Abstract Morphing enables a website to learn (actively and near optimally) which banner advertisements to serve to each cognitive-style segment in order to maximize outcome measures such as click-through, brand consideration, or purchase. Consumer segments are identified automatically from consumers' clickstream choices. Morphing works best on high-traffic websites with tens of thousands of visitors because large samples are necessary to reach steady state optimally. This paper describes the first large-sample random-assignment field test of banner morphing -over 100,000 consumers viewing over 450,000 banners on CNET.com. (Previously published morphing evaluations evaluated morphing website characteristics and were based on predictive simulations using only priming-study data.) On relevant webpages, CNET's clickthrough rates almost double relative to control banners. We supplement the CNET field test with a focused experiment on an automotive information-and-recommendation website. The focused experiment replaces automated learning with a longitudinal design which tests the premise of morph-to-segment matching. Banners matched to cognitive styles, as well as the stage of the consumer's buying process and body-type preference, significantly increase click-through rates, brand consideration, and purchase likelihood relative to a control
