MI 48084-5353 (formerly Technical Fellow at General Motors Research), [email protected]. John R. Hauser is the Kirin Professor of Marketing

Abstract Researchers and practitioners devote substantial effort to targeting banner advertisements, but less effort on how to communicate with consumers once targeted. Morphing enables a website to learn (actively and near optimally) which banner advertisements to serve to each cognitive-style segment in order to maximize click-through, brand consideration, and purchase. Cognitive-style segments are identified automatically from consumers' clickstreams. 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. On relevant webpages, CNET's click-through rates almost doubled relative to control banners. We supplement the CNET field test with a focused experiment on an automotive information-andrecommendation website. The focused experiment replaces automated learning with a longitudinal design to test 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. Together the field and the focused experiments demonstrate that matching cognitive styles provide significant benefits above and beyond more-traditional targeting. Such improved banner effectiveness has strategic implications for allocations among media

MI 48084-5353 (formerly Technical Fellow at General Motors Research), [email protected]. John R. Hauser is the Kirin Professor of Marketing

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