Endogenous differentiation of information goods under uncertainty

Information goods can be reconfigured at low cost. Therefore, firms can choose how to differentiate their products at a frequency comparable to price changes. However, doing so effectively is complicated by uncertainty about customer preferences, compounded by the fact that the search for a good product niche is carried out in competition with other searching firms.We study two firms that differentiate their information goods.The firms simultaneously compete in product configuration and price. We assume a non-uniform distribution of consumers: the largest number prefer a product located at a "sweet spot," but the rate at which the customer density falls off away from this product configuration is unknown. Our characterization reflects the standard tradeoff between exploitation (current profit) and exploration (learning to enhance future profit). In our model firms balance current profits from competing for a mass and a niche market, while learning about the profitability of these alternative strategies.We show that the amount of learning that firms will undertake depends on the convexity or concavity of the profit function in the rate of demand fall-off. In our model firms have an incentive to learn, and can use both price and product configuration in order to explore. We show that the ability to explore in product characteristic space leads to a previously unidentified consequence of learning: attenuation of competition. The incentive to learn induces firms to differentiate their products more than they would if the value of learning were ignored. This leads to decreased direct competition with rivals, and thus higher prices and profits than if the firms were acting myopically. Thus, we might expect that when firms are not well informed about consumer preferences for information goods --- as might be especially true in new markets for innovative products --- product diversity will be higher and direct competition will be smaller than might otherwise be expected.

[1]  Arie Segev,et al.  Brokering strategies in electronic commerce markets , 1999, EC '99.

[2]  Marshall W. van Alstyne,et al.  Information technology— a source of friction?: an analytical model of how firms combat price competition online , 2000, EC '00.

[3]  J. Harrington Experimentation and Learning in a Differentiated-Products Duopoly , 1995 .

[4]  B. Jullien,et al.  OPTIMAL LEARNING BY EXPERIMENTATION , 1991 .

[5]  Ramayya Krishnan,et al.  Pricing strategies on the Web: evidence from the online book industry , 2000, EC '00.

[6]  Edmund H. Durfee,et al.  Automated strategy searches in an electronic goods market: learning and complex price schedules , 1999, EC '99.

[7]  Jean Jaskold Gabszewicz,et al.  Spatial competition and the location of firms , 1986 .

[8]  Jeffrey O. Kephart,et al.  Competitive bundling of categorized information goods , 2000, EC '00.

[9]  André de Palma,et al.  Discrete Choice Theory of Product Differentiation , 1995 .

[10]  Leonard J. Mirman,et al.  A Bayesian Approach to the Production of Information and Learning by Doing , 1977 .

[11]  Rajarshi Das,et al.  Pricing information bundles in a dynamic environment , 2001, EC '01.

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  A. McLennan Price dispersion and incomplete learning in the long run , 1984 .

[14]  D. Neven,et al.  Can Price Competition Dominate Market Segmentation , 1988 .

[15]  Rajarshi Das,et al.  Two-Sided Learning in an Agent Economy for Information Bundles , 1999, Agent Mediated Electronic Commerce.

[16]  Edmund H. Durfee,et al.  Price wars and niche discovery in an information economy , 2000, EC '00.