Abstract: A method, system, and apparatus provide the ability to personalize a digital channel. A digital channel is provided to multiple users and visitor information at each visit is collected. The visitor information includes data about each visit and multiple content items that are presented. The users are autonomously clustered by segmenting the user population into behavioral groups such that mutual information is maximized between the users in an assigned behavioral group and the content items. Based on the clustering, a model is generated that estimates a score for each interaction between users and content items. The model is updated at a defined interval. Based on the score, content items to recommend to a specific user are determined. The recommendation jointly maximizes an outcome and a learning speed of the model. The personalized digital channel is delivered to the specific user based on the recommended multiple content items.

Abstract: A system, method, and apparatus provide the ability to generate and deliver digital content in a headless content management system. A layout engine exposes a visual editing interface to a business user and accepts input to compose a layout consisting of components that define structural entities that control what to render. Marketing content is associated with the components. Each component is added, via the visual editing interface, to zones of the layout. Developer code may restrict a type of component that can be added to each zone. The layout and zones control a conceptual design for rendering the digital content. The layout engine receives a request for content from one of multiple different client applications that are executing in different hosting environments. The content is delivered, based on the layout, to the client application for rendering to a consumer.

Abstract: A method and system provide the ability to personalize a digital channel. Multiple content assets are obtained and include an image content asset. Each of the assets is associated with an associated set of semantic elements. The multiple content assets are clustered into content clusters based on a similarity of the semantic elements. A first content asset is selected. The clustering is used as a metric to estimate distances between the first content asset and remaining multiple content assets. The remaining multiple content assets are scored based on the distances. One of the remaining multiple content assets is selected based on the scoring and provided for a personalized component of the digital channel. In addition, a coverage map that includes both users and content may be generated based on the clusters and then utilized to select the content asset.

Abstract: A method and system provide the ability to manage entities of a marketing domain model in a multi-state workflow. Multiple entities are acquired in a content hub. Each entity is a set of data that belongs together as one and includes properties that describe entity details. Relations are created between the multiple entities to give meaning to the marketing domain model. A solution graph is generated that represents all of the multiple entities (nodes) and relations (edges). Inside the solution graph, a state workflow can be created for each node. Nodes can be linked to a state and there are transitions between the states. Multiple non-linear state workflows can be orchestrated by an overall waterfall-based workflow (that is linear and time duration based. A graphical user interface enables management of and renders a representation of the multiple entities, the solution graph, and the workflows.

Abstract: A system, method, and apparatus provide the ability to generate and deliver personalized digital content. Multiple content tests are performed by presenting different variants of content to a set of different consumers of one or more consumers. A machine learning (ML model is generated and trained based on an analysis of results of the multiple content tests. Based on the ML model, personalization rules, that specify a certain variance for a defined set of facts, are output. The personalization rules are exposed to an administrative user who selects one or more of the personalization rules. A request for content is received from a requesting consumer. Based on similarities between the defined set of facts and the requesting consumer, a subset of the selected personalization rules are selected. The content is personalized and delivered to the requesting consumer based on the further selected personalization rules.

Abstract: A system, method, and apparatus provide the ability to generate and deliver personalized digital content. Multiple content tests are performed by presenting different variants of content to a set of different consumers of one or more consumers. A machine learning (ML model is generated and trained based on an analysis of results of the multiple content tests. Based on the ML model, personalization rules, that specify a certain variance for a defined set of facts, are output. The personalization rules are exposed to an administrative user who selects one or more of the personalization rules. A request for content is received from a requesting consumer. Based on similarities between the defined set of facts and the requesting consumer, a subset of the selected personalization rules are selected. The content is personalized and delivered to the requesting consumer based on the further selected personalization rules.

Abstract: A method, system, and apparatus provide the ability to personalize a digital channel. A digital channel is provided to multiple users and visitor information at each visit is collected. The visitor information includes data about each visit and multiple content items that are presented. The users are autonomously clustered by segmenting the user population into behavioral groups such that mutual information is maximized between the users in an assigned behavioral group and the content items. Based on the clustering, a model is generated that estimates a score for each interaction between users and content items. The model is updated at a defined interval. Based on the score, content items to recommend to a specific user are determined. The recommendation jointly maximizes an outcome and a learning speed of the model. The personalized digital channel is delivered to the specific user based on the recommended multiple content items.

Abstract: A method and system provide the ability to personalize a digital channel. Multiple content assets are obtained and include an image content associate. Each of the assets is associated with an associated set of semantic elements. The multiple content assets are clustered into content clusters based on a similarity of the semantic elements. A first content asset is selected. The clustering is used as a metric to estimate distances between the first content asset and remaining multiple content assets. The remaining multiple content assets are scored based on the distances. One of the remaining multiple content assets is selected based on the scoring and provided for a personalized component of the digital channel. In addition, a coverage map that includes both users and content may be generated based on the clusters and then utilized to select the content asset.

Abstract: A system, method, and apparatus provide the ability to generate and deliver personalized digital content. Multiple content tests are performed by presenting different variants of content to a set of different consumers of one or more consumers. A machine learning (ML model is generated and trained based on an analysis of results of the multiple content tests. Based on the ML model, personalization rules, that specify a certain variance for a defined set of facts, are output. The personalization rules are exposed to an administrative user who selects one or more of the personalization rules. A request for content is received from a requesting consumer. Based on similarities between the defined set of facts and the requesting consumer, a subset of the selected personalization rules are selected. The content is personalized and delivered to the requesting consumer based on the further selected personalization rules.

Abstract: A method and system provide the ability to manage entities of a marketing domain model in a multi-state workflow. Multiple entities are acquired in a content hub. Each entity is a set of data that belongs together as one and includes properties that describe entity details. Relations are created between the multiple entities to give meaning to the marketing domain model. A solution graph is generated that represents all of the multiple entities (nodes) and relations (edges). Inside the solution graph, a state workflow can be created for each node. Nodes can be linked to a state and there are transitions between the states. Multiple non-linear state workflows can be orchestrated by an overall waterfall-based workflow (that is linear and time duration based. A graphical user interface enables management of and renders a representation of the multiple entities, the solution graph, and the workflows.

Abstract: A system, method, and apparatus provide the ability to generate and deliver personalized digital content. Multiple content tests are performed by presenting different variants of content to a set of different consumers of one or more consumers. A machine learning (ML model is generated and trained based on an analysis of results of the multiple content tests. Based on the ML model, personalization rules, that specify a certain variance for a defined set of facts, are output. The personalization rules are exposed to an administrative user who selects one or more of the personalization rules. A request for content is received from a requesting consumer. Based on similarities between the defined set of facts and the requesting consumer, a subset of the selected personalization rules are selected. The content is personalized and delivered to the requesting consumer based on the further selected personalization rules.

Abstract: A system, method, and apparatus provide the ability to generate and deliver digital content in a headless content management system. A layout engine exposes a visual editing interface to a business user and accepts input to compose a layout consisting of components that define structural entities that control what to render. Marketing content is associated with the components. Each component is added, via the visual editing interface, to zones of the layout. Developer code may restrict a type of component that can be added to each zone. The layout and zones control a conceptual design for rendering the digital content. The layout engine receives a request for content from one of multiple different client applications that are executing in different hosting environments. The content is delivered, based on the layout, to the client application for rendering to a consumer.

Abstract: Behavioural data relating to online interactions is collected and stored in the form of a raw dataset. A data filter created on the basis of defined characteristics of interest is applied to the raw dataset, thereby obtaining a subset of data. Business intelligence analysis is performed on the data of the subset of data, and a business intelligence report is generated, in accordance with the defined characteristics of interest.

Abstract: The impact of personalized content on visitor behaviour is tested by dividing visitors belonging to a segment of visitors into first and second subsets. Visitors of the first subset receive personalized content and visitors of the second subset receive default content. The behaviour of the visitors is monitored and compared between the subsets, and the personalization rules are adjusted according to the outcome of the test. The test is initiated automatically by a content provider module.