Research

This paper establishes a theoretical equivalence showing that, under the basic assumptions of Independence and Invariance, an optimal sequential search process is observationally equivalent to a partial ranking over all feasible actions throughout the process, thereby introducing a ranking representation of optimal sequential search. This representation yields a simple and unified empirical strategy for implementing sequential search models to extract information from rich clickstream data, which was difficult with the traditional policy-based representation. For the classic Weitzman (1979) model, the proposed approach reduces simulation burden while improving estimation accuracy and ease of implementation. The same strategy extends to a broad class of sequential search settings suited for different data structure and search environments, including partially observed search processes and multi-stage information acquisition, such as sequential search with discovery. Overall, our results enhance the tractability and empirical applicability of sequential search models.

One of the most invoked assumptions in economics is that consumers know their preferences when making choices. Although theories and experiments in psychology and behavioral economics suggest that this may be unrealistic, there is relatively little evidence from the field on this question. In this paper, we use detailed clickstream data from a large Central Asian online platform to study the extent to which consumers learn about their preferences while searching for a smartphone. To quantify the speed at which this takes place and account for other factors, most notably that consumers obtain additional product information when they inspect product pages, we estimate a rich search model in which consumers learn about their willingness to pay each time they visit the checkout page. Consumers initially underestimate their price sensitivity and update it along the way. Taking this into account shows that consumers are more price sensitive than a standard search model would predict, and an intervention that prompts consumers to end their search early can lead to potential welfare loss.

We develop a structural sequential search model that incorporates imperfect recall of information acquired during search. Imperfect recall naturally arises in search environments and limits consumers’ ability to make optimal purchase decisions. We identify imperfect recall using consumers’ revisit actions, which allow them to reacquire decayed information, and estimate the model using rich clickstream data from a large online smartphone marketplace. Our results show that imperfect recall substantially influences both consumers' search process and purchase outcomes. Counterfactual simulations indicate that modest reductions in revisit costs, via mechanisms such as bookmarking, comparison tools, or retargeting prompts, can meaningfully reduce suboptimal purchases. These findings highlight the role of imperfect recall in consumer search and provide guidance for interventions that improve consumers' decision quality.