The Value of “Bespoke”: Demand Learning, Preference Learning, and Customer Behavior

“Bespoke,” or mass customization strategy, combines demand learning and preference learning. We develop an analytical framework to study the economic value of bespoke systems and investigate the interaction between demand learning and preference learning. We find that it is possible for demand learning and preference learning to be either complements or substitutes, depending on the customization cost and the demand uncertainty profile. They are generally complements when the personalization cost is low and the probability of having high demand is large. Contrary to usual belief, we show that higher demand uncertainty does not necessarily yield more complementarity benefits. Our numerical study shows that the complementarity benefit becomes weaker when customers are more strategic. Interestingly, the substitute loss can occur when the personalization cost is small and the probability of having high demand is large, when customers are strategic. The online supplement is available at https://doi.org/10.1287/mnsc.2017.2771 . This paper was accepted by Serguei Netessine, operations management.

• 저널Management Science · 64(7) · 3129–3145
• 토픽Supply Chain and Inventory Management · Management Information Systems
• DOI10.1287/mnsc.2017.2771
• 저자Tingliang Huang, Chao Liang, Jingqi Wang