A processor-implemented method of enabling a customer support interaction in multiple channels in parallel is provided. The processor-implemented includes (i) receiving a call at a contact center from a customer, (ii) providing a notification to the customer with reference to a hold time to reach a live human agent at a first live channel, (iii) selecting at least one automation task to be performed for a customer's requirement by a virtual agent at a second non-live channel, and (iv) enabling transfer of the customer from the first live channel to the second non-live channel with the virtual agent and communicating the at least one automation task to be performed for the customer's requirement to the virtual agent at the second non-live channel. The at least one automation task is a series of steps performed by the virtual agent to complete an objective.

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.

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.

The present disclosure relates to a method of managing a voice call having been queued to await a response by a human representative of a call centre, and a device performing the method. In an aspect, a method is provided of managing a voice call having been queued to await a response by a human representative of a recipient of the voice call, the method being performed by a communications device of a calling party and which includes detecting, from a voice indication provided by the recipient, information indicating a current position of the voice call in the queue, and displaying the detected information indicating a current position of the voice call in the queue.

Computer-implemented techniques for answer time prediction in a contact center service of a provider network. While a delayed processing timing has not been met, a set of contact queuing context-actual answer time data for a set of contact inquiries serviced is received as a first set of contact queuing context-actual answer time data. When the delayed processing timing has been met, a new queuing model is learned based on the first set of contact queuing context-actual answer time data and a previous set of contact queuing context-actual answer time data for a previous set of contact inquiries serviced or a previous version of the queuing model. A request to predict an answer time for a target contact queuing context is received and an answer time for the target contact queuing context is predicted based on the new queuing model. The predicted answer time is provided to a contact via a contact communications channel.

A system can route calls based on a minimum expected delay, classification, and priority of the call. In some embodiments, when a call is received, the call is classified and prioritized within the classification. A minimum expected delay time is determined. Based on the minimum expected delay time, the classification, and the prioritization, the call is routed to a representative with instructions on how the call should be treated.

A system for providing integrated contact information with search results, comprising a plurality of contact centers, each comprising at least a plurality of contact agents; a callback cloud, comprising at least a plurality of contact agents; a queue manager, comprising at least a software components operating and stored on a computing device; wherein the contact agents receive and respond to customer interactions; further wherein the queue manager monitors contact agent availability; further wherein the queue manager receives interaction requests; and further wherein the queue manager provides queue information to interaction requestors.

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 system for providing active callback management using callback objects and contact information integration, utilizing a cloud callback system comprising at least a profile manager, callback manager, interaction manager, media server, and environment analyzer, allowing users to call businesses, agents in contact centers, or other users who are connected to a cloud callback system, and, failing to connect to the individual they called, allow for an automatic callback object to be created, whereby the two users may be automatically called and bridged together at a time when both users are available.

Systems and methods are provided for dynamic generation of staff analytics and forecasts based on skill and service level. Dynamic forecasting allows for forecast generation in real-time and may be based on historical data regarding skills and results, as well as data science to identify patterns and make predictions. The resulting staffing forecast may therefore provide for efficient management of messaging staff costs while preserving the desired service quality. The staffing forecast may include a volume forecast that is tailored to the unique nature of asynchronous messaging, as well as the unique messaging needs of the entity so as to efficiently manage messaging operations and make data-driven staffing decisions that take service level into account. An exemplary embodiment may include dynamic analytics tools that may use specified target and/or resource numbers (e.g., desired service level) for an existing messaging operation and get a detailed per-skill staffing forecast.