Dynamic Online and Offline Channel Pricing for Heterogeneous Customers in Virtual Acceptance

We consider a manufacturer's dual distributions channels consisting on the one hand of a virtual (online) channel operated directly by a manufacturer and on the other hand of a real (offline) channel operated by an intermediate retailer. Customers are assumed heterogeneous in their virtual acceptance, deriving a surplus according to the channel they shop at. Assuming that customers' derived benefits are random with a known probability distribution, we obtain a probabilistic model, which is used to construct an inter-temporal model for shopping online. In addition, we suppose that the retailer uses a markup pricing strategy and has a strategic role. This results in a Stackleberg differential game where the manufacturer is leader and the retailer is a follower. The optimal policy shows that the manufacturer charges the same price across both channels. This finding is consistent with classical results in economics. However, our research goes beyond this observation and indicates that the online price, the retailer's markup and the probability to buy are affected by consumers' heterogeneity in a specific manner. Moreover, we show that while the retailer sets a price equal to the product value, the online price is lower and is equal to the product value less the guarantee provided by the manufacturer for the risk the customer take to buy online. This guarantee is not discriminating and is set to the risk of the customer with the lowest virtual acceptance. Finally, we show that the introduction of the online store is a win-win strategy; both the customers and the manufacturer are better off.

[1]  S. Balasubramanian,et al.  Managing Channel Profits: The Role of Managerial Incentives , 2005 .

[2]  R. T. Moriarty,et al.  Managing hybrid marketing systems. , 1990, Harvard business review.

[3]  R. Wigand,et al.  Electronic Markets and Virtual Value Chains on the Information Superhighway , 1995 .

[4]  C. Tapiero Applied Stochastic Models and Control for Finance and Insurance , 1998 .

[5]  Sungbaik Oh A multi-period investigation of agency costs of debt and the impact of bond indenture provisions , 1985 .

[6]  Paul M. Griffin,et al.  Supplier- and buyer-driven channels in a two-stage supply chain , 2002 .

[7]  Richard J. Tersine,et al.  Economic purchasing strategies for temporary price discounts , 1995 .

[8]  W. D. Ray Managerial Planning: An Optimum and Stochastic Control Approach , 1979 .

[9]  Rajiv Sabherwal,et al.  Determinants Of Retail Electronic Purchasing: A Multi-Period Investigation1 , 2002 .

[10]  Prakash L. Abad,et al.  Supplier pricing and lot sizing when demand is price sensitive , 1994 .

[11]  Serguei Netessine,et al.  Supply Chain Structures on the Internet: Marketing-Operations Coordination , 2000 .

[12]  M. Parlar,et al.  DISCOUNTING DECISIONS IN A SUPPLIER-BUYER RELATIONSHIP WITH A LINEAR BUYER'S DEMAND , 1994 .

[13]  David Gilo,et al.  Does a Supplier Have the Market Power We Thought it Had? The Use of Vertical Integration and Vertical Restraints to Restore the Supplier's Market Power , 1999 .

[14]  G. Zaccour,et al.  Channel coordination over time: incentive equilibria and credibility , 2003 .

[15]  Saeed Samiee,et al.  Exporting and the Internet: a conceptual perspective , 1998 .

[16]  Z. K. Weng,et al.  Channel coordination and quantity discounts , 1995 .

[17]  R. Ramsey,et al.  Strategic issues of e‐commerce as an alternative global distribution system , 2001 .

[18]  ChiangWei-yu Kevin,et al.  Direct Marketing, Indirect Profits: A Strategic Analysis of Dual-Channel Supply-Chain Design , 2003 .

[19]  Charles S. Tapiero Applied stochastic models and control in management , 1988 .

[20]  M. Parry,et al.  Channel Coordination When Retailers Compete , 1995 .