Optimal Feature Advertising Design Under Competitive Clutter

This study investigates consumers' attention to retail feature ads and proposes a method to optimize the design of the ads. Utilizing a large dataset of consumers' attention to over 1,100 individual feature ads collected with eye-tracking technology, we analyze the effects of the five key design elements of feature ads--brand, text, pictorial, price, and promotion--on consumers' attention to them. Attention is measured in terms of selection and gaze duration. We focus on the effects of the surface sizes of the design elements. A key feature of our model is that it takes into account the impact of visual clutter in the ad display page. To capture the clutter effects, we propose two new entropy-based measures that characterize the salience of feature ads in their competitive environment based on Attention Engagement Theory. In a Bayesian framework, we simultaneously estimate the parameters of the model and optimize the design of feature ads in terms of surface sizes of the five design elements. Our optimization results and comparisons with alternative design approaches indicate that significant improvements in attention to feature advertising can be achieved without increase in costs, and that the resultant optimal feature ad designs create win-win opportunities for manufacturers and retailers.