Dynamic Pricing for a Multiproduct Consumer Electronics Trade-in Program

We consider a dynamic pricing problem for a consumer electronics trade-in program where a firm acquires and resells multiple types of preowned (used) products over a finite selling horizon. The trade-in program offers two options: trade in for cash, where customers sell their products to the firm and receive a cash payment, and trade in for upgrade, where customers exchange their products for new products at discounted prices. The firm sets trade-in prices (both cash rewards and new products’ discounts) and resale prices to maximize its total expected profit. Customer arrivals follow independent Poisson processes, and their choices on both used product trade-in and refurbished product purchase follow the multinomial logit model. We develop simple and provably effective heuristic policies based on the solution to a deterministic upper-bound problem. We first propose the static control (SC) policy that sets static prices and show that its profit loss (relative to the optimal profit) is on the order of $O(T^{1/2})$, where T is the number of selling periods, which matches that of the best-possible stationary policy. We then design a dynamic policy called batched-adjustment control (BAC), where the selling horizon is divided into consecutive and disjoint batches, and the prices in one batch are updated based on the realized uncertainties in the previous batch. The profit loss of BAC is on the order of $O(T^{1/3})$. We numerically show that both policies perform well, whereas BAC outperforms SC. We further generalize the policies and performance analysis to (i) a broader class of choice models, (ii) initial stocking decisions of new products (for upgrade purposes), and (iii) other practical features of trade-in programs.

Funding: Z. Zhang is supported by the National Natural Science Foundation of China [Grant 72501237] and Fundamental Research Funds for the Central Universities of Xiamen University [Grant 20720241012]. Y. (M.) Lei is partially supported by the Natural Sciences and Engineering Research Council of Canada [Grants 1378108 and RGPIN-2021-02973]. S. X. Zhou is partially supported by the Hong Kong Research Grant Council GRF [Grant CUHK-14500019], the National Natural Science Foundation of China [Grant 72394395], and the Asian Institute of Supply Chains and Logistics.

Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2022.0268.