Mapping Online Consumer Search

Abstract

In this paper, we propose a method to visualize online consumer search in so-called product search maps. Manufacturers can use these maps to understand how consumers search for competing products prior to choice, including how information acquisition and product search is organized along brand-, product attributes-, and/or price related search strategies. The product search maps also inform manufacturers about the competitive structure in the industry and about the contents of consumer consideration sets. Our proposed method first defines a product search network, consisting of the products and links that designate if a product is searched conditional on searching other products. We next 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 two-fold. First, we simultaneously visualize the positions of products and the direction of consumer search over products in a perceptual map of “search proximity.” Second, we explain and relate the dimensions of the map using observed product attributes. We empirically apply our approach to consumer search for digital camcorders at Amazon.com and provide a number of managerial implications.