Pricing Models for Industrial Data

The increasing importance of digital technologies and connectivity in products, processes, and organizations leads to a growing amount of generated data. Manufacturing companies can use these data to control and monitor production processes as well as offer data-driven services in addition to their existing product portfolio. In the business to consumer (B2C) sector, data-driven services are already well established. Manufacturing companies on the other hand tend to struggle to effectively leverage and monetize data-driven service offerings, as the value creation and utility potential often is not apparent. However, to increase their competitive positioning, companies need to establish a sound understanding of the value creation of their products or services and the resulting pricing potential.
Even though success stories exist in the manufacturing industry, most companies face challenges in effectively pricing their data-driven offerings. This is, in part, due to the many facets of pricing such offerings. Because of their diversity and complexity, these may seem intimidating to consider at first glance.
Figure 1 shows four challenges of pricing data-driven service offerings that should be considered. The potential customer purchases a benefit and/or functionality, which, contrary to a physical product, is not tangible. Hence, the quantification of the customer’s benefit is based on the value created by the generated data and service offering. For manufacturing companies, the effort required to determine suitable price points may be higher, as the quantification depends on the benefit of the individual customer. From a customer’s perspective, estimation efforts regarding the return on invest may arise. Thus, both the customer and provider of data-driven service offerings need to have a sound understanding of the long-term value creation and utility potential that is generated.
Manufacturing companies notably face obstacles in selecting and designing a suitable pricing model to sell their data-driven service offerings. In addition to being more difficult to quantify, data-driven service offerings require more extensive analysis of the value creation and utility potential. This is due to the previously outlined intangible customer benefit.
This study aims to answer the question how to develop pricing models for industrial data. To do so, current industry standards and practices regarding data-driven service offerings were identified, along with an overview of relevant trends in this field. This knowledge lays the theoretical foundation of the pricing of data-driven service offerings.
In a second step, three case studies were conducted, in which various types of data-driven service offerings were implemented. Each process was thoroughly analyzed to infer data about pricing strategies that can realistically be implemented in manufacturing companies.
Finally, the previously acquired information about the process of pricing data-driven service offerings was abstracted to identify key performance indicators. These form the basis of general recommendations for pricing strategies that apply to all manufacturing industries.