Techniques for sharing control of assigning tasks between an external pairing system and a task assignment system with an internal pairing system are disclosed. In one embodiment, the techniques may be realized as a method for sharing control of assigning tasks between an external pairing system and a task assignment system with an internal pairing system comprising receiving, from the task assignment system, a plurality of task pairing requests and an agent pairing request, wherein each task request of the plurality of task pairing requests is assigned to one of a first pairing strategy and a second pairing strategy. The agent pairing request may indicate an agent that is available for pairing. The method may further comprise transmitting, to the task assignment system, a pairing recommendation being based in part on the plurality of task pairing requests, the first pairing strategy, the second pairing strategy, and the agent pairing request.

Methods, systems, and apparatus for prioritizing communications are described. Metadata that characterizes an electronic communication is obtained and a machine learning algorithm is applied to the metadata to generate a scoring model. A score for the electronic communication is generated based on the scoring model.

Various embodiments of the invention provide methods, systems, and computer program products for sending an outbound communication to a party to generate an inbound communication from the party for a contact center. An expected response time is derived identifying a time the inbound communication is expected to be received from the party after sending the outbound communication to the party. In particular instances, an agent is identified who is expected to be available at the expected response time to handle the inbound communication when it is received by the party at the contact center. In addition, the content of the outbound communication may be composed to identify the agent to the party. Accordingly, the outbound communication is sent to the party so that the inbound communication can be routed to the agent upon being received.

This disclosure describes management of a contact center executing in a service provider network. The management may be performed by using one or more trained deep learning/machine learning (ML) models. The ML models may be trained using metrics and data gathered from the various services and systems in the service provider network used to implement the contact center. The trained models may be used for forecasting staffing needs, e.g., agents, for the contact center, scheduling agents, detecting anomalies with regard to customer traffic, e.g., received communications from customers, at the contact center, adherence of agents to the scheduling, and dynamically routing of received communications to appropriate agents (e.g., based on skills of the agent and other factors) for handling of the communications.

A system for estimating a wait time to connect a customer to an agent according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to receive incoming interaction data from the customer indicative of a request to estimate the wait time, retrieve historical data indicative of interaction information for one or more interactions that have already been serviced in response to receipt of the request, and generate a wait time estimation based on the historical data.

A system for providing chat-based customer callbacks, wherein a callback application server transmits prompts and receives requests for customer callbacks, an interaction manager determines agent availability and arranges callback handling, and a session management server initiates callbacks, and methods for providing chat-based customer callbacks using the system of the invention.

Techniques for sharing control of assigning tasks between an external pairing system and a task assignment system with an internal pairing system are disclosed. In one embodiment, the techniques may be realized as a method for sharing control of assigning tasks between an external pairing system and a task assignment system with an internal pairing system comprising receiving, from the task assignment system, a plurality of task pairing requests and an agent pairing request, wherein each task request of the plurality of task pairing requests is assigned to one of a first pairing strategy and a second pairing strategy. The agent pairing request may indicate an agent that is available for pairing. The method may further comprise transmitting, to the task assignment system, a pairing recommendation being based in part on the plurality of task pairing requests, the first pairing strategy, the second pairing strategy, and the agent pairing request.

A system and method for managing virtual queues. A cloud-based queue service manages a plurality of queues hosted by one or more entities. The queue service is in constant communication with the entities providing queue management, queue analysis, and queue recommendations. The queue service is likewise in direct communication with queued persons. Sending periodic updates while also motivating and incentivizing punctuality and minimizing wait times based on predictive analysis. The predictive analysis uses “Big Data” and other available data resources, for which the predictions assist in the balancing of persons across multiple queues for the same event or multiple persons across a sequence of queues for sequential events.

A call management system of a call center identifies an inbound caller based upon computer analysis of customer identifiers, which may include at least two of customer name, street address, and zip code. Approximate string matching analysis matches n-grams generated from strings within customer identifiers, with n-grams generated from customer identification fields while searching one or more databases. Approximate string matching can incorporate a closeness metric based on Jaccard distance, and a Gaussian mixture model of best matches. In one embodiment, a polymr search engine analyzes customer identifiers of inbound callers to retrieve customer data, such as customer demographic data, matched to the customer identifiers. In another embodiment, the polymr search engine analyzes customer identifiers of inbound callers to identify repeat callers and retrieve previously collected customer data. Retrieved customer data is used in predictive modeling and scoring value of the inbound call, and in routing the scored inbound call.

A call handler in a contact center is configured to temporarily derogate the skill level of one or more agents in a group having a common skill. During the time period of derogation, incoming calls will be routed to agents with the highest skill level, such that the normally lower skill level agents will be selected to receive calls. If there are more than one agent meeting this criteria, then the agent that has been waiting the longest to receive a call is selected. This allows the lower skill level agents to gain experience in handling calls and improving their call handling capabilities. After a set threshold, the derogation of the agents is terminated, and their skill level is restored to the pre-derogation level. In this manner the lower skill level agents are given limited opportunities for improvement by handling calls.