Morphing advertising to improve online campaign success

__Abstract__ Even though online advertising revenues have grown dramatically, click-through rates for banner advertising continue to decrease, raising hard questions regarding its effectiveness when targeting consumers. However, with the development of a new technique that matches banners to the cognitive style of viewers, the world of online advertising is about to change

A New Method of Measuring Online Media Advertising Effectiveness: Prospective Meta-Analysis in Marketing

The authors introduce a new method, prospective meta-analysis in marketing (PMM), to estimate consumer response to online advertising on a large and adaptive scale. They illustrate their approach in a field study in the U.S., China and the Netherlands, covering equivalent ad content on social media, online video, display banner, and search engines. The authors tested a conceptual framework based on attention and engagement using a technological solution that allow them to observe participants browsing and clicking activity in depth from their own residences, offices, or places of choice to use the tested media platforms, e.g., Facebook, Weibo, Google, Baidu and others. The authors show how consumers respond differently to the same ad depending on how distant they are from purchase, and uncover which channels are most appropriate to which user at different stages of the funnel. They also show how engagement and attention strengthen consumer response to advertising. The authors show how PMM produces exploratory findings, confirmatory findings, and replications by systematically organizing the incremental exploration of complex phenomena with cycles of discovery and validation

Website Morphing 2.0: Technical and Implementation Advances and a Field Experiment

Website morphing infers latent customer segments from clickstreams and then changes websites' look and feel to maximize revenue. The established algorithm infers latent segments from a preset 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

Consumer Response to Social Media and Online Video Advertising

This paper investigates the effectiveness of social media advertising and online video advertising using three large-scale controlled field experiments in the U.S., China and the Netherlands. The study was implemented using a technological approach that allows researchers to combine controls with real-world validity. The studies are based on an adaptive-experimentation method that allows across-sample and within-sample adaptation. This method facilitates incremental exploration of complex phenomena with cycles of discovery and validation across multiple studies. For the real-world stimuli in the automobile industry, the results show that advertising effectiveness of online videos is consistently stronger than social media and that the relative strength of social media increases the further temporally a consumer is from purchasing. We also find that attention strengthens consumer response to online video and social media advertising, but when both are viewed simultaneously at high levels of attention, they become substitutes

Morphing banner advertising

Researchers and practitioners devote substantial effort to targeting banner advertisements to consumers, but they 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 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-more than 100,000 consumers viewed more than 450,000 banners on CNET.com. On relevant Web pages, CNET's clickthrough rates almost doubled relative to control banners. We supplement the CNET field test with an experiment on an automotive information-and-recommendation 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