Online retail operations with “Try-Before-You-Buy”

Try-Before-You-Buy (TBYB) is an emerging retail strategy of online retailers that allows consumers to have multiple items delivered to them and then determine which items they wish to keep or return for free. This paper addresses the TBYB retailer’s problem in its operations: the decision regarding how many items to send to a consumer to maximize its expected profit when the products are chosen by a retailer recommendation system. To this end, we develop a stylized model of the retailer’s quantity decision by taking into account the consumer’s purchase decision, which is influenced by the consumer’s budget constraint and the retailer’s recommendation accuracy. We derive the retailer’s optimal quantity decision and discuss how it is determined. We also examine how the retailer’s optimal quantity decision and corresponding profit are influenced by two factors reflecting the TBYB industry: recommendation accuracy and new consumer influx. Regarding recommendation accuracy, our results show that a minimum level of accuracy exists, which acts as an entry barrier to TBYB retail. Once the retailer’s accuracy exceeds the threshold, we show that the relationship between the accuracy and the optimal quantity is characterized as an inverted-U relationship. Regarding the latter, interestingly, we find that an influx of new consumers may hurt the retailer’s profit even when the accuracy applied to them is above the entry barrier.