A computer-based system collects data associated with a user activity. The data is transmitted from an app running on a computing device with a user account authenticated by the computer-based system. A carbon footprint of the user activity is calculated based on the data associated with the user activity. The system calculates a proof of environmental impact in response to a function of the carbon footprint and a baseline value. An amount of cryptocurrency is generated based on the proof of environmental impact by writing a transaction for the amount of cryptocurrency to a blockchain in response to proof of environmental impact. The amount of cryptocurrency is assigned to the user account authenticated with the computer-based system.

A system and method for determining market share of an organization. The method encompasses receiving, at least one of a voice of customer data, an internal data of the organization and an external data. The method thereafter leads to determining, one or more set of target features based on at least one of the voice of customer data, the internal data of the organization and the external data. Further the method comprises generating, one or more pre-trained dataset based at least on the one or more set of target features. The method thereafter encompasses receiving, at least one of a first set of feature constraints and a second set of feature constraints. Further the method comprises determining, the market share of the organization based at least on the one or more pre-trained dataset, the first set of feature constraints and the second set of feature constraints.

A system and method for predicting behavior and/or outcomes related to a consumer's experience with an organization are implemented. Household data for households that are associated with a customer service interaction as of a certain date is collected, the household data having been created over a first pre-determined period of time preceding the certain date. The household data is analyzed to identify positive household data sets and negative household data sets. The positive household data sets relate to customer service interactions which preceded a high level customer service interaction within a subsequent period of time and the negative household data sets relate to customer service transactions which did not precede a high level customer service interaction with the subsequent period of time. The positive household data sets and the negative household data sets are processed in the aggregate, using a trained support vector machine model, to determine cumulative differences between data contained within the positive household data sets and the negative household data sets. Each day, daily household data is collected. The daily household data describes individual customer service transactions occurring during a previous calendar day. The daily household data is processed using the model to determine whether each individual customer service transaction occurring during the previous calendar day is more similar to the positive household data sets or to the negative household data sets. The individual customer service transactions that are more similar to the positive household data sets are flagged for proactive intervention.

A system and method for predicting behavior and/or outcomes related to a consumer's experience with an organization are implemented. Household data for households that are associated with a customer service interaction as of a certain date is collected, the household data having been created over a first pre-determined period of time preceding the certain date. The household data is analyzed to identify positive household data sets and negative household data sets. The positive household data sets relate to customer service interactions which preceded a high level customer service interaction within a subsequent period of time and the negative household data sets relate to customer service transactions which did not precede a high level customer service interaction with the subsequent period of time. The positive household data sets and the negative household data sets are processed in the aggregate, using a trained support vector machine model, to determine cumulative differences between data contained within the positive household data sets and the negative household data sets. Each day, daily household data is collected. The daily household data describes individual customer service transactions occurring during a previous calendar day. The daily household data is processed using the model to determine whether each individual customer service transaction occurring during the previous calendar day is more similar to the positive household data sets or to the negative household data sets. The individual customer service transactions that are more similar to the positive household data sets are flagged for proactive intervention.

A system, method, and computer program product are provided for centralized consent management. In operation, the consent management system receives user selections from a user indicating which user data is capable of being utilized for analysis by a company. The consent management system stores the user selections of which user data is capable of being utilized for analysis by the company in a consent database. The consent management system generates a consent vector corresponding to the user selections of which user data is capable of being utilized for analysis by the company. Additionally, the consent management system associates the consent vector with a consent vector identification. Further, the consent management system tags incoming data with the consent vector identification to associate a user consent with the incoming data. The consent management system stores and encodes the incoming data. Moreover, the consent management system enforces consent restrictions by conditionally allowing access to the stored data by the company based on corresponding consent vector identifications.

A system, method, and computer program product are provided for centralized consent management. In operation, the consent management system receives user selections from a user indicating which user data is capable of being utilized for analysis by a company. The consent management system stores the user selections of which user data is capable of being utilized for analysis by the company in a consent database. The consent management system generates a consent vector corresponding to the user selections of which user data is capable of being utilized for analysis by the company. Additionally, the consent management system associates the consent vector with a consent vector identification. Further, the consent management system tags incoming data with the consent vector identification to associate a user consent with the incoming data. The consent management system stores and encodes the incoming data. Moreover, the consent management system enforces consent restrictions by conditionally allowing access to the stored data by the company based on corresponding consent vector identifications.

Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, filters can be defined for multiple segments of a training data set. Model segments corresponding to respective segments can be trained using an appropriate subset of the training data set. When a request for a machine learning result is made, filter criteria for the request can be determined and an appropriate model segment can be selected and used for processing the request. One or more hyperparameter values can be defined for a machine learning scenario. When a machine learning scenario is selected for execution, the one or more hyperparameter values for the machine learning scenario can be used to configure a machine learning algorithm used by the machine learning scenario.

In accordance with an embodiment, an information processing apparatus acquires a rank of a visiting user and an amount according to the ranking. The information processing apparatus acquires a price at an own store of a commodity that the user has selected for purchase. The information processing apparatus acquires a price at another store of the commodity that the user has selected for purchase. In a case in which the price at the own store is higher than the price at the other store, the information processing apparatus adds an amount based on a difference in price therebetween to the amount according to the ranking, which is acquired by a first acquisition means.

A method in which user information is transmitted from at least one data source to a processing unit of a learning device. The user information is used, by the processing unit via a machine learner, to generate at least one user model. The at least one user model is adapted via an adaptation of parameters used by the at least one machine learner to generating the at least one user model. The parameters, used by the at least one machine learner for generating the at least one user model, are adapted as a function of at least one predefined rule. The user model generated on the basis of the adapted parameters is used to personalize at least one terminal.

A method in which user information is transmitted from at least one data source to a processing unit of a learning device. The user information is used, by the processing unit via a machine learner, to generate at least one user model. The at least one user model is adapted via an adaptation of parameters used by the at least one machine learner to generating the at least one user model. The parameters, used by the at least one machine learner for generating the at least one user model, are adapted as a function of at least one predefined rule. The user model generated on the basis of the adapted parameters is used to personalize at least one terminal.