Abstract: Non-limiting examples of the present disclosure describe insight-based routing that is used to improve processing of a service request through a help desk service. A request for support (e.g. technical support) can be received through a modality of a help desk service. The request is evaluated, where an evaluation of the request comprises analyzing an issue associated with the request as well as user-specific signal data associated with a customer and generating insights. A support agent is matched to the customer based on an evaluation of the request. The support agent is selected from a pool of support agents based on application of a model that analyzes support agent data in correlation with the generated insights. An interaction between the matched support agent and the customer may be initiated through a modality of the help desk service.

Abstract: Non-limiting examples of the present disclosure describe insight-based routing that is used to improve processing during a communication of a help desk service. Real-time signal data is collected from a communication between a customer and a first support agent. The real-time signal data is evaluated to generate real-time insights for the communication. A determination is made as to whether the first needs assistance based on an evaluation of the real-time insights. A second support agent is identified to assist the first support agent based on a determination that the first support agent needs assistance. The second support agent is identified based on an application of a model that analyzes support agent data for a pool of support agents in correlation with the real-time insights. The second agent is then added to the communication to provide additional assistance in resolving a help desk case.

Abstract: Non-limiting examples of the present disclosure describe insight-based routing that is used to improve processing of a service request through a help desk service. A request for support (e.g. technical support) can be received through a modality of a help desk service. The request is evaluated, where an evaluation of the request comprises analyzing an issue associated with the request as well as user-specific signal data associated with a customer and generating insights. A support agent is matched to the customer based on an evaluation of the request. The support agent is selected from a pool of support agents based on application of a model that analyzes support agent data in correlation with the generated insights. An interaction between the matched support agent and the customer may be initiated through a modality of the help desk service.

Abstract: Non-limiting examples of the present disclosure describe insight-based routing that is used to improve processing during a communication of a help desk service. Real-time signal data is collected from a communication between a customer and a first support agent. The real-time signal data is evaluated to generate real-time insights for the communication. A determination is made as to whether the first needs assistance based on an evaluation of the real-time insights. A second support agent is identified to assist the first support agent based on a determination that the first support agent needs assistance. The second support agent is identified based on an application of a model that analyzes support agent data for a pool of support agents in correlation with the real-time insights. The second agent is then added to the communication to provide additional assistance in resolving a help desk case.

Abstract: Non-limiting examples of the present disclosure describe insight-based routing that is used to improve processing of a service request through a help desk service. A request for support (e.g. technical support) can be received through a modality of a help desk service. The request is evaluated, where an evaluation of the request comprises analyzing an issue associated with the request as well as user-specific signal data associated with a customer and generating insights. A support agent is matched to the customer based on an evaluation of the request. The support agent is selected from a pool of support agents based on application of a model that analyzes support agent data in correlation with the generated insights. An interaction between the matched support agent and the customer may be initiated through a modality of the help desk service.

Abstract: Non-limiting examples of the present disclosure describe insight-based routing that is used to improve processing during a communication of a help desk service. Real-time signal data is collected from a communication between a customer and a first support agent. The real-time signal data is evaluated to generate real-time insights for the communication. A determination is made as to whether the first needs assistance based on an evaluation of the real-time insights. A second support agent is identified to assist the first support agent based on a determination that the first support agent needs assistance. The second support agent is identified based on an application of a model that analyzes support agent data for a pool of support agents in correlation with the real-time insights. The second agent is then added to the communication to provide additional assistance in resolving a help desk case.

Abstract: Non-limiting examples of the present disclosure describe insight-based routing that is used to improve transparency and communication between customers and support agents of a help desk service. An unresolved case may be identified through a help desk service. Case details associated with the unresolved case may be evaluated. In examples, an evaluation of the case details may comprise analyzing an issue that requires resolution, detecting a case state that corresponds with one or more actions taken to resolve the issue of the unresolved case and detecting, through the help desk service, user presence information indicating availability of a customer associated with the unresolved case. A follow-up inquiry may be generated based on the detected case state. The follow-up inquiry may be automatically transmitted to the customer based on an evaluation of the user presence information indicating that the customer is available.

Abstract: Non-limiting examples of the present disclosure describe insight-based routing that is used to improve processing during a communication of a help desk service. Real-time signal data is collected from a communication between a customer and a first support agent. The real-time signal data is evaluated to generate real-time insights for the communication. A determination is made as to whether the first needs assistance based on an evaluation of the real-time insights. A second support agent is identified to assist the first support agent based on a determination that the first support agent needs assistance. The second support agent is identified based on an application of a model that analyzes support agent data for a pool of support agents in correlation with the real-time insights. The second agent is then added to the communication to provide additional assistance in resolving a help desk case.

Abstract: Non-limiting examples of the present disclosure describe insight-based routing that is used to improve processing of a service request through a help desk service. A request for support (e.g. technical support) can be received through a modality of a help desk service. The request is evaluated, where an evaluation of the request comprises analyzing an issue associated with the request as well as user-specific signal data associated with a customer and generating insights. A support agent is matched to the customer based on an evaluation of the request. The support agent is selected from a pool of support agents based on application of a model that analyzes support agent data in correlation with the generated insights. An interaction between the matched support agent and the customer may be initiated through a modality of the help desk service.

Abstract: Connectivity is tested using different locations and contexts selected from different possible failure zones. The failure zones may include: client failure zones; Internet failure zones; and online service failure zones. The results relating to different connectivity tests performed using the different failure zones are correlated and analyzed in an attempt to determine a root cause of the connectivity issue. For example, the root cause may be determined to be a configuration problem of the client, a problem with the client's networking equipment, an ISP problem, an Internet backbone problem; a problem of the online service, and the like. Different contexts may also be used when performing the tests. These results may be compared to the other connectivity test results. The results from the tests may be provided to the client experiencing the problem. Aggregated test results may also be used to detect service wide issues and trigger an alert.

Abstract: Concepts and technologies are described herein for providing contextually-aware discovery of solutions. In some configurations, a computing device may receive a request from a tenant. A request may be any type of request, including a service request or any other type of request for information. To process the request, the computing device may also obtain data that associates characteristics with the tenant. For example, one or more resources may maintain a database that includes a tenant identifier, data indicating the size of the tenant, the length of time a tenant has been in service, or any other type of characteristic of the tenant.

Abstract: Connectivity is tested using different locations and contexts selected from different possible failure zones. The failure zones may include: client failure zones; Internet failure zones; and online service failure zones. The results relating to different connectivity tests performed using the different failure zones are correlated and analyzed in an attempt to determine a root cause of the connectivity issue. For example, the root cause may be determined to be a configuration problem of the client, a problem with the client's networking equipment, an ISP problem, an Internet backbone problem; a problem of the online service, and the like. Different contexts may also be used when performing the tests. These results may be compared to the other connectivity test results. The results from the tests may be provided to the client experiencing the problem. Aggregated test results may also be used to detect service wide issues and trigger an alert.