Microsoft Technology Licensing, LLC

Tejas Pravin Mehta

Dieter P Jansen

Janet Longhurst

Wenvi Hidayat

Sooraj Ashutosh Purandare

Mary Ellen Arndt

John Lincoln DeMaris

Maya Angele Pelichet

Chase Aaron Hignight

Junle Li

Anshuman Gaur

Nancy Handa

Mathew Alexander Mooty

Christina Ray

John L Demaris

Paula Wing

A data processing system implements providing multiple targeted intranet sites and/or employee experiences within a tenant of a multitenant computing environment. The administrators of a tenant are provided with tools to create multiple separate instances of the intranet site and/or employee experiences. The individual instances of the intranet site and/or employee experiences are separate and utilize a layout and include components that are appropriate for the particular audience for which the instance of the intranet site or employee experience has been tailored. The administrator can define affinity information that indicates which users or groups of users should be directed to a particular intranet site and/or employee experience. The administrator can define an order in which the intranet sites and/or employee experiences are considered when determining which intranet site or employee experience to present to a user in response to a request to access the intranet site or the employee experience.

Multiple targeted intranet and employee engagement experiences within a tenant

Techniques are described herein that are capable of intelligently updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Intelligently updating a collaboration site or template

Techniques are described herein that are capable of iteratively updating a collaboration site or a template that may be used to create a new collaboration site. The collaboration site or the template may be updated to include new features based on (e.g., based at least in part on) a likelihood that the new features will be valuable to users. The likelihood that new features will be valuable to the users may be determined (e.g., derived) using heuristics, machine learning, intelligent user experiences, and/or an understanding of user behavior gathered by a service that provides the collaboration site or the template. The likelihood may be compared to a likelihood threshold to determine whether the collaboration site or the template is to be updated. In accordance with this example, the update may be made if the likelihood is greater than or equal to the likelihood threshold.

Iteratively updating a collaboration site or template

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Sammamish, WA, United States of America

Tejas Pravin Mehta