Pricing Digital Goods: Discontinuous Costs and Shared Infrastructure

Abstract

We develop and analyze a model of pricing for digital products with discontinuous supply functions. This characterizes a number of information technology-based products and services for which variable increases in demand are fulfilled by the addition of "blocks" of computing or network infrastructure. Examples include internet service, telephony, online trading, on-demand software, digital music, streamed video-on-demand and grid computing. These goods are often modeled as information goods with variable costs of zero, although their actual cost structure features a mixture of positive periodic fixed costs, and zero marginal costs. The pricing of such goods is further complicated by the fact that rapid advances in semiconductor and networking technology lead to sustained rapid declines in the cost of new infrastructure over time. Furthermore, this infrastructure is often shared across multiple goods and services in distinct markets.The main contribution of this paper is a general solution for the optimal nonlinear pricing of such digital goods and services. We show that this can be formulated as a finite series of more conventional constrained pricing problems. We then establish that the optimal nonlinear pricing schedule with discontinuous supply functions coincides with the solution to one specific constrained problem, reduce the former to a problem of identifying the optimal number of "blocks" of demand that the seller will fulfil under their optimal pricing schedule, and show how to identify this optimal number using a simple and intuitive rule (which is analogous to "balancing" the marginal revenue with average "marginal cost"). We discuss the extent to which using "information-goods" pricing schedules rather than those that are optimal reduce profits for sellers of digital goods. A first extension includes the rapidly declining infrastructure costs associated with Moore's Law to provide insight into the relationship between the magnitude of cost declines, infrastructure planning and pricing strategy. A second extension examines multi-market pricing of a set of digital goods and services whose supply is fulfilled by a shared infrastructure.Our paper provides a new pricing model which is widely applicable to IT, network and electronic commerce products. It also makes an independent contribution to the theory of second-degree price discrimination, by providing the first solution of monopoly screening when costs are discontinuous, and when costs incurred can only be associated with the total demand fulfilled, rather than demand from individual customers.

Digital Product Pricing Algorithm Development

“…To do this we must consider two factors as a basis: a stage of the product life cycle and the level of competition. These factors are quite provisional, because they largely depend on the product marketing and must be determined separately for each case (Huang & Sundararajan, 2011;Mysen & Chen, 2012). At the fourth stage we determine the level of a minimum acceptable income of company from the digital product realization (MPC), that will satisfy the requirements of the company to development and realization profitability, based on the company price policy.…”

Analyzing the Economies of Scale of Software as a Service Software Firms: A Stochastic Frontier Approach

“…The marginal cost of IT infrastructure is nonzero at the capacity limit when the vendor acquires additional capacity. Theoretically, this unique cost structure has been shown to be equivalent to a constant variable cost [11]. In accounting, "cost of revenue" is closest to variable cost conceptually.…”

“…In other words, results from the advertising expense suggest that the negative correlation between R&D and TE does not appear in all types of expenses. 11 2) Traditional Software Firms: Results in Table IX show that efficient traditional software firms spend more on both R&D and advertising. This finding is statistically significant but with very small absolute values.…”

“…Leading traditional firms spend more on both R&D and advertising, whereas leading SaaS firms spend significantly less on R&D. Since R&D and advertising are the two most important expenses, reducing investment 11 Within R 2 is the proportion of variability that is explained by the model within the group of each firm.…”

“…SaaS vendors may incur semivariable costs such as expanding their IT infrastructure in terms of installing more servers, procuring more storage devices, renting a larger space, as well as hiring more IT professionals. These IT infrastructure costs radically change the zero-variable-cost property in a unique way because infrastructure costs are neither purely fixed nor completely variable in nature [11] 2 . In this way, there are two countervailing effects regarding the economies of scale of SaaS firms, motivating us to further examine the overall effect by applying advanced productivity analysis to this new and promising software business model.…”