A contact center network including a contact center unit connected via a communication network to a plurality of agents and to a plurality of IoT devices, wherein each one of the plurality of IoT devices is equipped with sensor devices adapted to measure predetermined IoT measurement data, and at least one actuator device adapted to control the IoT device remotely, wherein the contact center unit is connectable to the sensor devices and the actuator devices via a IoT middleware unit adapted to receive an incident notification, if sensor measurement data matches a predetermined criterion indicating an incident, and wherein the contact center unit includes a routing unit adapted to route a contact based on the incident information comprised in the incident notification to an agent. Further, embodiments relate to a method for establishing a communication session in a contact center network.

Apparatuses and methods concerning providing a data-communications virtual assistant are disclosed. As an example, one apparatus includes a data-communications server. The data-communications server is configured to process user-data-communications between a client station and another station participating in data-communications via the data-communications services where the client station is associated with one client entity. The server is also configured to identify a context for each respective user-data-communication between the client station and the participating station, where the context data corresponds to at least one communications-specific characteristic associated with the user-data-communications, and to retrieve structured and unstructured data relating to previous data-communications provided by the data-communications server. The server is also configured to provide the integrated contact center to particular end users based on the identified context, and including data generated from the retrieved structured and unstructured data.

A method of routing interactions to contact center agents according to an embodiment includes identifying an interaction to be routed to a contact center agent, identifying a group of contact center agents as candidates for routing of the interaction, retrieving agent performance data for each candidate agent of the group of contact center agents identified as candidates for routing of the interaction, determining a predicted score for a key performance indicator for each candidate agent based on the agent performance data, determining an occupancy rate of each candidate agent based on the agent performance data, generating a ranking of the candidate agents for routing prioritization based on the predicted score for the key performance indicator for each candidate agent and the occupancy rate of each candidate agent, and signaling a routing device to route the interaction to a selected candidate agent based on the ranking of the candidate agents.

Techniques for benchmarking performance in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for benchmarking contact center system performance comprising cycling, by at least one computer processor configured to perform contact center operations, between a first contact-agent pairing strategy and a second contact-agent pairing strategy for pairing contacts with agents in the contact center system; determining an agent-utilization bias in the first contact-agent pairing strategy comprising a difference between a first agent utilization of the first contact-agent pairing strategy and a balanced agent utilization; and determining a relative performance of the second contact-agent pairing strategy compared to the first contact-agent pairing strategy based on the agent-utilization bias in the first contact-agent pairing strategy.

A method for routing customer service requests to call centers includes collecting data associated with customer service experience between a customer and a call center regarding a completed customer call. The collected data is analyzed to determine a quality of customer service for one or more completed calls between the customer and the call center. A nature of an incoming customer call is determined. The incoming customer call is routed to a call center based upon making reference to the analyzed collected data such that the nature of the customer call matches with corresponding one or more favorable attributes of the call center.

Provided herein are methods, systems, computing devices, and/or computing entities for generating a performance metric recommendation by generating an agent group data object for an agent profile; generating an agent assessment data object for the agent profile; generating an inferred performance gap data object for the agent profile; and generating the performance metric recommendation based at least in part on the inferred performance gap data object.

Predictive mapping technology is used to route a telephone call from a user to a customer service representative. The disclosed technology can use any one or more of the following factors to map a telephone call from a user to a customer service representative: (1) a sentiment score based on a topic of conversation; (2) an experience score of the customer service representative with a topic of conversation; and (3) a performance score of the customer service representative in managing a topic of conversation.

A computerized method for enhancing agent's efficiency in a contact center by orchestrating a multi-agent to multi-contact attribute-based routing while maintaining seamless contact interaction is provided herein. The computerized method includes receiving a request for service and in response may operate an orchestration engine. The orchestration engine derives contact related information from the communication channel to yield at least one contact attribute and according to a preconfigured set of rules and the agent's availability state, performs attribute-based routing of the request for service to one or more agents and designates a primary agent out of the one or more agents to handle the request for service, and when the orchestration engine can't find a human agent having a proficiency as defined in the computerized system, the orchestration engine is designates as a primary agent, a bot agent.

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.

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.