Management Science PhD student Can Küçükgül earned second place in the 2020 Jeff McGill Student Paper Award competition of the INFORMS Revenue Management and Pricing Section, an honor announced at the Virtual 2020 INFORMS Annual Meeting last November.

Enrolled in the Operation Management Concentration, Küçükgül wrote his winning paper, “Engineering Social Learning: Information Design of Time-Locked Sales Campaigns for Online Platforms,” with his PhD advisors, Dr. Özalp Özer, the George and Fonsa Brody Professor in Operations Management, and Dr. Shouqiang Wang, an assistant professor of operations management.

The paper notes that online retail platforms, such as Amazon, have opened the marketplace to independent vendors, allowing them to sell products to a broad range of customers. One prominent application of these platforms is a time-locked sales campaign. Such a campaign allows vendors to sell their products at a set price for a specified length of time, from a few hours to a few days.

The trouble, though, is that absent showrooms, “customers face uncertainty about the value of products, deterring them from making the purchase, particularly in the case of new products, products with nuanced features, or products that cater to a niche market.”

To overcome customers’ uncertainty and maximize sales in a limited time period, platforms offer prospective customers information about previous customers’ purchase decisions. The idea is the notion of social learning, that is, customers observe historical purchase decisions and update their evaluation of the product.

Küçükgül and the professors set themselves the task of determining: What is the platform’s best strategy to provide information during a time-locked campaign?

“In essence,” they highlighted, “this problem is one of engineering social learning: strategically providing information about historical purchase decisions to influence future customers’ product evaluation.”

The paper uncovers the platform’s fundamental trade-off in determining the optimal provision strategy: long-term information generation versus short-term revenue extraction. Ultimately, the authors suggest the platforms focus on collecting customers’ purchase decisions in the early phase of their campaigns; then, in the later phase, use this accumulated information for making more credible purchase recommendations. This strategy provides optimal information disclosure that both increases platform revenue and enables customers to make informed purchase decisions, resulting in purchase satisfaction.

Watch Küçükgül providing a detailed review of his study at https://www.youtube.com/watch?v=iGu3Sno2qkc.