Salesforce, Inc.

Salesforce Com, Inc.

Shrestha Basu Mallick

Kexin Xie

Yuxi Zhang

Gordon S Kino

Olav Solgaard

Onur Can Akkaya

Michel Jf Digonnet

Sheng Loong Su

Darrell Grissen

Onur Kilic

Yacov Salomon

Daniel Wilson

David Yourdon

Brian Brechbuhl

Matthew David Trepina

Michael Jones

Trent Albright

Gokhan Cagrici

Jonathan Daniel Showers Belkowitz

Zhao Jin

Jason Lestina

James Brewer

Daniel Louis Gasperut

Jeffery Allen Zickgraf

Todd Swardenski

Greg Lyman

Michael Ronald Brewer

Evan Black

Austin Rauschuber

Armita Peymandoust

Victoria Schultz

Peter Stadlinger

A system can recommend a next action for a user. A memory can store user data corresponding to the user and can include historic interaction points. A behavior pattern can be identified based on two or more interaction points stored in the user data. An intent of the user based on the behavior pattern can be identified. The intent can be based on a previous behavior pattern of another user. Several probabilities that the user will meet one or more objectives can be determined based on the intent. The probabilities can be scored using and used to assign a policy to the first user. A next action can be recommended based on the policy and executed with respect to the user. The outcome of the recommended next action can be stored to the user data.

Automatic rule generation for next-action recommendation engine

Methods, systems, and devices supporting data processing are described. In some systems, a data processing platform may support communication message analysis using machine learning. For example, a system may receive a set of communication messages (e.g., social media messages) and perform a machine learning process on the message contents and message interaction data to train a machine learned model. The system may further receive a subject line for a communication message for analysis, input the subject line into the machine learned model, and receive, as an output of the machine learned model, an engagement score based on the subject line. The engagement score may indicate an estimated probability that a user receiving the communication message opens the communication message (e.g., based on the subject line). A user&#x2014;or the system&#x2014;may modify the subject line based on the analysis to improve the engagement score.

Subject line tester

A cloud platform supports a digital communication system that identifies recommended communication frequencies based on past communication data. The cloud platform may support blending of weights applied to different engagement rates. Based on the weights, the system identifies recommended frequency ranges to maximize engagement rates, including the blended engagement rate using a redistribution simulation process.

Data processing for multi-objective communication engagement

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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San Francisco, CA, United States of America

Shrestha Basu Mallick