Abstract: Training of a machine learning model can indicate data features within operational data, customer data, and/or worker data that are most predictive of outcome scores, such as customer satisfaction scores, associated with performance of instances of a process by an entity. The training of the machine learning model can also indicate target zones, associated with values of the identified predictive data features, that are associated with outcome scores within a target range. Process data, associated with a set of instances of the process, can be analyzed to identify instances of the process that associated with values of the predictive data features that are, or are not, within the target zones.

Abstract: The present invention generally relates to chips, particularly microfluidic chips, that are rotatable and/or have a generally circular or rotationally symmetric geometry. The chips may be substantially planar in certain instances. In some cases, the chips of the invention can have more than one reaction site, which can, for example, contain cells. The reaction site can be very small, in some cases with a volume of less than about 1 ml. Reactions, transport, and/or other manipulations within the chip can be facilitated by rotating the chip, for example, at tens, hundreds or thousands of revolutions per minute (RPM). In some cases, data may also be written to and/or read from the chip. The chips of the invention can be used, for example, to move fluid from one portion of a chip to another, to concentrate and/or separate a mixture (e.g., a cell suspension), to lyse or fractionate a cell, or the like.