Abstract: According to an example, connections may be routed in a contact center by determining if a current connection from a customer terminal to the contact center is a repeat connection or a first connection. In response to determining that the connection is a repeat connection, the repeat connection is routed to an originating agent terminal when an originating agent is verified as employed, logged-in, and available. In response to determining that the connection is a first connection, the first connection is routed to a best available agent terminal.

Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.

Abstract: According to an example, connections may be routed in a contact center by determining if a current connection from a customer terminal to the contact center is a repeat connection or a first connection. In response to determining that the connection is a repeat connection, the repeat connection is routed to an originating agent terminal when an originating agent is verified as employed, logged-in, and available. In response to determining that the connection is a first connection, the first connection is routed to a best available agent terminal.

Abstract: According to an example, connections may be routed in a contact center by determining if a current connection from a customer terminal to the contact center is a repeat connection or a first connection. In response to determining that the connection is a repeat connection, the repeat connection is routed to an originating agent terminal when an originating agent is verified as employed, logged-in, and available. In response to determining that the connection is a first connection, the first connection is routed to a best available agent terminal.

Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.

Abstract: According to an example, connections may be routed in a contact center by determining if a current connection from a customer terminal to the contact center is a repeat connection or a first connection. In response to determining that the connection is a repeat connection, the repeat connection is routed to an originating agent terminal when an originating agent is verified as employed, logged-in, and available. In response to determining that the connection is a first connection, the first connection is routed to a best available agent terminal.

Abstract: A churn analysis system helps a business analyze, predict, and reduce customer churn. The system analyzes customer experiences by using an insightful block level approach to correlate customer experience with customer churn. Through the block level approach, the system is able to more accurately predict and effectively reduce future customer churn. As a result, businesses are able to reduce customer acquisition costs and improve customer retention rates.

Abstract: A churn analysis system helps a business analyze, predict, and reduce customer churn. The system analyzes customer experiences by using an insightful block level approach to correlate customer experience with customer churn. Through the block level approach, the system is able to more accurately predict and effectively reduce future customer churn. As a result, businesses are able to reduce customer acquisition costs and improve customer retention rates.