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Pricing Strategies for Information Goods S¯adhan¯a Vol.30,Parts2&3, April/June 2005, pp. 257–274. © Printed in India Pricing strategies for information goods SIVA VISWANATHAN and G ANANDALINGAM Decision and Information Technologies Department, Robert H Smith School of Business, University of Maryland, College Park, MD 20742, USA e-mail: fsviswana, [email protected] Abstract. Digital or information goods are becoming the norm across a wide variety of industries including books, music, entertainment, gaming and education. Due to the fact that the marginal cost of producing or reproducing information goods is very low, it is much easier to customise and personalise them for individual users. Furthermore, sellers of these information goods are increasingly using bundling and versioning strategies to appropriate a greater share of the surplus. This paper examines recent research on pricing of information goods with particular focus on customisation, bundling and versioning strategies adopted by information goods providers. The paper highlights both game-theoretic as well as optimisation models that not only provide different perspectives, but also examine issues of information goods pricing at different levels of abstraction and complexity. Keywords. Pricing strategies; information goods; bundling and versioning strategies; customisation. 1. Introduction In this paper, we examine issues related to the pricing of information goods. Digital formats are becoming the norm across a wide variety of industries including books, music, entertainment, gaming, and education. Driven largely by rapid advances in digitisation technologies and mobile communications, traditional formats like CDs, cassettes, paperbacks, and gaming- consoles, are fast being replaced by their digital counterparts. Even traditional industries such as insurance, mortgages, financial services, and auto-retailing are witnessing significant transformations as the information component of their value chains are unbundled from their traditional value chains. Infomediaries such as such as Autobytel.com and Edmunds.com in the auto-retailing sector, Avviva.com in real estate, Eloan.com and Lendingtree.com in mortgages, and Healthcareadvocates.com, WebMD in healthcare, have established significant presence by leveraging information to provide value-added services. Information goods though, differ from physical goods in several ways. An information good is mainly a ‘collection of symbols’, and its value primarily derives from the precise arrangement of these symbols, rather than from the medium used for their preservation and transmission. Naturally, information goods are intangible and can be classified as ‘experience goods’, as potential consumers typically have to experience the good to understand its quality. 257 258 Siva Viswanathan and G Anandalingam Sellers of information goods have to, therefore, seek innovative ways to advertise and mar- ket their products, and to devise strategies that can help overcome issues related to consumer uncertainty. Unlike physical products that tend to be standardised and homogeneous in valu- ation, information goods tend to be heterogeneous – much of this heterogeneity in consumer valuations stemming from the differences in their context of use. For instance, an accurate weather forecast would be a lot more valuable to a commodity trader than to the typical tele- vision viewer. Consequently, it is important for sellers to not only customise their offerings for different consumers, but also to price them differently, depending on the differences in consumer valuations. Information goods are also highly transmutable, and this enables sell- ers to easily mix and match as well as modify information products for different consumers, increasing the ease of differentiation. It is precisely these characteristics that make customi- sation, bundling, and versioning, potent strategies for information good vendors. Information goods, unlike most physical products, are characterised by high fixed (sunk) costs, and very low to negligible marginal costs of reproduction. Thus, while one of the primary issues of concern for physical product manufacturers is the level of investment in capacity, the issue of concern to most information good manufacturers relates to quality and the number of versions to offer, rather than quantity. A related characteristic of information goods is that, they tend to be “public goods1”, with the ‘degree of publicness’ largely depending on the state of the technology (the medium of transmission, in particular) and the legal regime. While an information good packaged in a physical format (such as a book/CD) tends to be more of a ‘private good’, digitisation technologies have significantly increased the ease of replication and reduced their cost of transmission. Added to this, the lack of adequate intellectual property protection measures in the context of emerging technologies make information goods more of a pure public good, making it difficult for manufacturers and sellers of information goods to fully appropriate the benefits of its creation. Consequently, sellers of information goods may be required to adopt new business models to counter the threat of new digitisation technologies and to manage consumer expectations about the ‘public’ nature of information goods. These unique features of information goods raise a lot of issues, create new challenges and provide novel opportunities for manufacturers and sellers. Traditional cost-based pricing strategies can be very misleading and firms would have to devise new value-based pricing strategies for information goods. The differences between information/digital goods and phys- ical goods have profound consequences for the pricing and competitive strategies of firms, for consumer welfare, as well as for policy makers and regulatory authorities. In the follow- ing sections we examine the issue of pricing of information goods and provide a review of some of the recent research in this context, specifically focusing on customisation, bundling, and versioning strategies for information goods. In particular, we highlight the use of both game-theoretic as well as optimisation models that have been used to study problems related to information goods pricing. While game-theoretic models primarily focus on the strategic interaction between agents (for instance, buyers and sellers as in the case of the research described in § 2), they are limited in the number of parameters and the complexity of the prob- lem that can be studied, and usually resort to a greater level of abstraction. On the other hand, traditional optimisation models (for instance, the integer programming model described in § 3) allow researchers to handle a higher degree of complexity, while abstracting away from 1Pure public goods are characterised by two key properties – non-rivalness and non-excludability. Non-rivalness refers to the property that one person’s consumption of the good does not affect the quantity available to another, while non-excludability refers to the property that it is prohibitively expensive to exclude an individual from enjoying the benefits of the good. Pricing strategies for information goods 259 the strategic interactions among different agents. In addition to examining recent research related to information goods pricing, the following sections also highlight how these differ- ent models provide varied perspectives, and can be used to study issues at different levels of granularity. 2. Customisation and first-degree price discrimination While electronic markets have enabled the lowering of search costs for consumers they have also brought about an equally profound change – enabling sellers to search for, target and customise offerings for individual consumers. Sellers have always sought to target individ- ual consumers – in North America alone, direct marketing was a $200 billion business in 2001, accounting for three-fifths of the total spending on advertising. However, traditional direct marketing techniques have always been imprecise, with a conversion rate of less than 2% largely due to the use of imprecise consumer profiles built using a combination of census data, questionnaires, and electoral-roll information. Such information lacks the granularity and reliability required for precise and economically viable targeting. However, recent devel- opments in intelligent agent2 and web-related technologies have made it feasible for sellers to obtain near-perfect information on individual customers by combining real-time browsing behaviour, consumer purchase histories, and individual and site demographics. Sellers are able to provide products and services tailored precisely to the consumers’ individual pref- erences, and also practise first-degree price discrimination. The existence of these agents radically transforms the dynamics of the buyer–seller transaction, as it creates in some sense an exclusive market for each buyer–seller pair, and opens up the possibility of mass customi- sation as well as behaviour-based price discrimination. The resulting changes in the com- petitive landscape and market structures and the implications for public policy are not well understood. In one of the first systematic analyses of the economic implications of intelligent agent technologies, Aron et al (2005) analyse the pricing, profitability, and welfare implications of using these agent-based technologies that can price dynamically, based on the preferences and demographic profiles of consumers. Their study focuses on highly customisable information goods
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