1. A method for predicting business outcomes based upon recorded telephone calls, the method comprising: using a feature extraction engine for using a series of recorded telephone calls of individuals calling a business, processing the recorded telephone calls as sound files, extracting various audio features from each of the telephone calls, and building a feature matrix whereby a plurality of extracted audio features are tabulated, wherein each audio feature is a first dimension in the feature matrix and each telephone call is a second dimension in the feature matrix, wherein the first dimension being one of a row or a column and the second dimension being the other one of the row or the column, wherein extracting various audio features comprises extracting emotional information comprising changes in emotional states; using a model builder engine for determining whether the telephone calls resulted in a pre-determined business outcome, annotating the feature matrix by indicating whether the pre-determined business outcome was achieved by adding to the first dimension of the feature matrix a business outcome, and establishing a model whereby the pre-determined business outcome is associated with a subset of the extracted audio features; using a prediction engine for using additional telephone calls from individuals calling the business, processing the additional telephone calls as sound files, extracting various audio features from each of the sound files, predicting whether the pre-determined business outcome will occur based upon the established model, and ranking the additional telephone calls based upon the prediction; using a refining engine for determining whether each additional telephone call resulted in the actual occurrence of the pre-determined business outcome, comparing the actual occurrence with the prediction and modifying the established model on the basis of the comparison; and using a Lift measure, Lift p (f s ), as an index of performance of the model wherein 
 Lift p ( f s )= f s ( p )/ f r ( p ) where f s (p) is an accuracy of the a new model, S, in the case of a first p percent of elements and f r (p) provides a percentage of the model's performance.