Accenture Global Services Gmbh

Linkedin Corporation

Junghoon Andrew Ahn

Anmol Bhasin

Abhishek Gupta

Bo Long

Christian Posse

Kun Liu

Jian H Li

Yurong Shi

Jacob Kessler

Venu Javarappa

Deepak Argawal

Systems and methods for reducing a churn rate associated with subscribers of social network services are described. In some example embodiments, the systems and methods may access activity information associated with a former subscriber of a social network service, compare the accessed activity information to activity information associated with subscribers of the social network service, identify one or more differences between the activity information associated with the former subscriber of the social network service and the activity information associated with the subscribers of the social network service, and perform an action based on the identified one or more differences.

Determining a churn probability for a subscriber of a social network service

To entities that are likely to engage in a behavior, a behavior discrimination threshold for each of a plurality of behavior variables is determined. Variable values for each entity of a plurality of target entities and for each of the behavior variables are compared to the relevant behavior discrimination threshold to provide identifying variables for each entity. An index value is determined for each of the plurality of target entities based on the identifying variables for that entity. An index threshold is determined such that those entities of the target entities having index values that meet or exceed the index threshold are likely to exhibit or engage in the behavior more frequently than a random sampling of the plurality of training entities. Actions designed to affect the likelihood that the resulting targeted entities will engage in the behavior may be directed to the targeted entities.

Identification of entities likely to engage in a behavior

Disclosed in some examples are methods, systems and machine readable medium for recommending an out-of-network communication by determining a set of potential recommended members of a social networking service based upon one or more recommendation criteria. In some examples the recommendation criteria may include: a profile similarity to a previous target of an out-of-network communication, a degree of correspondence between an interest and intent of the sending member, and a likelihood of response.

Suggested out of network communication recipients

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Cupertino, CA, United States of America

Junghoon Andrew Ahn