Abstract: The present invention is a cloud-based machine learning method and system that utilizes the attributes and past performance statistics of visitors to a set of webpage variants to predict performance statistics for incoming website visitors with respect to the webpage variants, and uses such predicted performance statistics to direct such incoming website visitors; and which learns from the performance of each directed website visitor by refining the past performance statistics to take into account such performance and the attributes of each directed website visitor, all in order to optimize future performance statistics for the set of webpage variants.

Abstract: A cloud-based, machine learning method and system to compare, rank and/or predict an example webpage's performance (such as conversion rate for webpages in an online marketing campaign) relative to its closest peers. The closest peers are selected from a sample set of webpages for which performance is known. A topic model is constructed from a modeling set of webpages, based on their content. A topic vector for the example webpage and for each webpage in the sample set is determined based upon the constructed topic model. The example webpage's closest peers are determined by the distance/similarity measure between the topic vector of the example webpage and of each webpage of the sample set. The method and system can be applied to one or a plurality of example webpages in order to assess webpages that are underperforming relative to their closest peers.