes to the performance of the contact center system relative to other agents according to some metric. In other embodiments, the plurality of tasks may be analyzed according to a network flow model.

[0070] The task assignment method 300 may then proceed to block 330. At block 330, the task assignment method 300 may pair a task in the plurality of tasks with an agent in the plurality of agents. In some embodiments, the task-agent pairing may be based at least in part on a ranking of the plurality of tasks, a ranking of the plurality of agents, and a compensation to be received by at least one agent in the plurality of bids to be proposed to the plurality of tasks. In some embodiments, the task-agent pairing may be based on an alternative behavioral pairing technique, such as a network flow model.

[0071] In some embodiments, the task assignment method 300 may then select a quote or set of quotes and / or agent rewards to be used in conjunction with the task-agent pairing. In other embodiments, the task assignment method 300 may perform a three-way pairing (e.g., task-agent-quote, task-agent-reward, etc.) or a four-way pairing (e.g., task-agent-quote-reward). A given quote may skew or adjust a percentile or percentile range for a task, while a given compensation may skew or adjust a percentile or percentile range for an agent. Therefore, by considering at least one quote or compensation, the task assignment method 300 is able to select a task-agent (or task-agent-quote, task-agent-compensation, task-agent-quote-compensation, etc.) pairing, thereby improving the overall performance of the task assignment system.

[0072] At this point, it should be noted that the task allocation 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 the task allocation according to the present disclosure as described above. Alternatively, one or more processors operating according to instructions can implement the functions associated with the task allocation according to the present disclosure as described above. If this is the case, 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, which is also within the scope of the present disclosure.

[0073] The present disclosure is not limited to the scope of the specific embodiments described herein. In fact, in addition to those described herein, various other 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 that the present disclosure can be advantageously implemented in a variety of environments for various purposes. Therefore, the following claims should be interpreted in view of the full breadth and spirit of the present disclosure as described herein.

Claims

1. A method for determining behavior pairing in a task allocation system, the method comprising: determining, by at least one computer processor communicatively coupled to the task assignment system and configured to operate in the task assignment system, a first task available for pairing; Determining, by the at least one computer processor, a first seat available for pairing; prior to pairing with the first agent, associating, by the at least one computer processor, a first measurement with the first task; pairing, by the at least one computer processor, the first task with the first agent based on at least one pairing strategy; after pairing with the first agent, associating, by the at least one computer processor, a second measurement with the first task; comparing, by the at least one computer processor, the first measurement value with the second measurement value to determine an influencing factor of the first task; determining, by the at least one computer processor, a second task available for pairing; as well as The at least one computer processor pairs the second task based on the at least one pairing strategy and the influencing factor of the first task.

2. The method of claim 1, wherein: The first measurement includes a first revenue value expected to be realized as a result of pairing the first task with the first agent.

3. The method of claim 2, wherein: The second measurement value includes a second revenue value actually achieved as a result of pairing the first task with the first agent.

4. The method of claim 1, wherein: The task distribution system is a contact center system, and the first task is a first caller that may call into the contact center system.

5. The method of claim 4, wherein: As a result of pairing the first caller with the first agent without the first agent providing at least one offer to the first caller, the first caller is more likely to contribute to a successful outcome.

6. The method of claim 4, wherein: As a result of pairing the first caller with the first agent without the first agent providing at least one offer to the first caller, the first caller is less likely to contribute to a successful outcome.

7. The method of claim 4, wherein: When the first agent is a high performing agent, the first caller is more likely to contribute to a successful outcome as a result of pairing the first caller with the first agent.

8. The method of claim 4, wherein: When the first agent is a high performing agent, as a result of pairing the first caller with the first agent, the first caller is less likely to contribute to a successful outcome.

9. A system for determining behavior pairing in a task assignment system, comprising: at least one computer processor communicatively coupled to the task assignment system and configured to operate in the task assignment system, wherein the at least one computer processor is further configured to: determining a first task available for pairing; Identify the first seat available for pairing; associating a first measurement with the first task prior to pairing with the first agent; pairing the first task with the first agent based on at least one pairing strategy; after pairing with the first agent, associating a second measurement with the first task; comparing the first measurement value with the second measurement value to determine an influencing factor of the first task; determining a second task that can be used for pairing; as well as The second task is paired based on the at least one pairing strategy and the influencing factor of the first task.

10. The system of claim 9, wherein: The first measurement includes a first revenue value expected to be realized as a result of pairing the first task with the first agent.

11. The system of claim 10, wherein: The second measurement value includes a second revenue value actually achieved as a result of pairing the first task with the first agent.

12. The system of claim 9, wherein: The task distribution system is a contact center system, and the first task is a first caller that may call into the contact center system.

13. The system of claim 12, wherein: As a result of pairing the first caller with the first agent without the first agent providing at least one offer to the first caller, the first caller is more likely to contribute to a successful outcome.

14. The system of claim 12, wherein: As a result of pairing the first caller with the first agent without the first agent providing at least one offer to the first caller, the first caller is less likely to contribute to a successful outcome.

15. The system of claim 12, wherein: When the first agent is a high performing agent, the first caller is more likely to contribute to a successful outcome as a result of pairing the first caller with the first agent.

16. The system of claim 12, wherein: When the first agent is a high performing agent, as a result of pairing the first caller with the first agent, the first caller is less likely to contribute to a successful outcome.

17. An article for determining a behavior pairing in a task allocation system, the article comprising: a non-transitory processor-readable medium; as well as instructions stored on said medium; wherein the instructions are configured to be readable from the medium by at least one computer processor communicatively coupled to the task allocation system and configured to operate in the task allocation system, thereby causing the at least one computer processor to operate to: determining a first task available for pairing; Identify the first seat available for pairing; associating a first measurement with the first task prior to pairing with the first agent; pairing the first task with the first agent based on at least one pairing strategy; after pairing with the first agent, associating a second measurement with the first task; comparing the first measurement value with the second measurement value to determine an influencing factor of the first task; determining a second task available for pairing; and The second task is paired based on the at least one pairing strategy and the influencing factor of the first task.

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