Adobe, Inc.

Thomas William Randall Jacobs

Kevin Gary Smith

Kent Andrew Edmonds

Jen-Chan Jeff Chien

Gavin Stuart Peter Miller

Peter Raymond Fransen

Oliver Isaac Goldman

Pradeep Saikalyanachakravarthi Javangula

Ashley Manning Still

Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Application digital content control using an embedded machine learning module

Techniques and systems are described to enable users to optimize a digital marketing content system by analyzing an effect of components of digital marketing content on audience segments, environments of consumption, and channels of consumption. A computing device of an analytics system receives user interaction data describing an effect of user interaction with multiple items of digital marketing content on achieving an action for multiple audience segments. The analytics system identifies which of a plurality of components are included in respective items of digital marketing content. The analytics system generates data identifying different aspects that likely had an effect on the achieving an action on the items of digital marketing content, such as components of the items of digital marketing content, environments of consumption, channels of consumption. The analytics system outputs a result based on the data in a user interface.

Digital media environment for analysis of audience segments in a digital marketing campaign

Digital content control based on shared machine learning properties

Techniques and systems are described for analyzing components of digital content. A computing device of an analytics system receives user interaction data that describes an effect of user interaction with a plurality of items of digital content on achieving an action. The analytics system identifies which of a plurality of components are included in respective items of digital content. The analytics system then generates outcome data describing a likely effect of the plurality of components on achieving the action based on association with respective items of digital content. Additionally, the analytics system generates a recommendation to configure a subsequent item of digital content based on the outcome data. The recommendation is based on the likely effect of the different ones of the plurality of components, to generate more effective digital content.

Digital media environment for analysis of components of digital content

Techniques and systems are described for analyzing components of digital marketing content as part of a digital marketing campaign. A computing device of an analytics system receives user interaction data that describes an effect of user interaction with a plurality of items of digital marketing content on achieving an action. The analytics system identifies which of a plurality of components are included in respective items of digital marketing content. The analytics system then generates outcome data describing a likely effect of the plurality of components on achieving the action based on association with respective items of digital marketing content. Additionally, the analytics system generates a recommendation to configure a subsequent item of digital marketing content based on the outcome data. The recommendation is based on the likely effect of the different ones of the plurality of components, to generate more effective digital marketing content items for digital marketing campaigns.

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Cupertino, CA, United States of America

Thomas William Randall Jacobs