Sap Se

Terence Chesire

Prerna Makanawala

Venkitesh Subramanian

Rei Kasai

Devasena Rajamohan

Claus Wallacher

Ryan Currier

Krithika Manohar

Janani Bhuvaneswari Sundar

Jaideep Godara

Jothish Karunakaran

Gabriele Bodda

Amith Manoharan Chithambaram

Kiran Karadi

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.

Predicting the severity of an active support ticket

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.

Systems and methods for customer relationship management

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Palo Alto, CA, United States of America

Terence Chesire