Recurrent neural network for solving several novel sales models: Try-Before-You-Buy

E-commerce, as a dominant business pattern that integrates online shopping with offline sales, has gained more and more popularity recently. Although this kind of business scheme brings great convenience for daily life, the product fit uncertainty leads to an increase in return rates. This paper first introduces several novel sales models based on different consumer groups including normal purchase model, Try-Before-You-Buy model and group buying under Try-Before-You-Buy model. More specifically, normal purchase model serves as an effective promotional strategy, attracting new customers and generating excitement around products. Try-Before-You-Buy model allows consumers to experience products before purchase and is helpful in finding more potential consumers. Through this model, not only can consumers take advantage of bulk discounts, but also helps retailers anticipate demand more accurately. These business models also introduce challenges such as increased shipping costs, depreciation of returned products, and the emergence of speculators. Then, the above three models are abstracted as related constrained optimization problems. After that, the recurrent neural network approach is applied to solve the constrained optimization problems. Numerical examples are given to show the effectiveness of the proposed neural network.