Disclosed embodiments may include a method that includes receiving profile data associated with a user, the profile data comprising a first phone number, receiving browsing data from a user device associated with the user, storing the browsing data with a store time, receiving a phone call from the first phone number at a call time, identifying the user from the first phone number, retrieving the browsing data, determining whether the store time is within a time threshold of the call time and whether the browsing data comprises trigger data. When the browsing data comprises the trigger data and the store time is within the time threshold of the call time, generate a customized audio IVR menu based on the browsing data and present the customized audio IVR menu to the user over the phone call.

Aspects of the present disclosure relate to a communication router and hub. In examples, a communication platform enables communication between a customer and a service. Customer information associated with a customer is stored as part of a customer record. The customer record may be associated with the customer using a customer address (e.g., a telephone number or an email address) and/or a communication address associated with the service. Thus, in response to a subsequent communication, the communication platform may identify the customer record based at least in part on the customer address and the service communication address, thereby enabling improved communication routing for incoming and outgoing communications associated with the customer. Additionally, customer information from the customer record may be presented to an experience agent of the service, such that the experience agent is able to better assist the customer.

A method for routing civilian calls to an associated public-safety officer is provided herein. During operation, a call processor receives a civilian call having a target identifier number identifying a target of the call. An incident identifier is then received from the calling party. A current workflow point is determined for the identified incident, and the call is routed to an appropriate person based on the workflow point for the identified incident.

Methods, a system, and computer readable media are disclosed to provide an enhanced Communication Waiting applications service. Enhancements include a method for handling an incoming communication received before an outgoing communication is acknowledged and a method to handle a second incoming communications to a user device before the first incoming communication is acknowledged by the user. Such methods remove limitations on existing techniques by allowing users to select between pending and new incoming calls. In some embodiments, calls not selected by the users are placed on hold or routed to voicemail.

A computer-implemented method related to routing incoming interactions of contact centers. The method may include: receiving initial data identifying a first incoming interaction that includes information disclosing at least an intent of the first incoming interaction; and performing a first subprocess to generate a personalized routing profile tailored to facilitate routing the first incoming interaction in accordance with preferences of a first customer. The first subprocess may include: accessing data from a database, the database including at least a first customer profile storing data relating to the first customer; based on the accessed data and the intent of the first incoming interaction, determining preferred agent characteristics data of the first customer for the first incoming interaction; and generating the personalized routing profile so to include the preferred agent characteristics data of the first customer.

A system for dynamically routing customer calls. For example, the system may receive user interaction data associated with a first user using a first user device. The system may also receive a phone call from a user using a first phone number. The system may also identify the user via the first phone number. The system may determine, using a first machine learning model, whether the first user has a first emotion type based on the user interaction data. When the first user does not have the first emotion type, the system may route the first user to any call center representative. When the first user has the first emotion type, the system may route the first user to a first call center representative among one or more first call center representatives.

Systems and methods for intelligently routing a member of an organization to a single point-of-contact within an optimized, secure network to address all the member's healthcare needs are described. The disclosed intelligent routing configurations transform and process, in real-time, vast amounts of member data to generate aggregated diagnoses and a member score specific to each member's household. The scores, among other things, are used to determine an identification of special needs and an appropriate advocate within the organization to route the member, and its account file containing real-time member and household level data.

A system for selecting communication routes based on multiple criteria is disclosed. Disclosed in the present application are a number of systems and associated processes for allowing users to configure call routing systems to dynamically route calls by performing one or more of: ranking call destinations (e.g., vendors or trunk locations), adjusting or replacing route groups, queuing calls, activating interactive voice responses (IVRs), and re-purposing ports and trunks.

Techniques are described for generating metric(s) that predict survey score(s) for a service session. Model(s) may be trained, through supervised or unsupervised machine learning, using training data from previous service sessions between service representative(s) and individual(s). Training data may include, for previous service session(s), a session record (e.g., audio record) of the session and a set of survey scores provided by the serviced individual to rate the session on one or more criteria (e.g., survey questions). The model(s) may be trained to output, based on an input session record, metric(s) that each correspond to a survey score that would have been provided by the individual had they completed the survey. The model may be a concatenated model that is a combination of a language model output from a language classifier recurrent neural network, and an acoustic model output from an acoustic feature layer convolutional neural network.

Techniques are described for generating metric(s) that predict survey score(s) for a service session. Model(s) may be trained, through supervised or unsupervised machine learning, using training data such as communications from previous service sessions between service representative(s) and individual(s), and survey scores provided by the serviced individual to rate the session on one or more criteria (e.g., survey questions). The model(s) may be trained to output, based on an input session record, metric(s) that each correspond to a survey score that would have been provided by the individual had they completed the survey. The model may be a concatenated model that combines a language model output from a language classifier recurrent neural network, and an acoustic model output from an acoustic feature layer convolutional neural network. Individuals can be clustered according to the metric(s) and/or other factors, and the cluster(s) can be employed for routing incoming service requests.