Abstract: A multivariate testing platform is disclosed. The platform includes a test definition environment including a test deployment and data collection interface configured to (1) manage test deployments within a retail environment to a controlled subset of users across a plurality of content delivery channels, and (2) receive test data from an analytics server regarding user interaction regarding the test deployment. The test definition environment includes a self-service test definition and analysis tool configured to generate a user interface accessible by enterprise users, the user interface including a test definition interface useable to define a test selectable from among a plurality of test types and an analysis interface graphically displaying test results based on the test data from the analytics server.

Abstract: Apparatus, systems, and methods for monitoring one or more batteries are disclosed. One such apparatus may include a battery management device. The battery management device may include a sensor configured to detect a characteristic of a battery, a processor communicatively connected with the sensor to receive a signal indicative of the characteristic of the battery, a network interface to send the signal to a server through a network and to receive an instruction from the server through the network, and a control circuit to control one or more aspects of the battery based on the received instruction.

Abstract: In various example embodiments, systems and methods for estimating the mean of a dataset having a fat tail. Data sets may be partitioned into components, a “torso” component and a “tail” component. For the “tail” component of the data set a more efficient estimator can be obtained (versus the traditionally calculated mean) by using the tail data to estimate parameters for a specific distribution and then deriving the mean from the estimated parameters. The estimated mean from the torso and the estimated mean from the tail may then be combined to obtain the estimated mean for the full data. This can be applied to gross merchandise bought (GMB) by various samples of visitors and apply the experience that was provided to the sample with the highest GMB to all visitors to increase gross revenue.