A redirection and messaging system receives telephony information identifying a caller and call context from a telephony system. The system selects one of a plurality of messaging operators based on the call context, optionally sends an introductory message to the caller via a messaging service, and generates a message interface for the selected message operator. The message interface includes the caller and call context and any messages sent between the caller and the selected message operator, with an input interface allowing the selected message operator to input and send messages to the caller.

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.

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.

The embodiment of the present disclosure provides a method for smart gas call center feedback management and an Internet of things (IoT) system thereof. The method is implemented based on a smart gas management platform. The method includes: receiving a call message of a target customer through a call center, and a content of the call message being related to a gas business; determining a feedback mode by analyzing the call message through the call center; in response to the feedback mode being manual feedback, determining a target operator through the call center to feed back a call of the target customer; and in response to the feedback mode being automatic feedback, determining a feedback content through the call center and sending the feedback content to the target customer.

A method, system and program product, the method comprising: obtaining for each call in one set of calls a respective pattern representing multiple different respective data fields; obtaining performance data for the respective patterns of the calls; obtaining performance data for the respective agents; determining agent performance sensitivity to call pattern performance for agents in a set of agents comprising the agent performance data correlated to call performance data for the calls the agent handles; and matching a respective one of the agents from the set of agents to one of the calls based at least in part on the performance data for the respective pattern of the one call and on the agent sensitivity to call performance for the respective one agent of the set of agents.

Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for pairing in a contact center including ordering one or more contacts, ordering one or more agents, comparing a first difference in ordering between a first contact and a first agent in a first pair with a second difference in ordering between a second contact and a second agent in a second pair, and selecting the first pair or the second pair for connection based on the comparing, wherein the first contact and the second contact are different or the first agent and the second agent are different.

The present disclosure provides, among other things, methods and systems of managing a first channel, including: receiving a request for a communication session on the first channel; determining that a monitored attribute of the communication session has met a first threshold; comparing, by a channel change analysis, a first performance measure of the first channel with a second performance measure of a second channel; and managing a channel change based on the determining and the channel change analysis.

A system and method for monitoring behavior of voice agents in a simulated environment of voice-based call center to route a call. It includes a set of models and wearable devices to estimate and analyze cognitive load and emotional state of a voice agent which are obtained using wearable devices in the real time. It collects physiological signals from the voice agents and analyze them along with skill-set profiles of the voice agent to identify best suited voice agent based on agent-customer matching score obtained using skill-set profile analysis, cognitive load and a predicted emotive state of the voice agent. It may assist the voice agent in call if the cognitive load of the voice agent raises beyond predefined threshold using brain computer interfacing.

A computer retrieves a dialog information records of the active call of the first operator. The computer extracts features from the dialog information records. The computer determines a feature vector from the extracted features and determines a transfer probability value based on the feature vector and previous call transfers to the second operator.

A method, a computer program product, and a computer system determine when to transfer a communication session from an agent to a bot. The method includes monitoring the communication session between the agent and a user. The method includes determining a continuing utility value indicating a predicted continuing cost to maintaining the communication session with the agent. The continuing utility value is indicative of a predicted continuing benefit to maintaining the communication with the agent. The method includes determining a transferring utility value indicating a predicted transferring cost to transferring the communication session from the agent to the bot. The transferring utility value is indicative of a predicted transferring benefit to transferring the communication session from the agent to the bot. The method includes, as a result of the predicted transferring benefit being greater than the predicted continuing benefit, transferring the communication session from the agent to the bot.