Electronic communication methods and systems for rating agents and allowing customers for select agents are provided. Exemplary systems include a communication server for connecting a user to a communication with a contact center, a presence system to determine status of one or more agents, a wait-time determination engine to determine a wait time for one or more agents present for the communication, an agent scoring system for determining a score for one or more agents present for the communication, and a routing engine to connect a selected agent to the communication.

A redirection and messaging system receives telephony information identifying a caller and call context from a telephony system. The system selects one of a plurality of messaging operators based on the call context, optionally sends an introductory message to the caller via a messaging service, and generates a message interface for the selected message operator. The message interface includes the caller and call context and any messages sent between the caller and the selected message operator, with an input interface allowing the selected message operator to input and send messages to the caller.

The present invention is a system and method for projective channel hopping within a customer engagement center (CEC) system. The CEC system receives a customer through a system entry point and receives, from the customer, a communication in a current customer service representative (CSR) channel in the CEC system. The CEC system assesses its ability to connect the customer with a CSR on the current CSR channel using a smart routing engine (SRE), which also assesses its current ability to connect the customer with a CSR on other equivalent CSR channels. Using the SRE, the CEC system compares its ability to connect the customer with a CSR on current and equivalent CSR channels to determine if the customer should remain on their current CSR channel or transfer to a new one. In the latter case, the CEC system offers the customer a chance to change channels or remain on their current channel.

Assigning an appropriate channel in an omni-channel contact center environment includes receiving, by a computer of the contact center, an incoming contact via a first communication channel, the incoming contact assignable to a plurality of available communication channels; queuing, by the computer, the incoming contact in a first queue associated with the first communication channel; determining, by the computer, a preferable communication channel from among the plurality of communication channels and the first communication channel; and when the preferable communication channel is one of the plurality of communication channels, determining whether to assign the incoming contact to the preferable communication channel.

Disclosed are systems and methods for forecasting inbound telecommunications, and more particularly, for analyzing real-time and historical call center data, and applying a forecasting model to said data in order to predict inbound call volume. Additionally, tools are disclosed for manipulating call center data and generating visual representations of metrics pertaining to forecasting call center data via a dashboard.

A computerized-method for operating an Omni Session Handling (OSH) engine for proactive OSH to determine that an outreach is required, according to one or more predefined requirements, to maximize capacity of an agent in a contact center by omni session handling, is provided herein. The computerized-method may retrieve one or more interactions from a Customer-Relations-Management database based on a requirement for an outreach to be stored in a queue of proactive contacts. The computer-implemented method may further allocate a queue of one or more interactions to an agent or to a team of agents and then present the agent with a digital preview of an interaction from the queue of one or more interactions for consideration via a display unit, while the agent is handling one or more interactions. The computer-implemented method may further receive from the agent a decision upon review of the presented digital preview of the interaction.

An example system for dynamic agent management involving multiple queues is presented. The system may include a remote network management platform that manages a network by way of a computational instance. A first group and a second group of agents may be assigned to address work items in a first queue in the computational instance. The second group may also be assigned to address and prioritize work items in a second queue in the computational instance. A computing device within the computational instance may assign a work item originating from a client device in the managed network into the first queue according to routing rules. The assignment may cause a timer to record a duration that the work item is in the first queue. The computing device may assign one of the second group of agents to address the work item based on the duration exceeding a pre-determined value.

A computerized-method for redirecting, inbound-voice-interactions to digital channels in a contact center, is provided herein. The computerized-method includes: (i) operating a digital-qualifier module to determine a digital-medium-transition-quotient, of an inbound-voice-interaction of a customer in an inbound-queue. The digital-medium-transition-quotient is an indication of a level of suitability of a digital-communication-channel to resolve a customer issue; (ii) operating an interaction-redirection module to determine a digital-communication-channel for redirection of the inbound-voice-interaction, based on customer-preference and the determined digital-medium-transition-quotient; and (iii) forwarding the inbound-voice-interaction and the determined digital-communication-channel to an Automatic Call Distribution (ACD) system to be carried-out by an agent via the determined digital-communication-channel when the determined digital-medium-transition-quotient is above a preconfigured threshold.

Technologies for scaling call center support staff include one or more local agent computing devices of a call center that includes an interaction management computing device communicatively coupled to one or more customer computing devices and one or more remote agent computing devices. The interaction management computing device is configured to receive inbound service calls and insert them into a respective service queue. The interaction management computing device is additionally configured to determine whether a service queue response threshold associated with the service queue has been violated as a function of each service call having been inserted into the service queue, identify, in response to a determination that the service queue response threshold associated with the service queue has been violated, one or more remote agents from a queue of available remote agents, and add the identified one or more remote agents to an agent pool associated with the service queue. Additional embodiments are described herein.

Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.