System and method for determining and managing reputation of entities and industries through use of behavioral connections

A method for removing skew from data, removing collinearity in data, and reducing server processing in a server of a network, comprising the steps of:

• removing, by the server, skew from data of previously determined factor scores by removing one or more redundant factors and collinearity in data of the previously determined factor scores by
• performing a redundancy analysis wherein the server projects a plurality of factors including the one or more redundant factors onto linearly independent unique components, resulting in one or more less factors than originally used, removing the one or more redundant factors;
• based on the redundancy analysis, decreasing training of the one or more supervised learning models by removing one or more survey questions from a first survey to a previous group of one or more respondents, by the server, wherein at least some of the data of the previously determined factor scores is based on one or more answers to the one or more survey questions to be removed from the first survey, the first survey to the previous group having a number of survey questions;
• based on the removing of the one or more redundant factors, reducing server processing to be performed by the server, wherein an unsupervised-machine-learning-based determining step comprises determining, by the server, which reputation driver within a set of reputation drivers each factor belongs by using unsupervised machine learning on data of the remaining factors after the redundancy analysis is performed, where a reputation driver is an area that members of the unique group would tend to care about when assessing the reputation of the entity, wherein the unsupervised-machine-learning-based determining assigns the remaining factors among the set of reputation drivers;
• wherein a sample size of population is determined that is statistically representative of a desired survey population and which provides at least a pre-defined percent confidence interval, where those within the sample size have a predefined level of familiarity with the entity and are referred to herein as a unique group,
• wherein the server determines a measure of the likelihood that the targeted population will perform a positive action on behalf of an entity, referred to as a behavioral connection score, wherein the server determines the measure of the likelihood that the targeted population will perform a positive action on behalf of an entity comprises:
• the server receives survey ratings from behavioral connection survey questions where each survey rating is provided by a party within the unique group;
• the server weights the received survey ratings to accommodate for at least one of the group consisting of cultural bias and missed demographic quotas;
• the server aggregates the weighted received ratings within each individual question to provide a single aggregated behavioral connection score for each behavioral connection survey question,
• wherein the predicted measure of likelihood that the targeted population will perform the positive action towards the entity, referred to as the behavioral connection score, is to be presented by a user interface of a user device that interacts with the server in the network.