United Services Automobile Association (Usaa)

Lambros Petropoulos

Bipin Chadha

John Michael Lombard

Michael P Gout

James Walter Wisnowski

Andrew Thomas Karl

Atesmachew Bizuwerk Hailegiorgis

Systems, methods, and computer program products provide a digital twin of a call center or call center network, including the members, representatives, and algorithms therein. The digital twin can be used to model real-world call centers and call center networks or may be modified to test changes (in the center/network's control such as algorithms or representative staffing, or beyond the center network's controls such as callers) before they are implemented in real-world production environments. Call loads, which may be based on real call loads or generated differently, can be used to test current or contemplated call center/network arrangements. The digital twin can be validated through comparison with data received from the real-world call center(s)/network(s) it models.

High fidelity call center simulator

Systems and methods are provided for modeling call center networks. Models are employed to run a simulation of the call center network based on call center network architecture data. The models are used to generate solution parameters for a call center network, and the solution parameters can be automatically implemented in at least a portion of the call center network.

Call center load balancing and routing management

Systems and methods simulate call centers networks and call loads to test load balancing and routing. The simulation can be used for generating, using a load balancer, a call score for the one or more calls based on the call information and selecting, using the load balancer, one of the simulated call centers as a selected call center based on the call score and the respective response entity profile.

Systems and methods for simulating multiple call center balancing

Systems and methods receiving call center network architecture data associated with a call center network; utilizing the call center network architecture data to create a model of the call center network; employing the model to run a simulation of the call center network that generates performance data associated with the model; using the model to generate solution parameters for the call center network; and providing the solution parameters to the call center network implementation in the call center network.

Systems and methods simulate call centers networks and call loads to test load balancing and routing. Performance can be logged to memorialize the load balancing and routing techniques' handling of different loads. This allows the testing of new algorithms or architectures and stress testing of existing architectures in non-production environments. Load balancing can be based on a call score, and routing can invoke proprietary routing protocols.

Systems and methods receiving performance data associated with a call center network; utilizing the performance data to create a model of the call center network; employing the model to run a simulation of the call center network that generates performance data associated with the model; using the model to generate solution parameters for the call center network; and providing the solution parameters to the call center network implementation in the call center network.

Techniques are described for forecasting and dynamic routing of incoming routing service requests in a service environment that includes one or more call centers. Implementations apply a doubly stochastic modeling technique to modeling call volumes, call wait times, calling handling times, and/or other parameters in a service environment that includes one or more call centers that include multiple skill codes for the service representatives. By simultaneously modeling on different time scales, such as modeling both inter-day and intra-day correlations, implementations are able to provide an analysis that exploits more of the structure in the data compared to traditional techniques. Implementations can also integrate in other types of time series for other effects that may impact the call volume or other parameters in a service environment, such as exogenous and/or anomalous variables that are independent of other inter-day and/or intra-day trends exhibited in the data.

Forecasting and dynamic routing for service environments

Systems and methods balance loads of traffic directed to various response entities. Systems and methods alternatively or complementarily generate simulated loads of traffic for handling by various response entities or load balancing algorithms.

Systems and methods solve functions relating to load balancing, call routing, and costs in call center networks for specific parameters. Systems and methods can also utilize machine learning to provide specific parameters relating to load balancing, call routing, and costs in call center networks.

The method may include simulating a call and determining a propensity score associated with the call. The method may also include selecting a first destination, from a plurality of destinations, for the call based on a routing algorithm and the propensity score. The method may include routing the call to the first destination and updating a first call load associated with the first destination based on the call. The method may also include comparing the first call load to a plurality of other call loads. Each of the other call loads may be associated with at least one of the plurality of destinations. The method may also include updating the routing algorithm based on the comparing.

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San Antonio, TX, United States of America

Lambros Petropoulos