New Search-and-Choice Model Improves Understanding of Online Shopping


“Consumers that viewed item j also viewed items j.” Anyone who regularly shops at popular online sites such as Amazon.com, Target.com and Staples.com, will be familiar with this feature, along with “consumers that viewed item j ultimately purchased items j”. Availability of such online consumer data provides new ways for marketers to better understand consumer decisions in a variety of product categories and to use them for better decision making.

Jun B. Kim, Paulo Albuquerque and Bart J. Bronnenberg set out to analyze consumer choice and pre-choice browsing behaviors in a single unified framework. They proposed a theory-based empirical model that characterizes consumer optimal sequential search and choice decisions in a costly search environment (a “sequential search” is a search for information that allows consumers to compare and evaluate each item in a list, one after the other, while a “search cost” involves the time, energy, and money expended by a consumer researching for a product). On the basis of the premise that consumers look for information to fully resolve match values about products in a costly search environment, they conceptualized search sets as the outcome of an optimal sequential search process, with consumers making their choices from the resulting set of alternatives.

The researchers applied their model to search and choice data from Amazon.com, estimated consumer demand, and offer various policy simulations that pertain to substitution patterns and market structure in the digital camcorder industry. To characterize a product, they included eight product characteristics: brand name, media format, form factor, high definition, zoom, number of pixels, and price.

From manager’s perspective, they offer a valuable toolkit for manufacturers with their product portfolio management decision. To that end, they first predicted how consumers substituted different products when manufacturers increased prices and when they withdrew products from their product lines. Companies can use the first simulation to identify competing products and the second simulation as an impetus to product line management. As an illustrative case, they used the proposed model and make recommendations for Sony to potentially streamline its product portfolio. This was a very meaningful exercise since because Sony had recently announced its decision to scale down its operations in many consumer electronics categories, including digital cameras. In addition, their market structure analysis helps managers understand and obtain essential insights regarding the price competitiveness of each brand in the category.

The study makes a number of contributions to the literature on consumer information search and aggregate demand models. Methodologically, their model explains both search and choice decisions, whereas the former models searched decisions only. Most importantly, the model offers decision aid tool for manufacturers with its product management using publically available data. This will be a cost-effective way for manufacturers to study their product categories.