Our research empirically analyzes consumer behavior at Internet shopbots— sites that allow consumers to make “one-click” price comparisons for product offerings from multiple retailers. By allowing researchers to observe exactly what information the consumer is shown and their search behavior in response to this information, shopbot data has unique strengths for analyzing consumer behavior. Furthermore, the method in which the data is displayed to consumers lends itself to a utility-based evaluation process, consistent with econometric analysis techniques.

While price is an important determinant of customer choice, we find that, even among shopbot consumers, branded retailers and retailers a consumer visited previously hold significant price advantages in head-to-head price comparisons. We also find that these models accurately predict consumer behavior out of sample, suggesting that our analyses effectively capture relevant aspects of consumer choice processes and can form a useful basis for understanding consumer behavior and leveraging this understanding to strategic advantage.