Apparatus and methods present a content item and question from an inquirer to a group of users in a first feedback session with a requirement for the users to provide predictions of how a member of a distinct target group will respond to the content item, receiving, during the first feedback session, the first group's prediction of the target group's responses, presenting, during a second feedback session, the same content item and the same question to the target group with a requirement for the target group to provide responses directed to their own responses, constructing a predictive model of the target group based on responses received from the target group during the second feedback session, measuring accuracy of the first group's predictions using the target group predictive model and providing the inquirer access to an ordered visual representation of the first group users arranged as a function of accuracy.

A segmentation system utilizes a supervised learning method and a clustering analysis to identify clusters, thereby segmenting a population into groups, where the clusters are associated with various conversion potentials that indicate the probability of an event. The segmentation system employs the supervised learning method to train a model on training data comprising historical conversion data and features associated with members of the group. A subset of features is selected from a ranked order that is determined using weights generated by the supervised learning. A clustering analysis is performed for a population with respect to the subset to generate clusters. A superior cluster is identified based on it having a conversion potential greater than a conversion potential of another cluster. In a marketing context, the system can be employed to identify a superior cluster of users that have a higher conversion potential in response to an advertisement campaign.

Transaction systems and methods are disclosed. A system may include a controller having a transaction detection circuit to interpret a transaction request value, wherein the transaction request value includes a transaction description for one of a proposed or an imminent transaction, and a cryptocurrency type value and a transaction amount value. A transaction locator circuit then determines a transaction location parameter in response to the transaction request value, wherein the transaction location is a geographic value or a jurisdiction value. A transaction execution circuit then provides a transaction implementation command in response to the transaction location parameter.

Systems and methods for forward market renewable energy credit prediction from business entity behavior data are disclosed. An example transaction-enabling system may include a forward market circuit to access a forward energy credit market and a market forecasting circuit to automatically generate a forecast for a forward market price of an energy credit in the forward energy credit market. The example system may include wherein the forecast is based at least in part on a business entity behavior collected from at least one business entity behavioral data source, and wherein the energy credit comprises a renewable energy credit associated a renewable energy system. The example system may further include a smart contract circuit to perform at least one of selling the renewable energy credit or purchasing the renewable energy credit on the forward energy credit market in response to the forecasted forward market price.

The present disclosure describes transaction-enabling systems and methods. A system can include a controller and a fleet of machines each having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement. The controller may include a resource requirement circuit structured to determine an amount of a resource for each of the machines to service at least one of the task requirements, a resource market circuit to access a resource market, and a resource distribution circuit to execute an aggregated transaction of the resource on the resource market in response to the determined amount of the resource for each of the machines.

One or more computing devices, systems, and/or methods for isolated budget utilization are provided. A first budget pacing component is assigned to control bidding by a first content serving component for a set of content items. A second budget pacing component is assigned to control bidding by a second content serving component for the set of content items. The first budget pacing component controls the bidding by the first content serving component according to a first portion of a content item budget based upon a traffic share of the first content serving component. The second budget pacing component controls the bidding by the second content serving component according to a second portion of the content item budget based upon a traffic share of the second content serving component.

Transaction-enabling systems and methods are disclosed. A system may include a controller to interpret a resource utilization requirement for a task system and a plurality of external data sources. The system may further include an expert system to predict a forward market price for a resource in response to the resource utilization requirement and the plurality of external data sources. Then, in response to the predicted forward market price, the controller may execute a transaction on a resource market.

Transaction-enabling systems and methods are disclosed. A system may include a fleet of machines each having a task resource requirement. A controller may include a resource requirement circuit to determine an amount of a resource required for each of the machines to service the task and a resource distribution circuit structured to adaptively improve a utilization of the resource for each of the fleet of machines.

Methods and apparatuses are described for automated optimization and personalization of customer-specific communication channels using feature classification. A server captures historical interaction data comprising a channel type, a user identifier, an interaction date, and a user response value. The server generates a channel feature vector for each combination of channel type, user identifier, and interaction date. The server identifies features from the channel feature vectors for each different channel type and aggregates the features into a common feature vector. The server executes a trained classification model on the common feature vectors to select user identifiers for each different channel type that have an engagement probability value at or above a corresponding threshold. The server determines, for each different channel type, a distance value between the engagement probability value and the corresponding threshold and communicates with a remote computing device via a channel that is associated with an optimal distance value.

Systems and methods for aggregating transactions and optimization data related to energy and energy credits include a transaction-enabling system including a resource requirement circuit structured to aggregate a resource requirement for a fleet of machines to perform a task, wherein the resource requirement comprises an energy credit requirement; a forward resource market circuit structured to access a forward market for energy; and a machine resource acquisition circuit structured to execute a transaction on the forward market for energy in response to the aggregated resource requirement.