Abstract: An issue tracking system capable of predicting the likelihood that an outcome of interest will occur during the lifecycle of an active support ticket. The likelihood can be represented as a severity index score. The issue tracking system can apply a predictive algorithm on attributes of the active support ticket to generate the severity index score. The predictive algorithm to use can depend on the outcome of interest while the correlation factors used to configure the predictive algorithm can depend on support tickets that have already been completed.

Abstract: According to various embodiments, a social media message posted on a social media system is accessed. One or more similar messages determined to be similar to the social media message may be selected from a database of previously posted messages, based on, for example, an author, product, problem, keyword, etc., identified in the social media message. The similar messages may be displayed in a similar message recommendation list of a user interface. One or more relevant knowledgebase articles determined to be relevant to the social media message may be selected from a knowledgebase repository, based on, for example, a product, problem, keyword, etc., identified in the social media message. The relevant knowledgebase articles may be displayed in a knowledgebase article list of a user interface.

Abstract: An issue tracking system capable of predicting the likelihood that an outcome of interest will occur during the lifecycle of an active support ticket. The likelihood can be represented as a severity index score. The issue tracking system can apply a predictive algorithm on attributes of the active support ticket to generate the severity index score. A graphics engine can present each active support ticket as a line positioned along the perimeter of an inner circle where the length of the line represents the severity index score of the corresponding active support ticket. In some examples, the lines can extend towards an outer circle that represents a predefined threshold value for the severity index score.

Abstract: A recommendation engine analyzes metrics on an active support ticket to provide recommended solutions, technicians and offers. The recommendation can communicate with a predictive analysis engine to identify solutions, technicians, or offers that are highly correlated with the input parameters. In some embodiments, instructions can be provided to a client device for measuring a metric that is used as an input parameter of the predictive analysis engine. The customer or the technician can follow the instructions to measure the metric.

Abstract: An issue tracking system capable of predicting the likelihood that an outcome of interest will occur during the lifecycle of an active support ticket. The likelihood can be represented as a severity index score. The issue tracking system can apply a predictive algorithm on attributes of the active support ticket to generate the severity index score. The predictive algorithm to use can depend on the outcome of interest while the correlation factors used to configure the predictive algorithm can depend on support tickets that have already been completed.

Abstract: According to various exemplary embodiments, a social media message posted on a social media system is accessed. Moreover, an agent listed in an agent directory is classified as an expert of content referred to in the social media message, based on a keyword identified in the social media message. Further, an identifier of the agent may be displayed in an expert finder list of a user interface.

Abstract: According to various embodiments, a social media message posted on a social media system is accessed. One or more similar messages determined to be similar to the social media message may be selected from a database of previously posted messages, based on, for example, an author, product, problem, keyword, etc., identified in the social media message. The similar messages may be displayed in a similar message recommendation list of a user interface. One or more relevant knowledgebase articles determined to be relevant to the social media message may be selected from a knowledgebase repository, based on, for example, a product, problem, keyword, etc., identified in the social media message. The relevant knowledgebase articles may be displayed in a knowledgebase article list of a user interface.

Abstract: According to various exemplary embodiments, a social media message posted by a user on a social media system is accessed, and a prioritization level is determined based on the social media message. The prioritization level may indicate a measurement of importance of the social media message. Further, the social media message may be inserted into a prioritized message queue, based on the determined prioritization level associated with the social media message.