Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Abstract: A method, non-transitory computer readable medium, apparatus, and system for content distribution are described. An embodiment of the present disclosure includes receiving, by a machine learning model, a prompt. The machine learning model generates a campaign brief based on the prompt. The campaign brief includes an identification of a user segment, an identification of a communication channel, and a content element. The machine learning model is trained using training data including a plurality of campaign briefs. A user experience platform provides content corresponding to the content element to a user from the user segment via the communication channel based on the campaign brief.

Abstract: A method, non-transitory computer readable medium, apparatus, and system for content distribution are described. An embodiment of the present disclosure includes obtaining, by a user experience platform, a prompt describing an element of a content distribution campaign. A machine learning model generates a user journey based on the prompt. The user journey includes at least one touchpoint for the content distribution campaign. The user experience platform provides digital content to a user corresponding to the at least one touchpoint based on the user journey.

Abstract: A method, non-transitory computer readable medium, apparatus, and system for data clustering are described. An embodiment of the present disclosure includes receiving, by a machine learning model, a prompt. The machine learning model generates a structured query for a database of users based on the prompt. A user experience platform generates a user segment of the users based on the structured query.

Abstract: A method, non-transitory computer readable medium, apparatus, and system for contextual content generation are described. An embodiment of the present disclosure includes obtaining, by a user experience platform, a content provider context for the user experience platform. The content provider context includes profile information for a content provider and an interaction history of the content provider. The user experience platform obtains a prompt based on the content provider context. Embodiments of the present disclosure further include generating content for a project within the user experience platform based on the prompt using a machine learning model.

Abstract: A method, non-transitory computer readable medium, apparatus, and system for data processing are described. An embodiment of the present disclosure includes receiving, from a content provider via a user interface, a query about a chart that includes information related to a domain. A machine learning model generates a response to the query based on the chart and a corpus of documents in the domain. The response includes information from the corpus of documents. The user interface provides at least a portion of the response to the content provider.

Abstract: A method, non-transitory computer readable medium, apparatus, and system for content generation are described. An embodiment of the present disclosure includes identifying, by a user experience platform, a content distribution campaign. The user experience platform obtains feedback for the content distribution campaign. Embodiments of the present disclosure further include generating content for a modified content distribution campaign based on the feedback using a machine learning model.

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Abstract: An ensemble model is described that is usable to predict revenue metrics for one or more keywords. The ensemble model may be formed using both a historical model and a user behavior model. In one or more implementations, weights are assigned to the historical model and/or the user behavior model based on one or more criteria. Various processing techniques of the ensemble model may utilize the historical model and the user behavior model to predict revenue metrics for one or more keywords.