Network effects have been the driving force behind some of the most successful companies in recent years, including giants such as Airbnb, LinkedIn, and Figma. But what exactly are network effects, and how can startups harness their power to build scalable, defensible businesses? Sameer Singh explores the key principles of network effects, including multiplayer interactions, the differences between virality and network effects, and the crucial aspects of scalability and defensibility.
Defining Network Effects
At its core, a network effect occurs when adding a user to a network makes the network more valuable for all users. This simple definition can be applied to a wide range of products and services, but when put into practice, numerous complications and nuances come into play. The key benefits of network effects are twofold: scalability and defensibility. As a network grows, the value for existing users increases, and new users become easier to acquire. This, in turn, can reduce customer acquisition costs (CAC) and increase the lifetime value (LTV) of customers. Additionally, network effects serve as a powerful economic moat for first-movers, making it difficult for competitors to catch up.
A study by NFX, a VC firm from Silicon Valley, revealed that of all companies that reached a billion dollars in value since 1994, 35% had network effects. However, these companies accounted for a staggering 68% of the total value created. This underscores the immense power of network effects in driving success and value creation in the tech industry.
Network effects are built on a multiplayer interaction between users
The heart of a network effect lies in the interactions between users. These interactions can take various forms, including connections between users of the same type (e.g., LinkedIn connections), connections between different types of users (e.g., buyers and sellers on a marketplace like FAIRE), or connections between developers and users on a platform (e.g., Salesforce’s AppExchange). These multiplayer interactions are the foundation upon which network effects are built.
Virality is different: It requires users to share the product while using it
While often used interchangeably, virality and network effects have distinct differences. Virality refers to the phenomenon where growth leads to more growth, as users spread the word about a product or service in process of using it. Network effects, on the other hand, occur when the value of a product or service increases as more users join the network.
Figma demonstrates a combination of network effects and virality, where sharing a design allows others to view it without needing an account, potentially leading them to sign up and create their own designs (virality), while signing up also enables collaboration and editing, increasing the value for both parties involved (network effect).
Unlike Figma, Zoom exhibits virality without a network effect. Sending a Zoom link allows users to join a call without requiring an account or extension, potentially raising awareness and prompting the recipient to consider using Zoom for future calls. However, the decision for one person to create a Zoom account does not influence or impact the other person’s decision, indicating the absence of a network effect.
Scalable network effects connect users across borders
Scalability is a key aspect of network effects, as it ensures that the value of a network continues to grow as more users join. A scalable network effect connects users across borders, making the network valuable for users, regardless of their location. Conversely, less scalable network effects are limited to specific locations. The level of scalability ultimately determines the potential for growth and value creation within a network.
Airbnb represents a highly scalable network as hosts in one location can attract tourists from around the world, and those guests can subsequently be leveraged to attract new hosts in other destinations. In contrast, Uber’s network is less scalable as it relies on acquiring drivers and riders within specific city areas, making expansion to different parts of the city challenging and necessitating starting anew each time, resulting in a less scalable marketplace.
Defensible network effects require network nodes to be unique
Defensibility refers to the ability of a network to resist competition and maintain its value. A defensible network effect hinges on the uniqueness of the nodes (or users) within the network.
Slack and Airbnb exemplify this, as on Slack, individuals aim to connect with specific users inside Slack, while on Airbnb, the uniqueness of each property based on criteria like location, amenities, price, and availability requires a substantial number of nodes to ensure usefulness. In contrast, networks with less unique nodes can be less defensible, such as Tinder, where broad attractiveness criteria make it easy for competitors to emerge, resulting in numerous dating apps in same locations, and Uber, where the focus is primarily on fast and affordable rides, allowing competitors to succeed by acquiring enough drivers to meet demand.
Network effects can be categorized into four variants
When examining network effects in the real world, four primary categories emerge:
• Data Networks: this is where a product collects data from users to improve the value of the product for the same group of users. (e.g., Waze, TrueCaller, TripAdvisor) • Interaction Networks: connect users and allow ongoing information exchange between them (e.g., social networks, dating apps, payment networks) • Marketplaces: connect users to allow them to buy and sell products or services from each other. (e.g., Airbnb, Amazon, Uber) • Platforms: where you allow developers to build applications on top of a product and connect those applications to the users of that product. (e.g., iOS, Android, Shopify, Salesforce)
Interaction networks and marketplaces can have a single- player SaaS component, a component of the product that can be used by itself without any other users being there. That can enhance the strength of the network effect there, meaning scalability and defensibility even further.
Sameer Singh: "There's a fundamental distinction between networks and communities. Networks rely on structured interactions, while communities thrive on unstructured interactions, making them more organic but harder to scale. As communities grow larger, curation becomes challenging, unlike products that naturally incorporate curation due to their structured nature. Communities, rooted in shared beliefs, maintain value by nurturing curated members, highlighting the importance of a smaller yet stronger community over a larger, diluted one."
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