Techniques for data matching in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for data matching 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, an interaction event time associated with a historical contact interaction; determining, by the at least one computer processor, an outcome event time associated with a historical contact interaction outcome; analyzing, by the at least one computer processor, the interaction event time and the outcome event time to determine a correlation; and matching, by the at least one computer processor, the historical contact interaction with the historical contact interaction outcome based on the correlation.

Disclosed are systems and methods for robocall steering over voice-hosted traffic networks. The disclosed framework provides novel systems and methods for increasing the accuracy and efficiency in tracking, identifying, blocking and preventing robocalls and robocallers. The disclosed systems and methods provide mechanisms for identifying and removing unwanted voice traffic from networks. The disclosed systems and methods analyze voice traffic over a predetermined period of time (e.g., 1 day or 30 days, for example), and leverage this information into a “know your customer” (KYC) score. This score enables incoming calls to be routed, controlled and/or blocked as they are communicated over voice networks.

An example method of operation may include one or more of identifying an inbound call intended for a mobile device subscribed to a protected carrier network, determining the inbound call is assigned an origination telephone number that is subscribed to the protected carrier network, determining whether an inbound call origination source location indicates the protected carrier network or an out-of-network carrier network based on one or more call parameters received with the inbound call, and determining whether to transmit an indication to the mobile device that the inbound call has an elevated likelihood of being a scam call based on the inbound call origination source location.

One example method of operation may include identifying one or more call parameters associated with each of a number of calls received over a fixed period of time, assigning scores to each of the calls based on the one or more identified call parameters for each of the plurality of calls, assigning one or more of the calls to a scam call category based on the assigned scores, and responsive to the assigning of the one or more of the calls to a scam call category, determining whether a number of remaining calls of the calls, which are not assigned to the scam call category, have increased or decreased beyond a deviation margin of a target percentage of calls.

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.

Call propensity source data may be received that include a first percentage of call propensity source data that correspond to presence of post-visit phone calls to a customer service of an entity after some customer visits to a web site of an entity and a second percentage of call propensity source data that correspond to absence of post-visit phone calls to the customer service after other customer visits to the website. A machine-learning model is trained based on a plurality of features in at least a portion of the call propensity source data to generate a trained machine-learning model. The trained machine-learning model is applied to multiple features included in at least one of corresponding website activity data and corresponding activity error data of a customer to generate a probability score that measures a likelihood of the customer calling the customer service regarding an issue that is unresolved via the website.

One disclosed method includes filtering incoming emergency data using at least one filtering criteria, as the emergency data is received in response to initiation of emergency events; generating at least one data bin representing a tally of emergency data meeting the at least one filtering criteria, without storing any of the emergency data used to generate the at least one data bin; and generating a statistical report using the at least one data bin. The statistical report may be generated real-time and displayed on a remote display.

In the field of telecommunications, methods, systems, and tangible, non-transitory computer-readable mediums comprising program code are disclosed that comprise receiving a first agent, a second agent, a third agent, and a fourth agent available for pairing to a contact; and selecting the first agent for pairing to the contact based on a pairing strategy, wherein the pairing strategy is configured such that if the third agent and fourth agent had not been available, the second agent would have been selected for pairing to the contact, wherein the pairing strategy is configured such that if the first agent had not been available, the third agent would have been selected for pairing to the contact.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a spoofed telephone call identifier are disclosed. In one aspect, a method includes the actions of receiving, by a first computing device, data indicating a placement of a telephone call from a second computing device to a third computing device, wherein the data includes a phone number of the second computing device. The actions further include determining characteristics of the phone number of the second computing device. The actions further include, based on the characteristics of the phone number of the second computing device, determining a likelihood that the phone number of the second computing device is spoofed. The actions further include, based on the likelihood that the phone number of the second computing device is spoofed, determining whether to transmit a notification of the telephone call to the third computing device.

Embodiments described herein provide for performing a risk assessment using graph-derived features of a user interaction. A computer receives interaction information and infers information from the interaction based on information provided to the computer by a communication channel used in transmitting the interaction information. The computer may determine a claimed identity of the user associated with the user interaction. The computer may extract features from the inferred identity and claimed identity. The computer generates a graph representing the structural relationship between the communication channels and claimed identities associated with the inferred identity and claimed identity. The computer may extract additional features from the inferred identity and claimed identity using the graph. The computer may apply the features to a machine learning model to generate a risk score indicating the probability of a fraudulent interaction associated with the user interaction.