The value of crowdsourcing in apparel and fashion retail buying
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI SYSTEM AT REQUEST OF AUTHOR.] Apparel and fashion retail buyers are responsible for selecting and ordering products, on behalf of the retailer, to be sold to retailers' end-consumers. Compared to their counterparts in other retailing sectors, fashion buyers face unique challenges such as high demand uncertainty and volatility, seasonality, frequent changes in fashion trends, and short product life cycles. As previous trends rarely provide useful information for predicting future sales of trendy products, fashion buyers make a subjective assessment of products' future demand by relying on their intuition and perceived expertise. Industry reports show that fashion buyers' predictions are often far from the demand that is later realized causing loss of profits for retailers. In this dissertation. I argue that retailers can benefit from the Wisdom of the Crowd (WOC) in predicting future sales of fashion products. It is suggested that lay customers as a group can provide more accurate prediction of future demand of fashion products than individual fashion buyers. An empirical study is conducted to test this proposition involving two groups: professional fashion buyers (N=60) and lay customers (N=397). Customers predicted future sales of products in all six product categories that were used in the study. The prediction error, measured by MAPE, was reduced between 12 and 73 percent. The implication of these findings for retailers are discussed, and directions for implementing crowdsourcing in fashion buying to improve prediction accuracy are provided.
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