The Anatomy of a Successful Platform Business Model

13 min readJun 19, 2023

“people don’t buy a platform, they buy what it can deliver in terms of products and services” Ming Zeng (2018)

Platforms have emerged as powerful engines of innovation and growth, reshaping industries and creating new avenues for value creation in our increasingly digital and globalised economy. These platforms, more than just collections of frameworks and technologies, form cohesive ecosystems that combine language, frameworks, tools, and services to work seamlessly together (Adler, 2023). They serve as the foundational digital infrastructure, enabling diverse participants to build, innovate, and interact. The value of platforms generally increases with the growth in the number of constituencies and applications, as growth in one group often drives growth in another (Komoroski, 2023).

Rather than owning the means of linear production with a single revenue stream, platforms own the means of connection and thereby enable value creation and value capture multiples. Platform businesses are characterised by their central role as intermediaries, connecting two or more distinct groups of constituents with different goals and complementary needs, enabling their direct interaction (Zhu & Furr, 2016). Platforms must own these relationships and interactions between members in the form of transactions and data flows, while every member in the platform ecosystem must retain a level of agency. Matchmaking is therefore at the heart of every platform business model, whether it takes the shape of transaction-focused marketplaces, social networking, supply and distribution optimisation between businesses, or learning (Hagel, 2015).

The Blueprint of a Successful Platform

Marco Iansiti and Karim R. Lakhani (2020) describe the anatomy of a successful platform to be characterised by modularity and scalability. Modularity allows for the easy addition and removal of components, while scalability ensures that the platform can handle increasing amounts of data flows and computations, continuously expanding to accommodate periods of exponential growth. At the same time, the platform architecture must support real-time data processing, aggregated and advanced analytics, and reinforcement learning for AI-generated insights and recommendations.

The Importance of Size and Shape of Networks

The power of a platform business model is built on the ability to harness network effects, a phenomenon where the value of a service or product increases with the number of participants. There are direct and indirect network effects. Direct network effects occur when constituents derive value from the presence of other members within the same group. Indirect or multi-sided network effects, on the other hand, arise when constituents from one category value the presence of participants from a separate, distinct category.

The first key dimension, network size, is pivotal in strengthening these network effects. The expansion of the network size can significantly and often exponentially enhance the value delivered to users, thereby creating formidable barriers to entry for potential competitors. To maximise the benefits of network size, platform businesses must focus on increasing the number of connections and thereby the data flow within the network. This can be achieved by identifying key user groups, balancing supply and demand, providing tools for positive interaction, incentivising participation, and removing transactional friction. Additionally, platforms should consider cross-network effects, where an increase in participants on one side of the platform translates into value for constituents on another side. This fosters and enhances interactions between different user groups, creates more valuable services for all users, and opens up multiple revenue stream opportunities for the platform business.

The second key dimension, network shape, is equally determining the competitiveness of a platform. It describes the structure of the network, including the presence of network hubs and the platform’s centrality in connecting disparate industries and businesses. The network clustering effect, which refers to the regionalisation or specialisation of clusters within a network, can have adverse competitive implications. Clustered networks are operating in a highly competitive market environment since local scale is all that matters for competitors to achieve critical mass. This also applies to the value of data and AI, with clustering limiting the relevancy and value of data aggregation across regions and applications.

Platforms businesses must strive to excel in aggregating data that flows within the network and creating learning effects through powerful algorithms and AI. They should also consider how data can be made valuable to complementary networks to open up additional revenue stream opportunities. For instance, designing algorithms that suggest connections between apps and users can create new network pathways and increase the overall network value. In essence, the success of a platform business model hinges on its ability to effectively manage both the size and shape of its network.

Mastering the Dual Dynamics of Value Creation and Value Capture

Platforms must strategically navigate the dual dynamics of value creation and value capture by implementing effective strategies. This will ensure that they both create real value for its partners and successfully capture a portion of that value for sustained growth and competitiveness. Understanding and strategically leveraging the following platform performance drivers is therefore crucial to thriving in a competitive landscape.

Value Creation Drivers:

Value creation in platform business models is primarily driven by network effects, learning effects, network synergies, and network clustering.

Network Effects: Increase the number of connections within and across networks to extend and broaden the digital network reach. Create both direct and indirect network effects to maximise the value of the platform as the number of users or connections grows.
Learning Effects: Harness inflowing data for experimentation and learning. Optimise analytics and AI algorithms to deliver enhanced services and experiences. Generate or combine unique and valuable data to create competitive moats and barriers.
Network Synergies: Go beyond existing core applications and participant groups in search for additional synergies. Integrate networks of related and complementary services into a new ecosystem to multiply value creation and capture new business opportunities.
Network Clustering: Create a network that requires a global scale as opposed to multiple local scales to raise entry barriers to competition. Focus both on geographic and application diversity.

Value Capture Drivers:

Value capture depends on strategies that enable the platform to retain a portion of the value created. It is primarily characterised by monetisation strategies, network bridging, and how well the platform business manages to protect itself against multi-homing and disintermediation risks.

Monetisation strategies: Explore diverse monetisation strategies such as charging each side of the platform differently, employing commission-based models for transactions, introducing subscription models for steady revenue streams, using freemium models to attract users with free services while charging for premium features, and generating revenue through advertisements. Avoid charging a constituent group for services it can receive for free or significantly subsidised by another adjacent platform competitor.
Network Bridging: Make new connections across previously separate networks to build important synergies. Connect multiple networks, for example by providing access to valuable data assets to multiple disconnected services and industries.
Multi-Homing: Limit your platform users from forming ties with multiple similar platforms or hubs at low switching costs. Implement strategies such as usage-based rewards to lock in one or multiple sides of the market.
Disintermediation: Prevent nodes in the platform from bypassing the hub and prevent network participants to interact directly after the original matchmaking. Block contact information, enforce terms and conditions, and most importantly provide additional value to platform-based business interactions, such as insurance, payment escrow, and dispute resolution services.

Ethical Responsibilities and Challenges

Ethical considerations are integral to the long-term success of platform business models. Trust, as Darin Adler emphasised in Apple’s 2023 Platforms State of the Union opening address, forms the bedrock of all digital platform businesses. Elements such as accessibility, inclusivity, environmental sustainability, and data security and privacy are fundamental to a platform’s success. Paramount to earning trust are responsible data management policies and practices, which provide all constituents control over their data and transparency regarding its use. The ethical responsibilities of platform businesses extend beyond just their shareholders, customers, partners, and employees, to the wider communities in which they operate. Several critical challenges, as noted by Iansiti & Lakhani (2020), need to be addressed by platform businesses to limit misuse of their platform, data and algorithms:

Digital Amplifications and Algorithmic Bias: Platforms, with their ability to amplify and influence information on a grand scale, face complex ethical challenges related to digital amplification and algorithmic bias. The risks of reinforcing pre-existing biases and propagating targeted disinformation are genuine threats. Furthermore, platforms can unintentionally perpetuate and exacerbate inequalities due to intrinsic biases in algorithm design, which can be influenced by flawed training data and human labelling bias.
Cybersecurity: Due to their valuable data and large scale, platforms become attractive targets for those aiming to exploit vulnerabilities and breach sensitive commercial and personally identifiable information. Platform businesses must immediately address any suspected or known vulnerabilities in their code and design responsible processes relating to data retention and management, to guard against potential breaches effectively.
Platform Control: Platform businesses can be seen as information fiduciaries with a duty to act in the best interest of their data partners. They are therefore charged with designing and enforcing data management and governance across its full range of data exchange operations, networks, and applications. This responsibility encompasses dealing with contentious issues related to content curation and censorship, and finding a balance between offering seamless, AI-driven processes and maintaining checks and balances to prevent and limit opportunities for misuse and exploitation.
Inequality and Inequity: Platforms must strive for a fair and equitable distribution of value and decision rights across the economies and communities they influence. An important ethical and legal challenge as platforms grow in power and influence is balancing the development of native platform capabilities against supporting complementors with equal opportunities to build and innovate within their platform ecosystem. Platforms need to maintain a level playing field, avoiding the exploitation of dominant market positions.

A practical approach to navigating the ethical responsibilities and challenges of a platform business is to establish a keystone strategy aligning the platform organisation’s objectives with the collective health of its networks and constituents (Iansiti & Lakhani, 2020). By aligning internal and external perspectives, a platform can shape and maintain healthy networks, improving the overall health of the business ecosystem it supports and relies on, and thereby ensuring the organisation’s long-term performance.

Strategic Steps to Transition into a Platform Business Model

Transitioning to a platform business model requires a systematic and strategic approach. Here is a sequence of steps that can guide businesses through this transformation:

Map Core Networks: Begin by listing and mapping the networks that the business is connected to. This will paint a picture of the existing ecosystem.
Evaluate Network Potential: For each major network, evaluate its potential for value creation and value capture at scale. Consider factors such as network effects, learning effects, network synergies, competitive pressure and the threat of new entrants or substitutes.
Understand Primary Networks: Reflect strategically on the core service delivered by the platform and its fundamental value proposition. Understand which networks are crucial to providing this service and establish the most important characteristics and dynamics of these networks, including the risk of network clustering. Develop strategies to achieve critical scale and scope while reinforcing network and learning effects over time.
Network Bridging Opportunities: Evaluate and analyse the characteristics of secondary networks to identify scope expansion opportunities. This involves creating indirect network effects by bridging previously separated networks, leveraging shared customers and data across markets, and fostering new connections across networks to build synergies and an ecosystem effect.
Identify and Address Value Capture Challenges: Establish challenges associated with network clusters, multi-homing, and disintermediation. Develop strategies to mitigate these challenges without compromising the value proposition.
Determine Value Capture Opportunities: Establish the best strategies for capturing value without limiting adoption and engagement. This includes considering various monetisation strategies, including anonymised (aggregated) data monetisation opportunities for related industries.
Establish Value and Engagement Metrics: Quantify what value different constituents derive from the platform. Establish metrics to measure the level of user engagement, growth in usage, and the success rate of matching participants with what they are searching for.
Own Relationships: Strive to exclusively own all direct relationships between platform participants. This involves managing externalities effectively and ensuring that the platform continues to deliver value to its users beyond the initial transaction.

Leading Organisational Change towards a Platform Business

The steps listed above, though critical, are not sufficient in isolation to guarantee a successful transition to a platform business model. The biggest challenge often lies in transforming an organisation’s operating architecture and building the requisite skills, capabilities, and culture (Iansiti & Lakhani, 2020). With transformative technologies becoming ubiquitous as cloud services and technical expertise available to assist with their deployment, it is often not the lack of resources that impedes this process. Instead, the biggest obstacle is often the fear of challenging the status quo and implementing changes. Overcoming these challenges requires an integrative perspective, driven by sustained executive and managerial commitment. Transitioning to a platform business model is indeed a substantial challenge, often met with significant organisational inertia. However, overcoming these obstacles can be addressed with a well-planned and strategic approach. Such a transformation journey can be envisioned across three horizons (Gourévitch et al., 2017):

Horizon 1: Laying the Foundation

The organisation must first assess its current position in terms of data, digitisation, and existing capabilities. The preliminary step involves a swift, objective assessment of the company’s situation against industry best practices. Key steps in this phase include:

Establishing Platform and Data Vision: A clear vision for the transformation should be established that aligns with the organisation’s objectives, whether that involves using data to enhance operations or constructing new business models.
Success of Early Use Cases: Launching small-scale efforts that serve as pilot projects. These initial projects may be limited in scope but their success can illustrate the advantages of digital transformation and help establish the right partnerships.

Horizon 2: Charting the Course

In the second horizon, insights from early wins help devise a roadmap for organisation-wide transformation. It involves building a portfolio of transformation opportunities and identifying and prioritising functions or units that can benefit most. Key steps in this phase involve:

Developing Data Platform and Analytics Model: The organisation should design a data platform (or data lake) that can accommodate its product map and should use that platform to progressively transform its legacy systems.
Establishing Data Management and Governance: Instituting governance rules and a data governance structure to safeguard data quality and integrity. It’s also crucial to define data quality and establish ways to continually improve it.
Scaling Data, Analytics and AI Infrastructure: Evaluating whether the current infrastructure can support the envisioned vision and product roadmap. This includes deciding whether to generate or buy data assets, establishing cloud, analytics and AI partners and how to approach legacy IT systems in the transformation.

Horizon 3: Operationalising Data, Analytics and AI

In the final horizon, companies “industrialise” data and analytics by constructing systems and capabilities to implement new data-driven strategies and processes. This horizon encompasses:

In the final horizon, organisations scale up data and analytics capabilities to support the full platform vision. Data-based systems and processes are standardised, ensuring the output is replicable, efficient, and reliable. Key steps in this phase include:

Operationalising Data-based Systems: Establishing processes to standardise and rationalise the operation of data-based systems and processes, ensuring the output is replicable, efficient, and reliable.
Cultivating Data-driven Culture: Ongoing change management program to cultivate the necessary talent and skills and establish data-centric and platform-focused mindsets, behaviours, and ways of working. Organisational gaps are addressed with employee development; new roles might have to be created and filled with new hires.

Each horizon of the transformation journey demands focused resource allocation and decisive actions. The role of executive leadership and management becomes pivotal in this context and plays a key role in the success of the platform business mode. Leadership is responsible for aligning every function of the organisation with the transformation agenda. Through their influence and strategic decision-making, they ensure that the transition progresses across all horizons. These three horizons are not sequential but can be started concurrently. Ultimately, the goal is to build a sustainable platform business model that can continuously adapt and evolve in response to changing business landscapes and customer needs.

Conclusion

Platform business models are powerful ecosystems that facilitate interactions among diverse agents. They provide a foundation for participants to build, innovate, and interact. Successful platforms are characterised by modularity, scalability, responsible and value-driven data governance, and the ability to support advanced analytics, AI and ongoing learning effects.

The power of platform business models lies in their ability to harness network effects, where the value of a service or product increases with the number of participants. Platforms should focus on increasing the number of connections and the data flow within the network, and manage both the size and shape of its network effectively.

Platform businesses must strategically navigate the dual dynamics of value creation and value capture. Value creation is driven by network effects, learning effects, network synergies, and network clustering, while value capture involves strategies that enable the platform to retain a portion of the value created.

Transitioning to a platform business model requires a systematic and strategic approach, including mapping core networks, evaluating network potential, identifying and capturing multiple value stream opportunities, and maintaining direct relationships with their customers. The power of platform business models lies in their ability to harness network effects, where the value of a service or product increases with the number of participants. Platforms should focus on increasing the number of connections and the data flow within the network, and manage both the size and shape of its network effectively.

Transitioning to a platform business model requires a systematic and strategic approach, including mapping core networks, evaluating network potential, identifying and capturing multiple value stream opportunities, and maintaining direct relationships with their customers. It is a journey and platform businesses must also be ready to manage significant organisational changes and overcome potential inertia in order to successfully adopt a platform model.

References

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