Virtual advisors often increase sales for those customers who find such online advice to be convenient and helpful. However, other customers take a more active role in their purchase decisions and prefer more detailed data. In general, we expect that websites are more preferred and increase sales if their characteristics (e.g., more detailed data) match customers' cognitive styles (e.g., more analytic). “Morphing” involves automatically matching the basic “look and feel” of a website, not just the content, to cognitive styles. We infer cognitive styles from clickstream data with Bayesian updating. We then balance exploration (learning how morphing affects purchase probabilities) with exploitation (maximizing short-term sales) by solving a dynamic program (partially observable Markov decision process). The solution is made feasible in real time with expected Gittins indices. We apply the Bayesian updating and dynamic programming to an experimental BT Group (formerly British Telecom) website using data from 835 priming respondents. If we had perfect information on cognitive styles, the optimal “morph” assignments would increase purchase intentions by 21%. When cognitive styles are partially observable, dynamic programming does almost as well—purchase intentions can increase by almost 20%. If implemented system-wide, such increases represent approximately $80 million in additional revenue.Internet marketing, cognitive styles, dynamic programming, Bayesian methods, clickstream analysis, automated marketing, website design, telecommunications

Researchers and practitioners devote substantial effort to targeting banner advertisements to consumers, but focus less effort on how to communicate with consumers once targeted. Morphing enables a website to learn, automatically and near optimally, which banner advertisements to serve to consumers in order to maximize click-through rates, brand consideration, and purchase likelihood. Banners are matched to consumers based on posterior probabilities of latent segment membership, which are identified 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 an experiment on an automotive information-andrecommendation website. The automotive experiment replaces automated learning with a longitudinal design that implements 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. The CNET field test and automotive experiment demonstrate that matching banners to cognitive-style segments is feasible and provides significant benefits above and beyond traditional targeting. Improved banner effectiveness has strategic implications for allocations of budgets among media.

January 2014Website morphing infers latent customer segments from clickstreams then changes websites' look and feel to maximize revenue. The established algorithm infers latent segments from a pre-set number of clicks and then selects the best "morph" using Expected Gittins' Indices. Switching costs, potential website exit, and all clicks prior to morphing are ignored.We model switching costs, potential website exit, and the (potentially differential) impact of all clicks to determine when to morph for each customer. Morphing earlier means more customer clicks are based on the optimal morph; morphing later reveals more about the customer's latent segment. We couple this within-customer optimization to between-customer Expected Gittins' Index optimization to determine which website "look and feel" to give to each customer at each click. We evaluate the improved algorithm with synthetic data and with a proof-of-feasibility application to Japanese bank card-loans. The proposed algorithm generalizes the established algorithm, is feasible in real time, performs substantially better when tuning parameters are identified from calibration data, and is reasonably robust to misspecification. Keywords:Automated Bayesian inference of latent cognitive-style segments with dynamic programming optimization to match website designs to customers. Bayesian inference on a customer's clickstream infers probabilities that the customer belongs to the latent segments. Using these probabilities and data from past purchases, the dynamic program automatically selects the best look and feel for the website for each customer. The "morph" assignment is (near) optimal in the sense that it balances learning about the best assignment for a segment with the profit that can be obtained by exploiting current knowledge of morph-to-segment purchase probabilities. HULB use data from a "calibration study" to simulate what would had happened had the BT Group implemented morphing on its broadband-sales website. HULB estimate that morphing would have increased revenue by $80M.Subsequently, Urban, Liberali, MacDonald, Bordley, and Hauser (2014) [hereafter Urban, et al.] adapted morphing to banner advertising. The only modification in HULB's algorithm was to account for multiple customer visits to the same website. In a field test with over 100,000 customers viewing over 450,000 banners on a CNET website, they report that banner morphing almost doubled click-through rates relative to a random assignment of banners. They also conducted a laboratory test on an automotive information-and-recommendation website to test the basic concept of morph-to-segment matching. The experiment replaced the automated algorithm with direct measurement in a 4-5 week longitudinal study.Click-through rates on banners, as well as consideration and preference for Chevrolet-branded vehicles, increased significantly when morphs were matched to customer segments. The Chevrolet application expanded the definitions of customer segments to include the stage of the automoti...