This dissertation investigates the coordination between new and used product sales using both empirical and analytical approaches.The first essay studies the potential for cannibalization of new product sales by remanufactured versions of the same product, which is a central issue in managing the secondary market. Practitioners have no fact-based information to guide practice at companies and academics have no studies available to use as the basis for assumptions in models. We address the cannibalization issue by using auctions to determine consumers' willingness-to-pay for both new and remanufactured products. The auctions also allow us to determine the potential impact of offering new and remanufactured products at the same time, which provides us insights into the potential for new product cannibalization. Our results indicate that for the consumer and commercial products auctioned, there is a clear difference in willingness-to-pay for new and remanufactured goods. For the consumer product, there is scant overlap in bidders of the new and the remanufactured product, leading us to conclude that the risk of cannibalization in this case is minimal. For the commercial product, however, there is evidence of overlap in bidding behavior, exposing the potential for cannibalization.Trade-in programs are designed to promote new product sales and give companies control over the secondary market. Motivated by a real problem facing a high-tech company, in the second essay, we develop methods to analyze data from Return Merchandise Authorization (RMA) forms, which contain information such as product number, quantity, and date. To accurately forecast the returned quantity of a product in an RMA, we treat the booked quantity as a signal and adjust its noise by taking product characteristics and customer heterogeneity into account. For our data set, we find that the proposed forecasting strategy that captures both product and customer information outperforms the two benchmark strategies that represent the high-tech company's current practice and a widely-adopted method in the literature, respectively. In addition, our analysis can also serve as a tool for companies to uncover the root causes of RMA reporting errors, to provide valuable insights on effective design of trade-in policies, and to monitor and evaluate the performance of trade-in policies on a continuous basis. Our forecasting methods are currently under a trial implementation in the company and can potentially be applied to similar business settings to extract useful information from noisy, yet valuable signals.In the third essay, we build an analytical model to study the trade-in strategy in the context of a monopolistic manufacturer offering a technology product to a heterogeneous consumer population. We consider an exogenous innovation process that governs the availability of new technology, and therefore a product is subject to two types of value decay: technological obsolescence (due to technology innovation) and functional depreciation (due to wear and tear). The manufacturer chooses the prices for the new products featuring a new and an existing technologies, respectively, and the trade-in credit for the used products featuring a previous-generation technology and an existing technology, respectively, to maximize her long-run average profit. Heterogeneous consumers make their consumption choice accordingly to maximize their utility.We characterize the optimal stationary pricing strategy for the manufacturer and the corresponding optimal consumption strategies that consumers choose based on their willingness-to-pay. We find that manufacturers with different production cost and resell cost structures should leverage trade-in differently to take the full advantage of it. Low production cost and low resell cost manufacturers should offer trade-in programs regardless of innovation state in order to induce the highest-valuation consumers to purchase a new product in every period. However, when production cost and/or resell cost are higher, trade-in programs should be restrained accordingly. We also evaluate the impact of innovation frequency, technological obsolescence, functional depreciation, and production and resell costs on the manufacturer's maximum long-run average profit. Some of the findings are interesting and not so intuitive. In general, technological obsolescence and functional depreciation reduce consumer valuation of used products, which should in turn lower the manufacturer's profitability. However, we identify scenarios under which they can actually increase a manufacturer's expected profit. Moreover, we characterize situations under which higher innovation frequency helps or hurts the manufacturer's bottom line.
