Ranmit Pantle
PhD Candidate, Marketing, Kellogg School of Management
Contact Information
Northwestern University
2211 Campus Drive
Research Interests
Consumer Search, Online Rankings, Platform Design, Platform Steering and Market Regulation, Quantitative Marketing
Work in Progress
Enable or Overwhelm? Optimal Pre-Search Information on Platform List Pages
Abstract: Online platforms must decide how much product information to display upfront on the list page versus defer to the detailed page. While conventional search models
assume consumers can effortlessly process all available pre-search information, behavioral evidence suggests that too much information can lead to overload and suboptimal choices.
This paper examines how the quantity of pre-search information affects consumer decision quality and search behavior. Using an incentivized lab experiment, I show that displaying
more irrelevant attributes impairs performance—even when relevant information remains constant. Significantly, some consumers also change their search strategies, relying on fewer
attributes under information-dense conditions, leading them to search lower-value options. To explain this, I propose and estimate a two-stage search model in which consumers
strategically select how many attributes to evaluate, trading off cognitive effort against the benefits of more informed search. Simulations based on the model reveal that the optimal
amount of upfront information depends critically on the consumer base: more information benefits niche consumers with low evaluation costs, but harms mainstream users prone to
overload. The results offer guidance for platform design and personalization – highlighting when more information helps, and when it hinders.
Consumer Inferences from Product Rankings: The Role of Beliefs in Search Behavior
with Jessica Fong and Olivia Natan. R&R, Management Science
Abstract: In online markets, consumers tend to search and purchase prominently positioned products. We develop an experimental paradigm to distinguish between two mechanisms
driving this behavior: position-specific search costs and beliefs about expected returns to search. Using incentivized lab experiments, we find that both mechanisms exist, and short-term
randomization of rankings alone does not separate the two mechanisms. Failing to account for beliefs leads to biased estimates of search costs and incorrect consumer welfare
predictions under alternative recommendation systems, such as platform self-preferencing. We discuss solutions for estimating unbiased search costs in real world search settings.