An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.

Techniques and systems are described for analyzing components of digital content. A computing device of an analytics system receives user interaction data that describes an effect of user interaction with a plurality of items of digital content on achieving an action. The analytics system identifies which of a plurality of components are included in respective items of digital content. The analytics system then generates outcome data describing a likely effect of the plurality of components on achieving the action based on association with respective items of digital content. Additionally, the analytics system generates a recommendation to configure a subsequent item of digital content based on the outcome data. The recommendation is based on the likely effect of the different ones of the plurality of components, to generate more effective digital content.

An presentation generator is disclosed for generating presentations for interacting with a user on a personal topic of, e.g., the user's selection, wherein the presentations assist the user in obtaining a greater awareness of his/her motivations and/or behaviors relating to the topic. In one embodiment, the presentation generator generates and presents to the user textual observations, questions, and/or statements for the user's consideration. Such presentations use and/or are consistent with textual descriptions obtained from: (a) the results of one or more personality/motivation test results, and (b) user inputs, e.g., regarding the selected topic together with his/her confidence in the validity of such inputs. The invention organizes the textual descriptions so that outputs to the user can be generated from various personality/motivational perspectives thereby assisting the user in viewing the topic of discussion from different perspectives and thereby becoming more aware of his/her biases, motivations, and/or concerns relating to the topic.

A computer-implemented method, computer program product, and computing system for identifying a social platform; identifying a defined contribution for the social platform for a client; defining an anticipated outcome for the social platform based, at least in part, upon the defined contribution; and incentivizing the client to increase their defined contribution to an enhanced contribution by illustrating to the client a non-linear increase in the anticipated outcome in response to the enhanced contribution.

A computer-implemented method, computer program product, and computing system for receiving a social platform inquiry from a client; defining one or more client identifiers for the client; and recommending one or more social platforms based, at least in part, upon the one or more client identifiers.

A computer-implemented method, computer program product, and computing system for identifying a social platform; obtaining one or more efficiency data points concerning the social platform; and processing the one or more efficiency data points to generate a value impact multiplier for the social platform.

A computer-implemented method, computer program product, and computing system for receiving a contribution for a social platform from a client; producing promotional material for the client concerning their contribution to the social platform, wherein this promotional material is configured to positively impact a current responsibility score associated with the client; and determining a reaction to the promotional material.

A computer-implemented method, computer program product, and computing system for defining a plurality of social platforms within a social platform pool; and associating each of the plurality of social platforms with one or more responsibility descriptors, chosen from a responsibility descriptor pool. The one or more responsibility descriptors identify one or more portions of a responsibility score that may be positively impacted when a contribution is made to the associated social platform.

A computer-implemented method, computer program product, and computing system for receiving a social platform inquiry from a client; analyzing a current responsibility score associated with the client; and recommending one or more social platforms based, at least in part, upon the current responsibility score associated with the client.

Methods, systems, apparatus and articles of manufacture to determine causal effects are disclosed herein. An example apparatus includes a weighting engine to calculate a first set of weights for a first set of covariates corresponding to a treatment dataset and a second set of weights for a second set of covariates corresponding to a control dataset using maximum entropy, the first set of weights to equal the second set of weights. The example apparatus also includes a weighting response engine to calculate a weighted response for the treatment dataset and a weighted response for the control dataset by: mapping the first set of weights and the second set of weights to a uniform weighting identifier, determining a constraint matrix based on the first set of weights, the second set of weights and the uniform weighting identifier, and bypassing multivariate reweighting by calculating the weighted response for the treatment dataset and the weighted response for the control dataset by applying maximum entropy to the constraint matrix.