Abstract: An online experiment system is described that generates a randomization evaluation for an online experiment, while the experiment is ongoing, indicating whether a distribution of experiment participants allocated to one or more participant groups satisfies an expected distribution. The online experiment system analyzes one of the experiment groups to obtain an observed distribution of the subset of experiment participants included in the experiment group. The online experiment system then evaluates the observed distribution relative to the expected distribution for the experiment according to a decision criteria of a population stability index test. The decision criteria is influenced by a tuning parameter that represents a ratio of experiment participants included in the observed experiment group to experiment participants included in a different experiment group.

Abstract: A server accesses a plurality of users' sessions with the web server. Each user session indicating a page flow of a corresponding user session for a plurality of web pages provided by the web server. The server generates a learning model using a neural network based on the plurality of users' sessions. The learning model is configured to predict a next user activity based on a current page flow of a current user session. The next user activity indicating one of continuing the current user session by visiting another web page provided by the web server and ending the current user session. The server dynamically adjusts a content of a web page based on the predicted next user activity.

Abstract: A server accesses a plurality of users' sessions with the web server. Each user session indicating a page flow of a corresponding user session for a plurality of web pages provided by the web server. The server generates a learning model using a neural network based on the plurality of users' sessions. The learning model is configured to predict a next user activity based on a current page flow of a current user session. The next user activity indicating one of continuing the current user session by visiting another web page provided by the web server and ending the current user session. The server dynamically adjusts a content of a web page based on the predicted next user activity.

Abstract: Sample delta monitoring is described herein. In one or more implementations, first and second experimental groups are defined in a randomized controlled trial. A first set of data indicative of a first experimental group in the randomized controlled trial is received. A second set of data indicative of a second experimental group in the randomized controlled trial is received. Based on sample counts of the first and second sets of data, an indication is generated that the experimental groups in the randomized controlled trial are skewed.

Abstract: A server accesses a plurality of users' sessions with the web server. Each user session indicating a page flow of a corresponding user session for a plurality of web pages provided by the web server. The server generates a learning model using a neural network based on the plurality of users' sessions. The learning model is configured to predict a next user activity based on a current page flow of a current user session. The next user activity indicating one of continuing the current user session by visiting another web page provided by the web server and ending the current user session. The server dynamically adjusts a content of a web page based on the predicted next user activity.