Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a machine learning model and a credit value model to generate user interface elements that present credit values and credit value conditions in real time for user accounts. For instance, the disclosed systems can generate an activity score using an activity machine learning model with internal user activity data of a user account. Then, utilizing a credit value model with the activity score and a user activity condition, the disclosed systems can determine a dynamic credit value range for the user account. Indeed, the disclosed systems can display user interface elements with selectable credit values from the dynamic credit value range. Additionally, the disclosed systems can utilize the credit value model to determine and display one or more dynamic credit value conditions for a selected credit value received from the selectable credit values.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a machine learning model and a credit value model to generate user interface elements that present credit values and credit value conditions in real time for user accounts. For instance, the disclosed systems can generate an activity score using an activity machine learning model with internal user activity data of a user account. Then, utilizing a credit value model with the activity score and a user activity condition, the disclosed systems can determine a dynamic credit value range for the user account. Indeed, the disclosed systems can display user interface elements with selectable credit values from the dynamic credit value range. Additionally, the disclosed systems can utilize the credit value model to determine and display one or more dynamic credit value conditions for a selected credit value received from the selectable credit values.

Abstract: A method and system are provided for providing incentives and rewards to consumers in return for their sharing of their electronically-derived consumer location data and sharing their opinions and feedback. The system may be programmed to determine parameters of a reward offered to an individual consumer or group of consumers based on or more criteria. In addition, location-derived insights and information reported by the consumers may be used to segment and customize the available incentives and rewards for each consumer. Reward redemption analytics may also be used to evaluate the effectiveness of reward customization.

Abstract: A system for offering survey response opportunities for sale to purchasers in a survey market system that is associated with a consumer analytics system. A survey response “opportunity” may be an expectation of the system that, if a survey is distributed, a response will be received and represents a chance for a potential surveyor to request that a survey be distributed on its behalf. Using the system, a purchaser may specify one or more conditions for distribution of a survey and one or more questions to be included in the survey, and request that a consumer analytics system distribute the survey in response to detecting that the conditions are met. For example, if a condition relates to consumer behavior, a survey may be distributed in response to detecting, from an analysis of location data indicating geographic locations of the consumer, that the consumer engaged in the behavior.

Abstract: A method and system are provided for providing incentives and rewards to consumers in return for their sharing of their electronically-derived consumer location data and sharing their opinions and feedback. The system may be programmed to determine parameters of a reward offered to an individual consumer or group of consumers based on or more criteria. In addition, location-derived insights and information reported by the consumers may be used to segment and customize the available incentives and rewards for each consumer. Reward redemption analytics may also be used to evaluate the effectiveness of reward customization.

Abstract: A method and system are provided for providing incentives and rewards to consumers in return for their sharing of their electronically-derived consumer location data and sharing their opinions and feedback. The system may be programmed to determine parameters of a reward offered to an individual consumer or group of consumers based on or more criteria. In addition, location-derived insights and information reported by the consumers may be used to segment and customize the available incentives and rewards for each consumer. Reward redemption analytics may also be used to evaluate the effectiveness of reward customization.