In many markets, consumers visit stores and physically inspect products before making purchase decisions. We view the inspection of a product at a retail location as a search for product fit. We quantify the cost and benefit from searching for product fit using a discrete choice model of demand with optimal sequential search. In these models, the benefit of searching is measured by the standard deviation of the product fit and has, heretofore, been fixed to one in estimation. We show that, with an exogenous search cost shifter, both the cost and benefit of searching can be separately estimated. Our empirical setting is the U.S. automotive market. We assemble a unique data set containing individual-level smartphone geolocation data that inform us about dealership visits. We also obtain information on new vehicle purchases from proprietary DMV registration data. Our exogenous cost shifter is the distance a consumer must travel to visit a dealership. Our results show that the benefit provided by dealerships to consumers is substantial. Within our empirical context, failure to estimate the standard deviation of the product fit leads to biased search cost and consumer surplus estimates and to inaccurate predictions regarding consumers’ number of searches and effects of at-home test drive programs.

在许多市场中，消费者在做出购买决定之前会去商店并实际检查产品。我们将在零售场所对产品进行检查视为对产品适合性的搜索。我们使用离散的需求选择模型和最佳顺序搜索来量化搜索产品适合性的成本和收益。在这些模型中，搜索的好处是通过产品拟合的标准偏差来衡量的，并且迄今为止，已将其固定为一个估计值。我们证明，使用外源搜索成本转移器，成本和搜索的好处可以单独估算。我们的经验是美国汽车市场。我们组装了一个独特的数据集，其中包含个人级别的智能手机地理位置数据，这些数据可告知我们有关经销商访问的信息。我们还从专有DMV注册数据中获取有关购买新车的信息。我们的外在成本转移者是消费者访问经销商所必须经过的距离。我们的结果表明，经销店为消费者提供的利益是巨大的。在我们的经验背景下，未能估算出产品适合度的标准偏差会导致搜索成本和消费者剩余估算值有偏差，并导致有关消费者搜索次数和家庭试驾计划效果的预测不准确。