Matthijs R. Wildenbeest

Consumer Search and Prices in the Automobile Market

In many markets consumers have imperfect information about the utility they derive from the products that are on offer and need to visit stores to find the product that is the most preferred. This paper develops a discrete-choice model of demand with optimal consumer search. Consumers first choose which products to search; then, once they learn the utility they get from the searched products, they choose which product to buy, if any. The set of products searched is endogenous and consumer specific. Therefore imperfect substitutability across products does not only arise from variation in their characteristics but also from variation in the costs of searching them. We apply the model to the automobile industry. Our search cost estimate is highly significant and indicates that consumers conduct a limited amount of search. Estimates of own- and cross-price elasticities are lower and markups are higher than if we assume consumers have full information.

Prices and Heterogeneous Search Costs

We study price formation in a model of consumer search for differentiated products when consumers have heterogeneous marginal search costs. We provide conditions under which a symmetric Nash equilibrium exists and is unique. Search costs affect two margins—the intensive search margin (or search intensity) and the extensive search margin (or the decision to search rather than to not search at all). These two margins affect the elasticity of demand in opposite directions and whether lower search costs result in higher or lower prices depends on the properties of the search cost density. When the search cost density has the increasing likelihood ratio property (ILRP), the effect of lowering search costs on the intensive search margin has a dominating influence and prices decrease. By contrast, when the search cost density has the decreasing likelihood ratio property (DLRP), the effect on the extensive search margin is dominant and lower search costs result in higher prices. We compare these results with those obtained when consumers have heterogeneous fixed search costs.

Search Engine Optimization: What Drives Organic Traffic to Retail Sites?

The lions share of retail traffic through search engines originates from organic (natural) rather than sponsored (paid) links. We use a dataset constructed from over 12,000 search terms and 2 million users to identify drivers of the organic clicks that the top 759 retailers received from search engines in August 2012. Our results are potentially important for search engine optimization (SEO). We find that a retailers investments in factors such as the quality and brand awareness of its site increases organic clicks through both a direct and an indirect effect. The direct effect stems purely from consumer behavior: The greater the brand equity of an online retailer, the greater the number of consumers who click its link, rather than a competitor in the list of organic results. The indirect effect stems from our finding that search engines tend to place better-branded sites in better positions, which results in additional clicks, since consumers tend to click links in more favorable positions. We also find that consumers who are older, wealthier, conduct searches from work, use fewer words, or include a brand name product in their search are more likely to click a retailers organic link following a product search. Finally, the brand equity of a retail site appears to be especially important in attracting organic traffic from individuals with higher incomes. The beneficial direct and indirect effects of an online retailers brand equity on organic clicks, coupled with the spillover effects on traffic through other online and traditional channels, leads us to conclude that investments in the quality and brand awareness of a site should be included as part of an SEO strategy.

What's in a Name? Measuring Prominence, and Its Impact on Organic Traffic from Search Engines

Organic product search results on Google and Bing do not systematically include information about seller characteristics (e.g., feedback ratings and prices). Consequently, it is often assumed that a retailer’s organic traffic is driven by the prominence of its position in the list of search results. We propose a novel measure of the prominence of a retailer’s name, and show that it is also an important predictor of the organic traffic retailers enjoy from product searches through Google and Bing. We also show that failure to account for the prominence of retailers’ names—as well as the endogeneity of retailers’ positions in the list of search results—significantly inflates the estimated impact of screen position on organic clicks.

Search with Learning

This paper provides a method to estimate search costs in an environment in which consumers are uncertain about the price distribution. Consumers learn about the price distribution by Bayesian updating their prior beliefs. The model provides bounds on the search costs that can rationalize observed search and purchasing behavior. Using individual-specific data on web browsing and purchasing behavior for electronics sold online we show how to use these bounds to estimate search costs. Estimated search costs are sizable and are found to relate to consumer characteristics in intuitive ways. The model outperforms a standard sequential search model in which the price distribution is known to consumers.

Search With Learning for Differentiated Products: Evidence from E-Commerce

This article provides a method to estimate search costs in a differentiated product environment in which consumers are uncertain about the utility distribution. Consumers learn about the utility distribution by Bayesian updating their Dirichlet process prior beliefs. The model provides expressions for bounds on the search costs that can rationalize observed search and purchasing behavior. Using individual-specific data on web browsing and purchasing behavior for MP3 players sold online we show how to use these bounds to estimate search costs as well as the parameters of the utility distribution. Our estimates indicate that search costs are sizable. We show that ignoring consumer learning while searching can lead to severely biased search cost and elasticity estimates.

On the identification of the costs of simultaneous search

This paper studies the identification of the costs of simultaneous search in a class of (portfolio) problems studied by Chade and Smith (2006). We show that aggregate data from a single market, or disaggregate data from a single market segment, do not provide sufficient information to identify the costs of simultaneous search in any reasonable interval. We then show that by pooling aggregate data from multiple markets, or disaggregate data from multiple market segments, the econometrician can identify the costs of simultaneous search in a non-empty interval. Within the context of specific examples, we illustrate that identification of the search cost distribution in its full support may easily be obtained.