A computer-implemented method for evaluating a reach of a social media influencer, comprising the steps of: receiving a data set associated with each of a plurality of influencers at a server; parsing the data set into quantitative data readable by a machine learning algorithm at the server; receiving, inputting, or both, a type of product or service at the server; classifying the type of product or service into at least one class of goods or services; training a node using the machine learning algorithm using the data set as an input; executing the machine learning algorithm to determine a score of each influencer for each class of goods or services; constructing a training data set from the data sets associated with each influencer, wherein the training data set comprises results of previous campaigns in at least one of the class of goods or services; analyzing the training data set input into the server; generating a forest of decision trees based on the training data set; receiving additional influencers on to the server, wherein the additional influencers have respective data sets associated with them, the data sets comprising non-conventional variables; executing the machine learning algorithm, the machine learning algorithm including the generated forest of decision trees, to determine a score of each additional influencer as it relates to each class of goods or services; prioritizing the influencers, additional influencers, or both, for each of the classes of goods or services that most are likely to have the highest influence in that class of goods or services, based on the respective determined scores, wherein non-conventional variables comprise: (i) reach or distribution potential, (ii) engagement and interaction potential, (iii) influence potential, (iv) creative information, (v) previous influencer programs or (vi) personal information. receiving a data set associated with each of a plurality of influencers at a server; parsing the data set into quantitative data readable by a machine learning algorithm at the server; receiving, inputting, or both, a type of product or service at the server; classifying the type of product or service into at least one class of goods or services; training a node using the machine learning algorithm using the data set as an input; executing the machine learning algorithm to determine a score of each influencer for each class of goods or services; constructing a training data set from the data sets associated with each influencer, wherein the training data set comprises results of previous campaigns in at least one of the class of goods or services; analyzing the training data set input into the server; generating a forest of decision trees based on the training data set; receiving additional influencers on to the server, wherein the additional influencers have respective data sets associated with them, the data sets comprising non-conventional variables; executing the machine learning algorithm, the machine learning algorithm including the generated forest of decision trees, to determine a score of each additional influencer as it relates to each class of goods or services; prioritizing the influencers, additional influencers, or both, for each of the classes of goods or services that most are likely to have the highest influence in that class of goods or services, based on the respective determined scores, wherein non-conventional variables comprise: (i) reach or distribution potential, (ii) engagement and interaction potential, (iii) influence potential, (iv) creative information, (v) previous influencer programs or (vi) personal information.