Abstract: This disclosure relates to providing content (e.g., marketing content) to an individual based on a psychographic profile of an individual. The method includes obtaining a psychographic profile of an individual. The psychographic profile comprises one or more psychographic dimensions associated with the first individual and a psychographic profile group. A psychographic dimension corresponds to at least one of an interest, an attitude, an opinion, a value, a behavior, or an activity of the individual. Content (e.g., marketing content) is received, the content being associated with a psychographic profile group corresponding to the psychographic profile of the individual. The content is provided to the individual based on the associated psychographic profile group of the content and the associated psychographic profile group of the individual.

Abstract: Systems and methods are disclosed herein for automatically identifying segments of customers based on customers having similar characteristics and behaviors. In one embodiment of the invention, event-level records representing customer interactions for multiple customers are received and the event-level records are summarized to combine attributes for respective customers into customer-level records. The customer-level records include attributes for customer characteristics and behaviors based on summarizing the event-level records. Systems and methods further cluster the customer-level records based on the attributes for customer characteristics and behaviors and, based on the clustering, identify segments of clusters having a statistically significant value relative to other clusters. The systems and methods display the identified segments on a user-interface.

Abstract: Systems and methods are disclosed herein for providing a user interface representing differences between segments of end users. The systems and methods receive user input on a user interface identifying a first segment, the first segment being a subset of the end users having a particular characteristic, determine differences between the first segment and a second segment, and represent, on the user interface, the differences between the first segment and the second segment based on relative significances of the differences. The marketer using the user interface is able to quickly and easily identify the metrics, dimensions, and/or relationships to other segments that most distinguish the compared segments from one another.

Abstract: Systems and methods disclosed herein compactly store representations of segment-specific interaction data from a real time data stream of data interactions by multiple entities to facilitate segment-specific analytics for particular time periods. Segment rules defining characteristics of entities within a segment are received. A first probabilistic data structure is created representing unique entity IDs included in instances of interaction data in the real time data stream during a first time period. A second probabilistic data structure representing unique entity IDs included in instances of interaction data in the real time data stream during a second time period different from the first time period. The first probabilistic data structure represents only entity IDs of entities within the segment and the second probabilistic data structure represents only entity IDs of entities within the segment. The first and second probabilistic data structures are indexed and stored.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to an analytics tool for detecting real-time user or “visitor” events based on real-time data. More specifically, events are detected based on actions not taken by a user. In this regard, events can be defined and, thereafter, detected based on inactions of a user. In some cases, events are inferred or predicted based on a calculated likelihood of a user not performing an action. Upon determining an event based on an action not being performed by a user, an interested party may be notified thereof such that the interested party can influence, in real-time, visitor conversion.

Abstract: A method of sowing watermelon seeds includes seeding triploid watermelon seeds and watermelon pollinizer seeds in separate cells within a seedling tray. The triploid watermelon seeds produce seedless watermelons and the pollinizer seeds produce seeded watermelons. The seeded watermelons are not inherently bred to have reduced competition to plants grown from the triploid watermelon seeds.

Abstract: A method of sowing watermelon seeds includes seeding triploid watermelon seeds and watermelon pollinizer seeds in separate cells within a seedling tray. The triploid watermelon seeds produce seedless watermelons and the pollinizer seeds produce seeded watermelons. The seeded watermelons are not inherently bred to have reduced competition to plants grown from the triploid watermelon seeds.

Abstract: Systems and methods are disclosed herein for automatically identifying segments of customers based on customers having similar characteristics and behaviors. In one embodiment of the invention, event-level records representing customer interactions for multiple customers are received and the event-level records are summarized to combine attributes for respective customers into customer-level records. The customer-level records include attributes for customer characteristics and behaviors based on summarizing the event-level records. Systems and methods further cluster the customer-level records based on the attributes for customer characteristics and behaviors and, based on the clustering, identify segments of clusters having a statistically significant value relative to other clusters. The systems and methods display the identified segments on a user-interface.

Abstract: Systems and methods are disclosed herein for providing a user interface representing differences between segments of end users. The systems and methods receive user input on a user interface identifying a first segment, the first segment being a subset of the end users having a particular characteristic, determine differences between the first segment and a second segment, and represent, on the user interface, the differences between the first segment and the second segment based on relative significances of the differences. The marketer using the user interface is able to quickly and easily identify the metrics, dimensions, and/or relationships to other segments that most distinguish the compared segments from one another.

Abstract: This disclosure relates to providing content (e.g., marketing content) to an individual based on a psychographic profile of an individual. The method includes obtaining a psychographic profile of an individual. The psychographic profile comprises one or more psychographic dimensions associated with the first individual and a psychographic profile group. A psychographic dimension corresponds to at least one of an interest, an attitude, an opinion, a value, a behavior, or an activity of the individual. Content (e.g., marketing content) is received, the content being associated with a psychographic profile group corresponding to the psychographic profile of the individual. The content is provided to the individual based on the associated psychographic profile group of the content and the associated psychographic profile group of the individual.

Abstract: Systems and methods disclosed herein compactly store representations of segment-specific interaction data from a real time data stream of data interactions by multiple entities to facilitate segment-specific analytics for particular time periods. Segment rules defining characteristics of entities within a segment are received. A first probabilistic data structure is created representing unique entity IDs included in instances of interaction data in the real time data stream during a first time period. A second probabilistic data structure representing unique entity IDs included in instances of interaction data in the real time data stream during a second time period different from the first time period. The first probabilistic data structure represents only entity IDs of entities within the segment and the second probabilistic data structure represents only entity IDs of entities within the segment. The first and second probabilistic data structures are indexed and stored.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to an analytics tool for detecting real-time user or “visitor” events based on real-time data. More specifically, events are detected based on actions not taken by a user. In this regard, events can be defined and, thereafter, detected based on inactions of a user. In some cases, events are inferred or predicted based on a calculated likelihood of a user not performing an action. Upon determining an event based on an action not being performed by a user, an interested party may be notified thereof such that the interested party can influence, in real-time, visitor conversion.

Abstract: A method of sowing watermelon seeds is provided, specifically a method for sowing triploid seedless watermelon seeds and enhanced watermelon pollenizer seeds in the same seedling tray through the use of a mechanical seeder.

Abstract: A method of sowing watermelon seeds is provided, specifically a method for sowing triploid seedless watermelon seeds and enhanced watermelon pollenizer seeds in the same seedling tray through the use of a mechanical seeder.

Abstract: A method of sowing watermelon seeds is provided, specifically a method for sowing triploid seedless watermelon seeds and enhanced watermelon pollenizer seeds in the same seedling tray through the use of a mechanical seeder.

Abstract: A method of sowing watermelon seeds is provided, specifically a method for sowing triploid seedless watermelon seeds and enhanced watermelon pollenizer seeds in the same seedling tray through the use of a mechanical seeder.