Optimization of a Dual-Channel Retailing System with Customer Returns

A plethora of retailers have begun to embrace a dual-channel retailing strategy wherein items are provided to consumers through both an online store and a physical store. As a result of standards and competitive measures, many retailers provide buyers who are unhappy with their purchases with the ability to achieve a full refund. In a dualchannel retailing system, full reimbursements can be done through what is called a crosschannel return, when a buyer purchases a product from an online store and returns it to a physical store. They can also be done through what is called a same-channel return, when a buyer purchases a product from a physical store and returns it back to the physical store, or purchases a product from an online store and returns it back to the online store. No existing research has examined all common types of customer returns in the context of a dual-channel retailing system. Be notified that the practice of cross-returning an item purchased from the physical store back to the online store is not common. Thus, it is not considered in this dissertation. We first study the optimal pricing policies for a centralized and decentralized dual-channel retailer (DCR) with same- and cross-channel returns. We consider two factors: the dual-channel retailer’s performance under centralization with unified and differential pricing schemes, and the dual-channel retailer’s performance under decentralization with the Stackelberg and Nash games. How dual-channel pricing behaviour is impacted by customer preference and rates of customer returns is discussed. In this study, a channel’s sales requests is a linear function of a channel’s own pricing strategy and a cross-channel’s pricing strategy. The second problem is an extension of the first problem. The optimal pricing policies and online channel’s responsiveness level for a centralized and decentralized dual-channel retailer with same- and cross-channel returns are studied. Indeed, the online store is normally the distribution centre of the enterprise and is not limited to the customers in its neighbourhood. Also, the online store experiences a much higher return rate compared to the physical store. Thus, it has the capability and the need to optimize its responsiveness to customer returns along with its pricing strategy. A channel’s sales requests, in the second problem, is a linear function of a channel’s own price, a crosschannel’s price, and the online store’s responsiveness level. The third problem studies the dilemma of whether or not to allow unsatisfactory online purchases to be cross-returned to the physical store. If not properly considered, those returns may create havoc to the system and a retailer might overestimate or underestimate a channel’s order quantity. Therefore, we study and compare between four vi different strategies, and propose models to determine optimal order quantities for each strategy when a dual-channel retailer offers both same and cross-channel returns. Several decision making insights on choosing between the different cross-channel return strategies and some properties of the optimal solutions are presented. From the retailer’s perspective of outsourcing the e-channel’s management to a third party logistics and service provider, we finally study three different inventory strategies, namely transaction-based fee, flat-based fee, and gain sharing. For each strategy, we find both channels’ optimal inventory policies and expected profits. The performances of the different strategies are compared and the managerial insights are given using analytical and numerical analysis. Methodologies, insights, comparative analysis, and computational results are delivered in this dissertation for the above aforementioned problems

Shipping Policies of Omnichannel Retailers: The Effect of Shipping Fees on Demands and Profitability

The omnichannel strategy that integrates online channels and traditional brick-and-mortar stores has been broadly utilised in retailing practices to deliver a seamless shopping experience. Early empirical studies have demonstrated its benefits: for physical stores, it brings footfall and increases the opportunity of cross-sells; for online channels, it allows customers to inspect the merchandise before purchase, thereby reducing the return rate. However, the relevant operating costs and the investment of channel integration can be substantial, yet the discussion on the pricing of omnichannel services is scarce. This thesis aims to provide insights to address the dilemmas omnichannel retailers face in the pre-purchase and post-purchase stages and identify the optimal pricing of omnichannel shipment. In the ex-ante stage, omnichannel retailers face a dilemma: charging omnichannel service at a low price could attract online traffic, yet financial loss may occur when stores are less profitable and integration costs outweigh cross-sales in-store. Hence, a stylised model is developed to study the shipping policy, especially the shipping fee for omnichannel service, and their impacts on customer demand and overall profitability. Three scenarios are considered: (1) shipment fee is consistent across channels; (2) omnichannel service is charged at a discounted rate or (3) free of charge. The results show that omnichannel positively convert online traffic into footfall instore but does not always grow total demands or boost overall profitability; charging a discounted rate could help retailers shift demands to a more profitable channel. This study identifies the optimal shipping policy that depends on the retailer’s operational efficiency and distribution costs. When the distribution cost is low, the retailer can offer free omnichannel shipment or charge a discounted rate if the cost is medium. Finally, the home delivery fee should be adjusted jointly with the omnichannel shipping fee. In the ex-post stage, customers need to decide whether and where they return the purchased product. Retailers face a trade-off: allowing cross-channel returns could reduce the shipment cost and potentially increase cross-sales in-store; however, handling returned products in-store means extra labour costs, such as inspecting, re-packing, re-storing. Moreover, stores potentially face financial loss if the returned product is re-sold in-store at a discounted price. Therefore, the features of omnichannel operations are incorporated in the post-purchase stage. A stylised model is built to characterise omnichannel operations and study how return policies impact customer channel choices and the retailer’s profitability. This study differentiates online channels with stores based on customer return behaviours. When customers purchase online, they need to bear the risk of receiving a product that does not match their expectations. Distinctively, store customers can inspect the product before purchase. Customers are assumed not to return a product if they purchase in-store. Hence, this model focuses on four return policies depending on return fees and whether cross-channel is available. The unit selling price is consistent across channels, and the retailer offers a full refund. In this model setting, purchase decision and return channel decisions are endogenous. The results suggest that cross-channel returns are not recommended if online returns are free, whereas retailers with a larger customer base and efficient in-store operations or wide store networks could benefit from omnichannel returns. Last, the optimal omnichannel return policy should be jointly considered with the existing online return policy. Overall, this thesis extends utility theory in understanding the customer’s cross-channel behaviours and use decision theory to analyse the omnichannel retailer’s service pricing in pre-and postpurchase stages. This analysis helps retailing practitioners to understand the scenario when the retailer should allow omnichannel implementations, such as buy online and collect or return instore, and the condition of optimal shipment pricing

The optimal omnichannel strategy for SMEs apparel retailers

The omnichannel transition took mainly place in big companies within the apparel retailing while in SMEs it was usually performed far less. Multichannel presents a structure of independent different channels, whereas a full channel integration is reached within an omnichannel strategy, which can deliver a seamless customer experience in all touchpoints. Nowadays, customers are more independent than ever, thus the drivers of purchase have changed. Considering this tendency, current academic research about this topic, with the concern of its implementation in smaller apparel retailers, is limited. The thesis aims to analyze the current condition of channel strategies in the apparel retailing, mostly in Italy and Portugal, to build an implementation process for an omnichannel shift for multichannel apparel of SME’s retailers, as well as its main characteristics. Therefore, qualitative research was conducted through the analysis of 14 semi-structured interviews with four different categories in the apparel industry. The findings show that small apparel retailers have not developed this strategy yet because of the fear of uncertainty, lack of consumer knowledge, inefficient warehouse and inventory management, and basic knowledge concerning the topic. Hence, it was concluded that omnichannel is a customer-oriented strategy in which elements of the marketing mix need to be aligned to guarantee a unified offer regarding communication and availability. This is reachable only through a cloud-based operation system and logistic partnerships. Lastly, SMEs can pursue this transformation by following a seven-step approach that requires long term vision and investments in digitalization.A transacção para estratégias omnicanalizadas deu-se maioritariamente em grandes empresas de retalho de moda, enquanto que a sua performance em PMEs é baixa. As estratégias multicanalizadas estruturam-se em diferentes canais independentes, enquanto a integração total está contida na estratégia omnicanalizada, podendo oferecer uma experiência de cliente perfeita em todos os pontos de contacto. O consumidor de hoje está o mais independente, tendo assim o comportamento de compra mudado. Considerando esta tendência, a investigação no tópico referente à sua implementação em retalhistas de moda mais pequenos é limitada. A presente dissertação tem como objectivo a análise das condições atuais em estratégias no retalho de moda maioritariamente em Itália e Portugal, por forma a construir um processo de implementação de estratégias omnicanal para PMEs multicanalizadas e das respectivas principais características. Como tal, a investigação qualitativa consistiu na análise de 14 entrevistas semi-estruturadas com quatro categorias nesta indústria. Os resultados mostram que os pequenos retalhistas de moda ainda não desenvolveram tal estratégia dada a aversão à incerteza, falta de conhecimento sobre o consumidor, gestão de armazém e inventário ineficientes e conhecimento básico sobre tópico. Consequentemente, concluiu-se que a omnicanalidade é uma estratégia orientada para o cliente, na qual os elementos do marketing mix precisam estar alinhados para garantir uma comunicação e disponibilidade unificadas da oferta. Isto é alcançável por meio de sistemas operacionais cloud-based e de parcerias logísticas. Por fim, as PMEs podem procurar alcançar esta transformação seguindo uma abordagem de sete passos, que requer visão a longo prazo e investimentos em digitalização

Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments

A retailer following a competition-based dynamic pricing strategy tracks competitors' price changes and then must answer the following questions: (1) Should we respond? (2) If so, respond to whom? (3) How much of a response? (4) And on which products? The answers require unbiased measures of price elasticity as well as accurate estimates of competitor significance and the extent to which consumers compare prices across retailers. There are two key challenges to quantify these factors empirically: first, the endogeneity associated with almost any type of observational data, where prices are correlated with demand shocks observable to pricing managers but not to researchers, and second, the absence of competitor sales information, which prevents efficient estimation of a full consumer-choice model. We address the first issue by conducting a field experiment with randomized prices. We resolve the second issue by exploiting the retailer's own and competitors' stockouts as a source of variation to the consumer choice set, in addition to variations in competitors' prices. We estimate an empirical model capturing consumer choices among substitutable products from multiple retailers. Based on the estimates, we propose and test a best-response pricing strategy through a carefully controlled live experiment that lasts five weeks. The experiment documents an 11 percent revenue increase while maintaining a margin above a retailer-specified target.http://deepblue.lib.umich.edu/bitstream/2027.42/116278/1/1265_Li.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116278/2/1265_Li_Dec2015.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116278/6/1265_Li_Feb2016.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116278/8/1265_Li_Aug2016.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116278/10/1265_Li_Nov2016.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116278/12/1265_Li_Jan2017.pdfDescription of 1265_Li_Nov2016.pdf : November 2016 revisionDescription of 1265_Li_Aug2016.pdf : August 2016 updateDescription of 1265_Li_Feb2016.pdf : February 2016 revisionDescription of 1265_Li_Dec2015.pdf : December 2015 revisionDescription of 1265_Li_Jan2017.pdf : January 2017 revisio