Managing Trade-In Programs Based on Product Characteristics and Customer Heterogeneity in Business-to-Business Markets

Abstract

Trade-in programs are offered extensively in business-to-business (B2B) markets. The success of such programs depends on accurate prediction of return flow characteristics. Motivated by a real problem facing a high-tech company, this paper first develops a method to analyze data from Return Merchandise Authorization (RMA) forms, which contain information such as booked and returned quantities and dates of each trade-in product. To provide accurate forecast of returned quantities in a given time window, we treat booked quantity information from RMAs as signals and adjust the noise of the signals by taking product characteristics and customer heterogeneity into account. We compare three forecasting strategies: Strategy 1 utilizes product characteristics, Strategy 2 considers customer heterogeneity, and Strategy 3 incorporates both. Our results show that both product and customer information from RMAs helps to improve forecast accuracy. From the management standpoint, our results emphasize the importance of understanding product portfolios, monitoring and segmenting customers based on their historical RMA accuracy, promoting responsible customer conducts, and enforcing terms specified in trade-in program policies.

Keywords: empirical research, trade-in program, signal-based forecast, count regression models, customer segmentation, substitutable and complementary products