Abstract: Systems, apparatus, methods, and computer program products are provided for optimized and effective leading messages, which may be an email subject that may provoke a consumer to access the body portion of an email message. A system may include circuitry configured to programmatically determine a predicted access rate for a leading message when the leading message is provided as a portion of a promotional message. Circuitry may be configured to track historical data indicating the access rates of leading messages and/or leading message terms. The circuitry may be configured to leverage the historical data to determine predicted access rates for leading messages, such as based at least in part on historical access rates associated with one or more leading message terms of the leading message.

Abstract: The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with a device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Abstract: Systems, apparatus, methods, and computer program products are provided for optimized and effective leading messages, which may be an email subject that may provoke a consumer to access the body portion of an email message. A system may include circuitry configured to programmatically determine a predicted access rate for a leading message when the leading message is provided as a portion of a promotional message. Circuitry may be configured to track historical data indicating the access rates of leading messages and/or leading message terms. The circuitry may be configured to leverage the historical data to determine predicted access rates for leading messages, such as based at least in part on historical access rates associated with one or more leading message terms of the leading message.

Abstract: A method, apparatus, and computer program product are disclosed for improved machine learning using a statistical model. In the context of an apparatus, some example embodiments include a processor configured to cause retrieval of information regarding a plurality of consumers, and modeling circuitry configured to train a statistical model of the plurality of consumers based on the retrieved information, and predict, using the statistical model, an incremental booking value associated with the promotion for each consumer of the plurality of consumers. The processor is further configured to select a subset of the plurality of consumers for receiving impressions of the promotion. Some example embodiments may further include communications circuitry configured to transmit an impression of the promotion to each consumer in the subset of the plurality of consumers.

Abstract: Embodiments are disclosed for automated evaluation of compatibility of a data structure with a user device. An example method includes receiving, by communications circuitry, a set of user device characteristics regarding the user device, and retrieving, by the personalization circuitry, a set of data structure characteristics regarding the data structure. The example method further includes calculating, by the personalization circuitry, a set of characteristic-level compatibility scores, and generating, by the personalization circuitry and based on the set of characteristic-level compatibility scores, a compatibility score for the data structure and the user device. Subsequently, the example method includes generating, by an aggregator and using the generated compatibility score, an indication of relative compatibility of the data structure for the user device, and causing transmission, by communications circuitry, of a control signal to the user device based on the indication of relative compatibility.

Abstract: Embodiments provide a computer-executable method, computer system and non-transitory computer-readable medium for programmatically generating an association among two or more purchase categories based on purchase data of a plurality of consumers. The method includes programmatically accessing, from a dataset via a network device, prior purchase data associated with purchases of a plurality of commercial objects by a plurality of consumers. The method also includes programmatically identifying a plurality of categories associated with the plurality of commercial objects. The method also includes, for each consumer in the plurality of consumers, programmatically generating a total number of purchases by the consumer in each category in the plurality of categories.

Abstract: The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with a device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Abstract: Systems, apparatus, methods, and computer program products are provided for optimized and effective leading messages, which may be an email subject that may provoke a consumer to access the body portion of an email message. A system may include circuitry configured to programmatically determine a predicted access rate for a leading message when the leading message is provided as a portion of a promotional message. Circuitry may be configured to track historical data indicating the access rates of leading messages and/or leading message terms. The circuitry may be configured to leverage the historical data to determine predicted access rates for leading messages, such as based at least in part on historical access rates associated with one or more leading message terms of the leading message.

Abstract: Embodiments provide a computer-executable method, computer system and non-transitory computer-readable medium for programmatically generating an association among two or more purchase categories based on purchase data of a plurality of consumers. The method includes programmatically accessing, from a dataset via a network device, prior purchase data associated with purchases of a plurality of commercial objects by a plurality of consumers. The method also includes programmatically identifying a plurality of categories associated with the plurality of commercial objects. The method also includes, for each consumer in the plurality of consumers, programmatically generating a total number of purchases by the consumer in each category in the plurality of categories.

Abstract: A method, apparatus, and computer program product are disclosed for improved promotion targeting. An example apparatus includes a processor configured to cause retrieval of historical information regarding transactions associated with a plurality of identifier entities. The example apparatus further includes modeling circuitry configured to train a statistical model using the retrieved historical information, and estimate, using the statistical model, values for expected identifier entity transaction requests associated with each of the plurality of identifier entities. The processor of the example apparatus may further be configured to select, based on the estimated values, a subset of identifier entities to receive impressions of the promotion, and the apparatus may further include communications circuitry configured to transmit an impression of the promotion to each identifier entity in the subset.

Abstract: A method, apparatus, and computer program product are disclosed for improved promotion targeting. An example apparatus includes a processor configured to cause retrieval of historical information regarding transactions associated with a plurality of identifier entities. The example apparatus further includes modeling circuitry configured to train a statistical model using the retrieved historical information, and estimate, using the statistical model, values for expected identifier entity transaction requests associated with each of the plurality of identifier entities. The processor of the example apparatus may further be configured to select, based on the estimated values, a subset of identifier entities to receive impressions of the promotion, and the apparatus may further include communications circuitry configured to transmit an impression of the promotion to each identifier entity in the subset.

Abstract: The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with a device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Abstract: A method, apparatus, and computer program product are disclosed for improved machine learning using a statistical model. In the context of an apparatus, some example embodiments include a processor configured to cause retrieval of information regarding a plurality of consumers, and modeling circuitry configured to train a statistical model of the plurality of consumers based on the retrieved information, and predict, using the statistical model, an incremental booking value associated with the promotion for each consumer of the plurality of consumers. The processor is further configured to select a subset of the plurality of consumers for receiving impressions of the promotion. Some example embodiments may further include communications circuitry configured to transmit an impression of the promotion to each consumer in the subset of the plurality of consumers.

Abstract: Embodiments are disclosed for automated evaluation of compatibility of a data structure with a user device. An example method includes receiving, by communications circuitry, a set of user device characteristics regarding the user device, and retrieving, by the personalization circuitry, a set of data structure characteristics regarding the data structure. The example method further includes calculating, by the personalization circuitry, a set of characteristic-level compatibility scores, and generating, by the personalization circuitry and based on the set of characteristic-level compatibility scores, a compatibility score for the data structure and the user device. Subsequently, the example method includes generating, by an aggregator and using the generated compatibility score, an indication of relative compatibility of the data structure for the user device, and causing transmission, by communications circuitry, of a control signal to the user device based on the indication of relative compatibility.

Abstract: Embodiments are disclosed for automated evaluation of compatibility of a data structure with a user device. An example method includes receiving, by communications circuitry, a set of user device characteristics regarding the user device, and retrieving, by the personalization circuitry, a set of data structure characteristics regarding the data structure. The example method further includes calculating, by the personalization circuitry, a set of characteristic-level compatibility scores, and generating, by the personalization circuitry and based on the set of characteristic-level compatibility scores, a compatibility score for the data structure and the user device. Subsequently, the example method includes generating, by an aggregator and using the generated compatibility score, an indication of relative compatibility of the data structure for the user device, and causing transmission, by communications circuitry, of a control signal to the user device based on the indication of relative compatibility.

Abstract: A method, apparatus, and computer program product are disclosed for improved machine learning using a statistical model. In the context of an apparatus, some example embodiments include a processor configured to cause retrieval of information regarding a plurality of consumers, and modeling circuitry configured to train a statistical model of the plurality of consumers based on the retrieved information, and predict, using the statistical model, an incremental booking value associated with the promotion for each consumer of the plurality of consumers. The processor is further configured to select a subset of the plurality of consumers for receiving impressions of the promotion. Some example embodiments may further include communications circuitry configured to transmit an impression of the promotion to each consumer in the subset of the plurality of consumers.

Abstract: Systems, apparatus, methods, and computer program products are provided for optimized and effective leading messages, which may be an email subject that may provoke a consumer to access the body portion of an email message. A system may include circuitry configured to programmatically determine a predicted access rate for a leading message when the leading message is provided as a portion of a promotional message. Circuitry may be configured to track historical data indicating the access rates of leading messages and/or leading message terms. The circuitry may be configured to leverage the historical data to determine predicted access rates for leading messages, such as based at least in part on historical access rates associated with one or more leading message terms of the leading message.

Abstract: Embodiments provide a computer-executable method, computer system and non-transitory computer-readable medium for programmatically generating an association among two or more purchase categories based on purchase data of a plurality of consumers. The method includes programmatically accessing, from a dataset via a network device, prior purchase data associated with purchases of a plurality of commercial objects by a plurality of consumers. The method also includes programmatically identifying a plurality of categories associated with the plurality of commercial objects. The method also includes, for each consumer in the plurality of consumers, programmatically generating a total number of purchases by the consumer in each category in the plurality of categories.

Abstract: The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with a device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Abstract: A method, apparatus, and computer program product are disclosed for improved promotion targeting. An example apparatus includes a processor configured to cause retrieval of historical information regarding transactions associated with a plurality of identifier entities. The example apparatus further includes modeling circuitry configured to train a statistical model using the retrieved historical information, and estimate, using the statistical model, values for expected identifier entity transaction requests associated with each of the plurality of identifier entities. The processor of the example apparatus may further be configured to select, based on the estimated values, a subset of identifier entities to receive impressions of the promotion, and the apparatus may further include communications circuitry configured to transmit an impression of the promotion to each identifier entity in the subset.