In order to provide effective communications, the individuals engaged in the communication should have appropriately matched language proficiencies. By ensuring that a customer of a contact center is matched with an agent having, or presenting, content of a communication utilizing language proficiency appropriate for the customer, effective communications may be provided. Should an agent deviate and provide communication content having language proficiency that is misaligned with the customer, automatic corrective action may be taken to realign the language proficiency presented to the customer.

Method starts with processing, by a processor, audio signal to generate audio caller utterance. Processor generates an agent action ranking score associated with the audio caller utterance and determines whether the agent action ranking score is below a minimum threshold. In response to determining that the agent action ranking score is below the minimum threshold, processor generates a transcribed caller utterance using a speech-to-text processor and generates an identified task based on the transcribed caller utterance. Using the transcribed caller utterance and a task-specific agent coaching neural network associated with the identified task, processor generates an ideal response. Processor generates a feedback result and causes the feedback result to be displayed on a display device of the agent client device. Other embodiments are disclosed herein.

A contact center system uses user profiles and agent profiles to determine a routing for a contact center engagement request from a user device to an agent device. Responsive to the contact center engagement request, a user profile associated with the user device is accessed to determine a prioritization score. The prioritization score can be determined in some cases using both information associated with the user profile and input obtained from the user device. Agent profiles are accessed to determine an agent to support the contact center engagement request based on the prioritization score. Each of the agent profiles corresponds to a different contact center agent or agent group. The determined agent may be selected from amongst a plurality of candidates identified based on the profile evaluation. A private session is then established between the user device and a device of the agent.

A method and system for management of multi-party communications that is capable of intelligently assessing a first party/requestor's need and automatedly matching the first party's need to a suitable second party resource that is most probable to meet that need in an efficient manner. The system effectively serves as a chatbot-based (text-based or voice-based) switchboard operator that allows requestors to state a need for a service or consultation, and then automatedly have that need researched and/or matched to the most appropriate resource/service provider based on real-time automated inquiry and/or known data from past research. Further, the system is capable of contacting one or more resources/service providers, confirming that a particular resource/provider can meet the current need, and also coordinating/facilitating a communication session between the first party/requestor and the second party/resource without the need for human intervention.

A system and method for an optimized, self-learning and self-organizing contact center has been developed. This system and method uses principles and tools of information theory, including the latent Dirichlet allocation which reduces information to specific predetermined topics and a distribution of topic related words to infer its hidden, generative underpinnings so to self-organize a contact center, infer its desired electronic versus human make up, and optimally route all customer requests to an electronic resource or a specific human agent best suited to respond to the request for maximal business value per interaction.

A method and system matching experts or potential experts with an agent request for an expert are disclosed. Exemplary systems include an expert term extraction engine, an agent term extraction engine, and a matching engine to compare agent request terms to the expert terms from the expert term extraction engine and to determine whether there is a match or a potential match between the agent request terms and the extracted expert terms.

A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.

Techniques are described for routing service requests. Actions performed by an individual in an application are detected and used to determine a category of product or service that the individual is interested in. An activity record can be stored in an activity data store, the activity record identifying the individual and the category of interest determined for the individual. On subsequently receiving a service request from the individual, to an environment such as a call center, the activity record can be accessed and used to predict the individual's service need as corresponding to the category determined based on their previous actions. The service request is then automatically routed to a service representative who is suited to handle requests in that particular category. In some implementations, the category can be predicted and/or inferred based on the detected user activities in particular portions of the application.

Disclosed are systems and methods for routing inbound communications at call centers in order to enhance customer experience and call center agent efficiency. In order to effectively route communications, customer service call types and one or more agent performance rates responding to said customer service calls are analyzed in order to determine which one or more call center agents should be routed which specific customer service call type. Additionally, tools are disclosed for analyzing call center agent performance rates and assigning call center agents to service specific customer service call types.

Implementations of the present specification provide a telecommunication traffic allocation method and apparatus, and an electronic device. The method includes: determining a customer service agent that meets a response need of a traffic request, including: in response to that the traffic request is an audio interaction request or a video interaction request and that a customer service agent is currently processing an audio interaction or a video interaction, determining that the customer service agent does not meet the response need of the traffic request; in response to that the customer service agent is currently processing neither an audio interaction nor a video interaction, and that a processing volume corresponding to a text interaction being processed by the customer service agent does not exceed a first processing volume upper limit, determining that the customer service agent meets the response need of the traffic request; and allocating telecommunication traffic corresponding to the traffic request to a customer service agent that meets the response need. In the method according to an implementation of the present specification, the unified allocation of audio interactions, video interactions, and text interactions can be implemented, thereby improving the efficiency of customer service traffic processing, reducing the amount of customer service human resources occupied by a remote customer service, and reducing operating costs of the remote customer service.