Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a spam telephone call reducer are disclosed. In one aspect, a method includes the actions of receiving first telephone call data that reflects telephone calls received by a first user and second telephone call data that reflects telephone calls received by a second user. The actions further include comparing the first telephone call data and the second telephone call data. The actions further include determining that the first user received more spam telephone calls than the second user. The actions further include determining a first characteristic of the first user and a second characteristic of the second user. The actions further include determining an action that increases a similarity of the first characteristic to the second characteristic. The actions further include performing the action on the first characteristic.

Disclosed herein are various embodiments for a customer callback system. An embodiment operates by determining a call action indicating a customer to whom a representative is to speak. An identifier associated with the customer is determined. A plurality of computing activities corresponding to interactions between the computing device and one or more websites provided to the computing device over a defined period of time are retrieved. A pattern associated with when the plurality of computing activities corresponding to the interactions between the computing device and the one or more websites were performed is identified. The overlapping time of day is provided to the representative during which to perform the call action.

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.

A method for operating a device for handling a phone call is disclosed. In one aspect the method comprises (a) receiving a phone call from a calling party, (b) determining an action to be performed by the device among a predetermined list of actions related to the handling of a phone call, as a function at least of identity information about the calling party, and (c) performing the determined action for handling the phone call. The device may be programmable and mobile, e.g., a smartphone.

Systems and methods forecast inbound telecommunications, and more particularly, analyze real-time and historical call center data, and apply a forecasting model to the data in order to predict inbound call volume. These systems and methods employ tools that manipulate call center data and generate visual representations of metrics pertaining to forecasting call center data via a dashboard.

An analytics system to analyze and report customer experience, comprising a customer interface device, an analysis server which analyzes data from a customer and a machine, and the database which is connected to the customer interface device and to the analysis server wherein the database receives data from the customer interface device, and/or the analysis server. The analytics system analyzes and reports customer experience.

A system and method for configuring network pairings enabling end-to-end communications between electronic devices is disclosed. The system receives a network pairing request from a requesting device to communicate with a target device. The system identifies historic data associated with the communication object and determines whether an affinity group associated with the requesting device is found. Further, the system determines whether the communication object has a valid permission token associated therewith. If a valid permission token is unavailable, the system checks whether a subscriber filter criteria is met by the requesting device. If the filter criteria is met, system calculates a subscriber access score (SAS) for the requesting device and compares the score to a threshold. When the SAS is greater than the predetermined threshold value, system creates a dynamic network pairing between the requesting device and the target device.

Systems and methods for creating a network communication pathway between devices based on type of communication request are disclosed. The system obtains a communication object from a first device to enable communication with a second device. The first device may be a device within an affinity group of the second device, such that the system identifies historic data relating to the affinity group and/or relating to communication attempts from non-subscriber devices found in one or more affinity groups stored within the system. Based on the communication object type, the system identifies whether communication object relates to a question object previously received from the second device and calculates an Expert Attribute Score (EAS) for the first device. The system may evaluate the EAS against a predetermined threshold and transmit the communication object to the second device, if the EAS is greater than or equal to the predetermined threshold.

Methods, systems, and apparatus for prioritizing communications are described. Metadata that characterizes an electronic communication is obtained and a machine learning algorithm is applied to the metadata to generate a scoring model. A score for the electronic communication is generated based on the scoring model.

A call analytics system and associated methods that can be used to rapidly analyze call data and provide conversational insights. The call analytics system receives audio call data of a phone call between a customer and an agent of a business, and converts the call data into one or more messages for handling by a distributed stream-processing platform. In some embodiments, the stream-processing platform is the Apache Kafka platform. The distributed platform processes the messages and communicates with various software modules to generate a variety of conversational insights. When processed by a stream-processing platform, certain analyses can occur in parallel which allows conversational insights to be provided to the businesses shortly (e.g., within seconds) after the call data is received.