Salesforce Com, Inc.

Salesforce, Inc.

David Yourdon

Kexin Xie

Nathan Irace Burke

Wanderley Liu

Xingyu Wang

Yacov Salomon

Shrestha Basu Mallick

Brian Brechbuhl

Michael Jones

Sheng Loong Su

Trent Albright

Jonathan Budd

Gloriose Hitimana

Zhao Jin

Todd Swardenski

Armita Peymandoust

Methods, systems, and devices for displaying a user interface for frequent pattern (FP) analysis are described. In some cases, data stored at a multi-tenant database server may be analyzed to understand various interactions and patterns between data attributes associated with multiple users, or determine one or more attributes associated with a characterization of an individual (e.g., a persona). The multi-tenant database server may effectively cluster and/or perform calculations on attributes of the data to understand user patterns and determine common personas. The results may then be displayed by a user interface at a user device (e.g., associated with the user).

User interface for frequent pattern analysis

A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

Attribute diversity for frequent pattern analysis

A database server may perform reach potential analysis for a local segment, or a target audience, of a data set. The local segment may include user devices which share a specific, common attribute. The database server may calculate similarities and correlations between a first data set for a user and a second data set from a data provider. The database server may calculate a reach index using the second data set from the data provider to determine whether user devices are likely to join the local segment by taking on the specific attribute which defines the local segment. Using the data set from the data provider, the database server may determine a reach potential within the first data set, outside of the first data set, or both.

Correlating user device attribute groups

A database server may receive or monitor user engagement metadata corresponding to a plurality of communication messages transmitted to the users. The database server analyzes the metadata to determine optimal transmission frequencies for digital communication messages based on engagement rates received in the user engagement metadata.

Communication frequency optimization

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Durham, NC, United States of America

David Yourdon