Abstract: Methods and systems for improved and efficient campaign message classification are disclosed. By automating the campaign message classification process, the system can improve efficiency in categorizing and managing campaign messages. The system can predict a message's type or characteristics via an ensemble model that comprises one or more logic-rule model(s) and machine learning language model(s). The ensemble model can process various data and predict a message's type or characteristics based on an aggregated prediction mechanism.

Abstract: Apparatuses, methods, and systems for determining winning arms of electronic testing. One method includes obtaining historical data related to the testing, creating a histogram based on the historical data, the histogram including bins and weights, creating a distribution by computing concentration parameters of the distribution from the weights of the histogram, executing the testing, receiving new data collected based on the execution of the test, allocating the new data into same bins as the bins of the histogram of the historical data yielding a new data bin count, computing a posterior distribution comprising updating the distribution using the same bins and the new data bin counts and the concentration parameters of the distribution in closed form vie conjugate prior formulae, inferring corresponding central tendencies of samplings of a metric distribution, constructing an overall utility distribution for each arms of the test, and determining a winning arm of the testing.