Trade-in programs are offered extensively in business-to-business (B2B) markets. The success of such programs depends on well-designed and executed trade-in policies as well as accurate prediction of return flow to support operational decisions. Motivated by a real problem facing a high-tech company, this paper develops methods to segment customers and forecast product returns based on return merchandise authorization information. Noisy, yet proven to be valuable, returned quantity signals are adjusted by taking product characteristics and customer heterogeneity into account, and the resulting forecast outperforms two benchmark strategies that represent the high-tech company's current practice and a widely adopted method in the literature, respectively. In addition, our methods can serve as tools for companies to uncover the root causes of return merchandise authorization discrepancy, monitor and analyze customer behavior, design segment-specific trade-in policies, and evaluate the effectiveness and efficiency of trade-in programs on a continuous basis.
All Science Journal Classification (ASJC) codes
- Strategy and Management
- Management Science and Operations Research