A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.

Data analysis, filter, and presentation techniques are described for an example call management system. An example method for a data management system includes receiving, from a user device, an account identifier of a first person, receiving, from the user device, a first message related to a topic of conversation to be discussed during a telephone call with the first person, determining, based on the account identifier and within a pre-determined time period, a presence of a set of data that describes conversation(s) from prior telephone call(s)/chat(s) with the first person, and sending, to a computer, at least some data from the set of data and a second message that indicates that the telephone call is mapped to a second person, where the computer is configured to display on a screen the second message and a presentation option that presents the at least some data via the computer.

Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, a plurality of contacts available for connection to an agent; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the agent and the plurality of contacts; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings according to a probabilistic network flow model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for connection in the contact center system.

A method and system for operating a multitenancy telephony system including receiving a plurality of call requests associated with an application server; selecting a subset of the received call requests using a load balancer; assigning each selected call request to one of a plurality of resources creating, on the assigned resource, a call session for each selected call request; and coordinating the created call sessions resulting from the selected subset of the received call requests associated with the application server.

A method and system for operating a multitenancy telephony system including receiving a plurality of call requests associated with an application server; selecting a subset of the received call requests using a load balancer; assigning each selected call request to one of a plurality of resources creating, on the assigned resource, a call session for each selected call request; and coordinating the created call sessions resulting from the selected subset of the received call requests associated with the application server.

A method includes receiving, at one or more processors, data indicative of a customer communication, inputting, via the one or more processors, the data to a first communication routing model corresponding to a first product or service goal associated with one or more product or service types, and determining, via the one or more processors and based on the first communication routing model, a first score corresponding to a first likelihood that the customer communication will satisfy the first product or service goal. The method also includes inputting, via the one or more processors, the data to a second communication routing model different than the first communication routing model and corresponding to a second product or service goal associated with the one or more product or service types, and determining, via the one or more processors and based on the second communication routing model, a second score corresponding to a second likelihood that the customer communication will satisfy the second product or service goal. The method also includes routing, via the one or more processors, the customer communication to a member service representative group based on a comparison of the first score with the second score or a first weighted score derived from the first score with a second weighted score derived from the second score.

Techniques for benchmarking pairing strategies in a contact center system are disclosed. In one embodiment, the techniques may be realized as a method for benchmarking pairing strategies in a contact center system comprising: determining for each contact of a plurality of contacts, an associated plurality of historical contact assignments; determining, for each contact, an associated outcome value; partitioning, for each contact, the associated plurality of historical assignments into a first associated subset assigned using a first pairing strategy and a second associated subset assigned using a second pairing strategy; determining, for each contact, a first portion of the associated outcome value attributable to the first associated subset and a second portion of the associated outcome value attributable to the second associated subset; outputting a difference in performance between the first and second pairing strategies according to the first and second associated portions of the associated outcome value for each contact.

We disclose a concierge device that can be configured to register, control and support a consumer device. It can alternatively or redundantly connect to a home management bridge and/or cloud-based management servers. It can accept menus that allow a single concierge device to provide a wide range of functions for various consumer devices. The concierge device allows the user in a single action to initiate a support session, automatically identifying the consumer device. The concierge device can be configured for voice or video support calls. The concierge device in conjunction with a home management bridge or gateway can manage on boarding of components of an automated home, such as switches and lamps. Implementations of the concierge device that include a display can show supplemental information, such as advertising, optionally in coordination with media being played on a consumer device coupled in communication with the concierge device.

A system for transitioning a telephony or in-person servicing to an artificial intelligence (AI) chat session. The system may receive a phone call from a user device associated with a user, and transmit a voice request for personally identifiable information associated with the user. The system may also receive and authenticate the requested personally identifiable information and, in response, generate an authentication token. The system may further receive a servicing intent from the user device, and generate a corresponding servicing intent token. Also, the system may generate an API call to an AI chatbot model, transmit the authentication token and the servicing intent token to the AI chatbot model, and map the servicing intent token to a stored servicing intent. Finally, the system may transmit a message to the user device via the AI chat session.

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.