Abstract: According to some embodiments of the present disclosure, a computer-implemented method for generating machine-learned models on behalf of an organization is disclosed. In embodiments, the method includes receiving a contact list, generating a respective individual profile, generating a plurality of analytical insights, presenting the analytical insights, receiving a set of attribute-specific filters, filtering the individual profiles, training a machine-learned model, determining a measure of predictive power of the machine-learned model based on a testing dataset and the machine-learned model, receiving a cutoff level from a user, and deploying the machine-learned model on behalf of the organization.

Abstract: An intelligent peer-to-peer fundraising campaign platform is provided for managing charitable and for profit campaigns. The platform includes capabilities for creating and managing campaigns by supporters, retrieving person data for supporters and leads for the campaign, relationship data and other attributes including affinity data, donation history data, potential and financial data and storing such data in the form of social graph. The campaign platform also helps supporters of a campaign to identify leads, send personalized communication to the leads for enlisting support to the campaign, determine donor outcomes with respect to each personalized communication sent to the lead; and updating or reinforcing a donor prediction model based on the donor outcome.

Abstract: An intelligent peer-to-peer fundraising campaign platform is provided for managing charitable and for profit campaigns. The platform includes capabilities for creating and managing campaigns by supporters, retrieving person data for supporters and leads for the campaign, relationship data and other attributes including affinity data, donation history data, potential and financial data and storing such data in the form of social graph. The campaign platform also helps supporters of a campaign to identify leads, send personalized communication to the leads for enlisting support to the campaign, determine donor outcomes with respect to each personalized communication sent to the lead; and updating or reinforcing a donor prediction model based on the donor outcome.