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

Tingliang Huang (), Chao Liang () and Jingqi Wang ()
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Tingliang Huang: Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467
Chao Liang: Cheung Kong Graduate School of Business, Beijing 100738, China
Jingqi Wang: Faculty of Business and Economics, University of Hong Kong, Pokfulam, Hong Kong

Management Science, 2018, vol. 64, issue 7, 3129-3145

Abstract: “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.

Keywords: operations–marketing interface; customization; demand forecast; customer behavior (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:64:y:2018:i:7:p:3129-3145

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