A system for automatic authentication of service requests includes authentication of a remote access device. This authentication may be accomplished automatically prior to text or audio communication between a customer and a service agent. In some embodiments, authentication is accomplished automatically by authentication of the remote access device or accomplished by asking the customer questions. A single authentication of the remote access device may be used to authenticate a service request transferred between service agents. The authentication of the remote device may include, for example, use of a personal identification number, a fingerprint, a photograph, and/or a hardware identifier. Some embodiments include an intelligent pipeline configured for managing queues of customer service requests.

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.

A sequence of call volume measurements is accessed, where each of the call volume measurements is associated with respective metadata. The respective metadata may provide information regarding a time period during which a call volume measurement was made. A window of the sequence of call volume measurements with the respective metadata is input to a machine learning model to obtain a prediction of a call volume. The machine learning model includes embedding functions that are applied to the respective metadata for the call volume measurements in the window.

A method for routing incoming interactions in a contact center. The incoming interactions are instigated by customers for communicating with the contact center, and the contact center includes agents between which the incoming interactions are routed. The method includes: determining a routing interval; identifying received incoming interactions for inclusion in a received interactions batch, each of the received incoming interactions being identified as ones of the incoming interactions that are received within the routing interval; identifying candidate agents for inclusion in a candidate agent group, the candidate agents being identified as available ones of the agents to whom the received incoming interactions of the received interaction batch can be routed; and subsequent to the end of the routing interval, routing the received incoming interactions included in the received interaction batch to the candidate agents included in the candidate agent group.

To schedule a future interaction between a caller and a call-center, a management computing system associated with the call center is configured to determine an estimated call time for addressing the caller's issues, based at least in part on a predicted call intent as well as feature enrichment data received from one or more memory storage areas. Upon determining a predicted call duration for the call, the management computing entity accesses the caller's calendar, such as through an internet-based communication between the caller's user computing entity and the management computing entity, or via third-party access permissions provided by the caller. The management computing entity identifies one or more candidate timeslots based at least in part on the predicted call duration, and receives user input selecting a candidate timeslot for scheduling the callback.

A computerized-method using a cloud-based computing environment for improving client service, in a contact center is provided herein. The computerized-method includes: retrieving a context of a query and a time-limit from a CTI event and attempting to retrieve data to evaluate average resolution time for the received context. When the data is found, comparing the evaluated average resolution time with the received time-limit and when the received time-limit is below the evaluated average resolution time, sending a delay notice and providing the client a menu of options for querying through other channels. When the data is not found, or when the received time-limit is above the evaluated average resolution time, presenting on an agent dashboard, the time-limit of the client and accordingly updating parameters in the agent dashboard during the inbound call, thus, improving client service, by considering the time-limit of the client before the agent addresses a query.

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.

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 determination is made that a real-time communication session (e.g., a voice call) between a first agent terminal and a customer communication endpoint has been placed on hold by a first contact center agent. An estimated hold time for the real-time communication is determined, based on a rule, in response to determining that the real-time communication session has been placed on hold. It is determined that the estimated hold time for the real-time communication session has expired. In response to determining that the estimated hold time for the real-time communication session has expired, one or more actions occur. For example, the real-time communication session is automatically transferred to a second agent terminal.

Implementations relate to determination and display of estimated hold durations for calls. In some implementations, a computer-implemented method includes obtaining an identifier of a target entity, the identifier usable by a first call device to initiate a call between the first call device and the target entity. A hold duration is determined that is an estimated amount of time before the call is matched to a human agent when the call is initiated at a particular time that is prospective to a current time. The hold duration is determined based on previous calls between one or more call devices and the target entity. The hold duration is provided for display by the first call device prior to the initiation of the call between the first call device and the target entity.