Techniques for workforce management in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for workforce management in a contact center system comprising generating historical workforce data regarding an agent workforce capacity of the contact center system, and initiating an increase or decrease to an agent workforce of the contact center system based at least in part on the historical workforce data to increase an amount of choice among available agents or waiting contacts.

A computerized-method for determining an agent-proficiency when addressing concurrent customer sessions via one or more channel types and utilization thereof is provided herein. The computerized-method includes operating a Concurrent-Sessions-Handling-Agent-Proficiency (CSHAP) module. The CSHAP-module includes: (a) operating an interactions-module to retrieve one or more interactions and metadata thereof of the agent; (b) for each interaction, determining if the interaction has been handled with concurrent interactions; (c) for each determined interaction as handled with concurrent interactions, checking in the metadata if the interaction has one or more defocused-events; (d) calculating a CSHAP-score for the agent based on one or more attributes from the metadata of the interaction to provide an indication as to an ability of the agent to address concurrent customer sessions; (e) storing the calculated CSHAP-score in a data-store; and (f) sending the CSHAP-score to one or more applications, to take one or more follow-up actions based on the CSHAP-score.

Embodiments of the present invention provide systems and methods for determining an engagement level for a customer-agent interaction (e.g., a phone call, text chat, etc.), and the engagement level can be used to evaluate the performance of the agent. For example, the engagement level can be used to determine or adjust a skill level of the agent, a performance of the communication channel that facilitates the interaction, and/or an optimal workload of the agent. According to some embodiments, the engagement level is used to determine that the agent may be experiencing difficulty satisfying a customer inquiry such that the agent should be assigned resources or assistance.

Aspects of human assisted virtual agent support are discussed. A conversation between a user and a virtual agent may be monitored. A probability of the user abandoning the conversation may be predicted and a notification may be provided to a human agent to provide assistance in the conversation based on the probability.

Apparatuses and methods concerning providing a data-communications contact center virtual agent are disclosed. As an example, user-data-communications between client and participant stations are facilitated as follows, which may be implemented using a data communications server and associated communications circuitry. Service request data is received from users at a participant stations, and context information is identified for user-data-communications between a client station and the participant stations based on the service request data at least one communications-specific characteristic associated with the user-data-communications. The identified context information is aggregated for the client station and used for choosing a data routing option routing data with each user at the participant stations, based on the service request data and the aggregated context information.

Methods, apparatus, and media for assigning agents for shifts in a call center environment. Shift template data structures are created for an agent for a single week, the shift template data structure having data fields including, an agent work time data field for a plurality of days in a week, an agent weekly minimum hour data field, and an agent weekly maximum hour data field. Worker parameters are combined with the shift template to define shift rotations which can be applied to a scheduling algorithm to assign the agent to a schedule of rotating shifts.

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.

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.

A hard phone, a method for implementing a traffic operation, a call center system, and a non-transitory computer-readable storage medium. The hard phone may include: a processing module (11), an input module (12), a sending module (13), a display device (14), and a receiving module (15). The display device (14) displays different types of traffic operation instructions such that a user carries out selection. The input module (12) receives the traffic operation instruction inputted by the user and an identity identification of a target attendant, and transmit to the processing module (11). The processing module (11) processes the traffic operation instruction and the identity identification according to a preset protocol, and then transmit same to the sending module (13), and send to a server via the sending module (13). The receiving module (15) receives and transmit the execution result to the processing module (11).

An event log computing system receives event data associated with one or more events that may impact operations of a contact center. The event log system may analyze event data and one or more contact center performance metrics to determine an actual impact level of the event on each of the one or more contact center performance metrics. The event log system may also analyze anomalies in one or more contact center performance metrics and event data in the event log to identify events in the event log that may be associated with the anomalies.