Platform canvas

An extension of the theses presented in this note can be found in the chapter: Doligalski, T. Platform canvas: Does the platform business model imply disruption and monopolisation?. In Disruptive Platforms (pp. 1-17). Routledge.

!!! The full content of the chapter is available at the Amazon.com page in the preview of the Kindle version.

Among the business models of Internet companies, it is the platforms that attract the most attention from researchers. It is to them that a significant impact on the economy, society and everyday life is attributed. Although platforms have functioned in traditional economies, e.g. in the form of bazaars, their role has increased in the digital economy. This is due to the greater ease of aggregation of sellers and buyers, creators and consumers, or people with the same interests, identities or problems. Platform is defined here as a business model that matches independent agents and facilitates their interactions.

In the following, the business model of multi-sided platforms will be presented using a modified scheme called Business Model Canvas (Osterwalder, Pigneur, 2010). This approach has been acclaimed by practitioners and has seen many modifications e.g. lean canvas (Maurya, 2012), including a couple for platforms (Choudary, 2015; Sorri et al., 2019; Allweins, Proesch, & Ladd, 2020). This approach is characterized by presenting the most important elements of the system in the form of a graphical structure. It does not represent the interactions between these elements or the consequences in the form of emergent phenomena such as competitive advantage (cf. Table 1).

Table 1. Canvas-based platform business model

 

 

 

Object makers
• creators
• providers

 

 

 

 

 

 Value for object makers
• network values
• non-network values
• incurred costs

Reputation system  

 


Value for object takers
• network values
• non-network values
• incurred costs

 

 

 

Object takers
• consumers
• recipients

 

Matchmaking mechanism
Objects and other resources
Institutions and interventions
Value capture

Multi-sided platforms bring together complementary groups of customers, which are described here as object makers (creators, suppliers) and object takers (buyers, customers). These ventures exploit the network effect. The more customers belonging to one group, the greater the benefits to customers in the other group usually are.

The value delivered to customers by platforms consists of network and non-network values. Network values result from interacting with other users or using the facilities they provide. Non-network values are the benefits of operating the platform without the direct involvement of other customers (e.g., customer service, user-friendly interface, mobile app availability). The costs of using the platform include the risks associated with the collection of user data by the platform, being exposed to ad impressions, having to pay fees. In addition, in the case of object donors, there is the need to invest in the entry to the platform (including the development of an appropriate reputation) and the subsequent dependence on the platform, expressed in high switching costs.

An important element of almost every platform are objects and other resources that are made available there by users. Objects are goods for which customers come to the platform (e.g. content, software) or are their presentation (e.g. descriptions of products, people, institutions). Other resources are also ratings and reviews, as well as posts on forums or thematic groups. Platforms shape their object offerings aiming for economies of scope, i.e. having a broad set of objects (Gawer, 2014), as well as economies of scale of having a deep set of objects. In some cases, however, there may be disadvantages of having too large a collection that includes lower quality objects.

A key element of any platform is the matching mechanism, which determines the ways in which facilities are available to client-customers. The diversity of this mechanism is illustrated by two extreme approaches. The first is to allow users to search, filter, and browse all objects made available on the platform without offering an algorithmically personalized set of objects. This is typically the case in discussion forums, some e-commerce platforms, etc. The platform does not suggest any objects to users (except for the homepage, etc.), the collection of objects is ordered according to their basic parameters (name, subject, time of addition, price). Then, the key role is played by mechanisms that increase the transparency of information. The essence of the second approach is to use an algorithmically personalized set of objects. This is how some dating sites operate (e.g. eHarmony, eDarling). They provide their users daily with only a few profiles of people they can contact. In contrast, competing sites allow users to view the profiles of all registered people.

A key element of many platforms are reputation systems. They represent the past activity of individuals within the community in a way that makes it easier for other users to decide whether to interact with the individual (Dellacoras, 2010). Reputation systems may use an element of self-presentation, activity statistics, ratings and comments from other users, or synthetic measures that take into account multiple variables and give them appropriate weights (e.g., ranks, awards). A developed reputation system, often based on tens of thousands of users’ opinions, facilitates the potential buyers’ decision making process, and thus increases the platform’s attractiveness. It also constitutes a serious barrier to a seller’s exit from a given platform, as reputation developed in such a way is difficult to transfer to another market context.

Institutions and interventions most often serve the purpose of increasing the efficiency of the platform’s functioning, the degree of its orderliness and security. The purpose of applying institutions and is also – using concepts from network theory – shaping the network of users, i.e. taking care of an appropriate number of nodes (users), network density (number of interactions between them), quality of connections (interactions) and network stability. Examples of institutions are the matching mechanism and reputation system highlighted in the model. Other institutions are rules of gatekeeping and norms governing platform interactions. Interventions are ad hoc actions to correct the functioning of the platform (e.g., a promotional campaign to attract customers from a selected group).

The value capture by a platform involves the use of a selected revenue model specifying the entity that is the source of the revenue and the event from which it arises. Revenue models of multi-sided platforms are often asymmetric, e.g. only sellers are charged. In addition to actions resulting from the application of the chosen revenue model, e-commerce platforms often use their strong position to capture additional value at the expense of their customers. They change recommendation algorithms to place more emphasis on price, impose restrictions on prices that sellers can set elsewhere, and use mechanisms that weaken seller-buyer relationships (Hagiu and Wright, 2021). Platforms often leverage their knowledge of buyers’ needs and introduce their products, similar to bestsellers offered by sellers, increase the visibility of their products in key places like search results, homepage, recommendation mechanisms. Platforms therefore have two roles – as an ally enabling the seller to conclude transactions with buyers in a friendly environment and as his competitor offering similar products and having a privileged position.

References

Choudary S. (2015). Platform scale : how an emerging business model helps startups build large empires with minimum investment. [Place of publication not identified] : Platform Thinking Labs.

Cusumano M.,  Gawer A.,  Yoffie D. (2019). The business of platforms : Strategy in the age of digital competition, innovation, and power. New York: HarperCollins Publishers.

Dellarocas, C. (2010). Online reputation systems: How to design one that does what you need. MIT Sloan Management Review, 51(3), 33.

Filistrucchi, L., & van Damme, E. (2013). Identifying two-sided markets. World Competition36(1).

Gawer A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, vol. 43(7)

Hagiu, A., & Wright, J. (2021). Don’t Let Platforms Commoditize Your Business. Harvard Business Review99(3), 108-114.

Maurya, A. (2012). Running lean: iterate from plan A to a plan that works. ” O’Reilly Media, Inc.”.

Meyer, M. H., & Lehnerd, A. P. (1997). The power of product platforms. Simon and Schuster.

Osterwalder, A., & Pigneur, Y. (2010). Business model generation: a handbook for visionaries, game changers, and challengers. John Wiley & Sons.

Parker G., Alstyne M., Choudary S. (2016). Platform Revolution. New York: W. W. Norton & Company.

Sorri, K., Seppänen, M., Still, K., & Valkokari, K. (2019). Business model innovation with platform canvas. Journal of Business Models7(2).

Ziaie, P., & Krcmar, H. (2012). Introducing a design framework for reputation systems in multi-tier production communities. Digital Creativity, 23(2).