Associate Professor of Marketing
Brand Networks; Asymmetric Multidimensional Scaling; Product Search; Hierarchical Bayes Estimation ;
The authors propose a new method to visualize browsing behavior in so-called product search maps. Manufacturers can use these maps to understand how consumers search for competing products before choice, including how information acquisition and product search are organized along brands, product attributes, and price-related search strategies.The product search maps also inform manufacturers about the competitive structure in the industry and the contents of consumer consideration sets. The proposed method defines a product search network, consisting of the products and links that designate whether a product is searched conditional on searching other products.The authors model this network using a stochastic, hierarchical, and asymmetric multidimensional scaling framework and decompose the product locations as well as the product-level influences using product attributes. The advantages of the approach are twofold.First, the authors simultaneously visualize the positions of products and the direction of consumer search over products in a perceptual map of search proximity. Second, they explain the formation of the map using observed product attributes.The authors empirically apply their approach to consumer search of digital camcorders at Amazon.com and provide several managerial implications.