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.

A method and system automatically optimizes agent time. The method performed by a transferring device includes monitoring a communication session between an agent device used by an agent of the contact center and a user device used by a user. The communication session including first communications generated by the agent and second communications generated by the user. The method includes determining when the communication session is to be transferred from the agent device to an automated system of the contact center. The automated system is configured to perform the communication session by generating third communications for the second communications. The method includes generating a control signal upon determining the communication session is to be transferred that is configured to automatically transfer the communications session from the agent device to the automated system.

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 adaptive conversational system may simultaneously monitor multiple active calls or ongoing voice or telephone conversations, may extract a different set of conversation elements from a current point in each conversation in real-time as each conversation proceeds, may determine different rules that apply to current points of different ongoing conversations based on the extracted conversation elements satisfying different rule triggers, and may control different conversations at different times according to actions of different rules that are applied at different times to different conversations. The system may selectively control the conversations when the conversations become non-compliant, deviate from best practices, or can be controlled to more effectively reach a positive disposition than when allowing a telephone agent to independently control the conversation. The system may use machine learning and/or artificial intelligence to define rules based on tracked actions that are produce a positive disposition more effectively than existing rules.

The present invention is a system and method of continuous sentiment tracking and the determination of optimized agent actions through the training of sentiment models and applying the sentiment models to new incoming interactions. The system receives conversations comprising incoming interactions and agent actions and determines customer sentiment on a micro-interaction level for each incoming interaction. Based on interaction types, the system correlates the determined sentiment with the agent action received prior to the sentiment determination to create and train sentiment models. Sentiment models include agent action recommendations for a desired sentiment outcome. Once trained, the sentiment models can be applied to new incoming interactions to provide CSRs with actions that will yield a desired sentiment outcome.

A method and system for providing a data consolidation for improved customer communication and agent performance evaluation in a multi-channel contact center are provided. The method includes receiving interaction data of an agent with customers across different communication channels within the contact center, consolidating the interaction data received across the different communication channels, aggregating the consolidated data to generate a set of metrics indicative of agent performance, inputting the set of metrics into an agent performance measurement framework, and determining a performance score for the agent based on an output of the agent performance measurement framework.

A computer-implemented method of predicting customer satisfaction scores for a call center is disclosed, along with the use of the predicted customer satisfaction scores to perform various analytical functions, such as identifying changes to the predicted customer satisfaction score and identifying root causes of the predicted customer satisfaction scores. In some implementations, a pipeline includes an inference engine that includes an AI model trained on call transcripts and call attribute data to predict a customer satisfaction score.

Embodiments disclosed are directed to a computing system that performs steps to automatically generate fine-grained call reasons from customer service call transcripts. The computing system extracts, using a natural language processing (NLP) technique, a set of events from a set of text strings of speaker turns. The computing system then identifies a set of clusters of events based on the set of events and labels each cluster of events in the set of clusters of events to generate a set of labeled clusters of events. Subsequently, the computing system assigns each event in the set of events to a respective labeled cluster of events in the set of labeled clusters of events.

Assigning contacts in a contact center including extracting, by a blend application, contextual information related to an inbound contact received at a first port and transmitting, by the blend application, the contextual information to an outbound dialer. The outbound dialer places an outbound call from a second port to the first port, automatically patches the second port with a third port that is associated with an agent assigned to an outbound campaign, and connects a customer device associated with the inbound contact to a device associated with the agent, during which the agent remains assigned to the outbound campaign.

A mobile application for a wireless handheld computing device, such as a smartphone, is disclosed in combination with a communication link establishment and management system. Systems and methods are disclosed for calling desired contacts using a smartphone that can take advantage of the power and efficiency of agent-assisted dialing provided by the communication link establishment and management system. The systems and methods automatically integrate with a customer relationship management (CRM) system connected to the communication link establishment and management system.