Methods for optimizing the routing of customer communications include receiving a customer communication; identifying a customer associated with the customer communication; accessing a profile of the identified customer to determine customer data; receiving customer metric scores for a plurality of customer metrics; identifying available agents; accessing a profile of each available agent to determine agent data; predicting interaction outcome metric values for a plurality of customer metrics based on the customer data and the agent data; calculating, in real-time, an aggregate agent-customer pairing score for each available agent; selecting a responding agent from the available agents with the highest aggregate agent-customer pairing score; and providing a routing recommendation to a communication distributor to route the customer communication to the responding agent with the highest aggregate agent-customer pairing score.

A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.

Techniques for benchmarking performance in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for benchmarking contact center system performance comprising cycling, by at least one computer processor configured to perform contact center operations, between a first contact-agent pairing strategy and a second contact-agent pairing strategy for pairing contacts with agents in the contact center system; determining an agent-utilization bias in the first contact-agent pairing strategy comprising a difference between a first agent utilization of the first contact-agent pairing strategy and a balanced agent utilization; and determining a relative performance of the second contact-agent pairing strategy compared to the first contact-agent pairing strategy based on the agent-utilization bias in the first contact-agent pairing strategy.

An enhanced abandoned call recovery (“E-ACR”) process allows certain abandoned calls to be eligible for a callback call. An E-ACR assignment point defines which abandoned calls in an inbound campaign or interactive voice response (“IVR”) menu are eligible to be processed to determine whether the E-ACR callback should occur. The determination of whether a callback occurs involves various compliance tests, such as ensuring calling window, call attempts, and other regulatory concerns are addressed. Once a callback is determined to occur, it is associated with a specific campaign to ensure the called party is provided with agents having the skill set as defined for that assignment point. In this manner, only eligible callers receive an E-ACR callback, and further receive the callback in a compliant manner and handled by the same skill set of agents as would have been allocated to the caller had they not abandoned their call.

A computer-implemented method of predicting customer satisfaction scores for a call center is disclosed, along with the use of the predicted customer satisfaction scores to perform various analytical functions, such as identifying changes to the predicted customer satisfaction score and identifying root causes of the predicted customer satisfaction scores. In some implementations, a pipeline includes an inference engine that includes an AI model trained on call transcripts and call attribute data to predict a customer satisfaction score.

Certain aspects of the disclosure are directed to template-based management of telecommunications services. According to a specific example, a server is provided comprising one or more computer processor circuits configured to interface with a remotely-situated client entity using a first programming language. The server includes a call control engine that is configured to provide a private branch exchange (PBX) for the client entity, and identify a call control template written in a second programming language. The call control engine is further configured to control call routing by the PBX, by executing the call control template to identify at least one data source that corresponds to a call property for the VoIP telephone call, retrieve data from the data source, and implement one or more call processing functions specified by the call control template as being conditional upon the retrieved data.

An exemplary embodiment of the present application is a system and method for work allocation optimization. In the present disclosure, analytics are applied to work items while the work items are waiting in a work queue in order to optimize the routing and allocation of work items to agents in the most efficient manner possible, while optimizing the utilization of agents. By performing a look ahead at more than the initial work item, the system assesses the agent skills required by imminent work items in the work queue. This is then compared to a skillset of each available and/or soon to be available agent in order to achieve the optimal allocation of the work items to maximize utilization of agents. The work items are then routed to the agents accordingly.

The invention concerns a query response device comprising: an input adapted to receive user queries; a memory (106) adapted to store one or more routing rules; one or more live agent engines (116) configured to support interactions with one or more live agents; one or more virtual assistant engines (120) configured to support interactions with one or more virtual assistants instantiated by an artificial intelligence module (103); and a routing module (104) coupled to said live agent engines and to said virtual assistant engines, the routing module comprising a processing device configured: to select, based on content of at least a first user message from a first user relating to a first user query and on said one or more routing rules, a first of said live agent engines or a first of said virtual assistant engines; and to route one or more further user messages relating to the first user query to the selected engine.

Techniques for hybrid behavioral pairing in a contact center system are disclosed. In one embodiment, the techniques may be realized as a method for hybrid behavioral pairing in a contact center system comprising: determining a first ordering of a plurality of contacts according to a behavioral pairing strategy with a balanced contact utilization; determining a second ordering of the plurality of contacts according to a performance-based routing strategy with an unbalanced contact utilization; determining a third ordering of the plurality of agents according to a combination of the first ordering and the second ordering having a skewed contact utilization between the balanced contact utilization and the unbalanced contact utilization; and outputting a hybrid behavioral pairing model based on the third ordering for connecting an agent to a contact of the plurality of contacts in the contact center system.

A computer-implemented method is presented for selecting a preferred live agent from a plurality of live agents. The method includes constructing, via the processor, a human expertise matrix pertaining to each of the plurality of live agents by determining an average net promoter score (NPS) for each of the plurality of live agents for each category of a plurality of categories, and in response to a voice call by a user, determining, via the processor, a predicted human expertise on average by collectively assessing the human expertise matrix, a predicted NPS derived from a first deep neural network, and a predicted category derived from a second deep neural network. The method further includes, based on the predicted human expertise on average determined, triggering communication via the live agent communication network between the user and the preferred live agent to initiate a conversation between the user and the preferred live agent.