What are Network Effects?
Network Effects refer to the incremental benefits gained from new users joining the platform, which results in the product becoming more valuable for all users.
- What is the definition of network effects?
- How are direct network effects distinguishable from indirect network effects?
- What are the different types of network effects?
- Which real-life companies benefited from network effects?
Table of Contents
Network Effect Definition
The core idea behind network effects is that each new user improves the value of a product/service for both new and existing users alike.
The term “network effect” describes the phenomenon in which the value of a product improves for all users as more users join a platform, even for the existing user base.
The concept of network effects is particularly important in the digital age, given continued technological disruption amid rapid globalization.
Companies with network effects observe that more product usage is beneficial for their entire user base. However, “usage” refers to customers that actively use a product or participate on the platform.
Therefore, the impact of network effects is contingent on the total number of potential buyers and sellers in the market and how much the company can leverage its user base.
Negative Network Effects
Generally speaking, the more users and sellers there are, the greater the network effects are (and the value offered to all sides).
In contrast, a “negative network effect” is when a platform’s value declines after growth in usage or scale.
For instance, an overwhelming number of users could lead to network congestion, i.e. a noticeable drop-off in product quality and customer service.
Examples of Network Effects
Most, if not all, of the leading technology companies and startups nowadays benefit from network effects.
- Social Media: Twitter, Facebook/Meta, Instagram, Reddit, Snapchat, TikTok, Pinterest
- E-Commerce: Amazon, Shopify, eBay, Etsy, Alibaba, JD.com
- Recruiting: LinkedIn, Glassdoor, ZipRecruiter, Indeed
- Ride-Sharing: Uber, Lyft
- Food-Delivery: Grubhub, UberEats, Postmates, Doordash
- Delivery Service: Shipt, Instacart, GoPuff
- Freelance: TaskRabbit, Upwork, Thumbtack
- Food Reservation: OpenTable, Resy
- User Reviews: Yelp, Tripadvisor
The pattern from these companies and their products is that positive feedback loops form the basis of their network effects.
For example, Google’s search engine platform is one of the best examples of a durable moat created by network effects, as far more accurate search results are provided due to more user data collection.
Google’s search capabilities benefit not only the core search engine but also all product offerings (e.g. YouTube, Google Maps) within its portfolio of offerings, as well as on the advertising side.
Hence, Google has consistently retained 90%+ of the global search engine market share.
Global Search Engine Market Share (Source: StatCounter)
Metcalfe’s Law is frequently brought up when discussing network effects, as it states that the value of a network grows in proportion to the square of the number of users within the network.
The theory originally emerged from telecommunications networks, as Robert Metcalfe (Ethernet, 3Com) attempted to explain the cause of non-linear exponential growth.
In the best-case scenario, a company can capitalize on a network effect once connectivity is established, i.e. the network appears to market itself as organic user growth continues to climb upward.
However, one distinction is that growth by itself is not always a sign of network effects – instead, user engagement and retention are just as important (i.e. growth merely sets network effects into motion).
Direct vs Indirect Network Effects
Broadly, network effects can be categorized as either direct or indirect.
- Direct Network Effects: The growth in the network size and increased usage positively impact the value for the entire platform (i.e. “same-side effects”).
- Indirect Network Effects: The indirect advantages that emerge for certain users and the platform later on (i.e. “cross-side effects”).
Direct network effects are more intuitive and easier to understand, i.e. more users result in compounding benefits from improved technical capabilities and word-of-mouth marketing.
On the other hand, indirect network effects provide value after other developments, such as if another user group joins the network.
For instance, if a new user joins Grubhub to order food delivery, the added value to other users (and most drivers) is near zero. But drivers within the same location – i.e. one sub-group of existing or potential future drivers – could someday benefit from that user joining as they can service the new user.
Another example of indirect network effects would be upselling/cross-selling on software tools (e.g. Microsoft 365, G Suite), as the positive benefits emerge later on from a different product, after an upgrade, or from the collaboration between the tools.
Two-Sided Network Effects
Two-sided network effects occur when more product usage by one distinct group of users increases the value of a complementary offering to a different set of users (and vice versa).
Types of Network Effects
The value creation from network effects can stem from various sources, with some notable examples being the following:
- Marketplace Network Effects: Aggregating customers and suppliers into one shared marketplace to exchange goods (e.g. Amazon, Shopify).
- Data Network Effects: Gathering more user data and insights over time can establish a competitive edge (e.g. Google Search Engine, Waze).
- Platform Network Effects: User growth and high retention rates within the product ecosystem (e.g. Apple, Meta/Facebook).
- Physical Network Effects: Significant initial spending needs can be a barrier to entry that creates a network (e.g. Infrastructure, Utilities, Telecommunication, Transportation).
Uber & Lyft Network Effects Example
Network effects compound once critical mass is attained, so customer acquisition costs typically decline beyond the inflection point.
For sharing (or “gig”) economy platforms like Uber and Lyft to attain exponential growth, asset purchases and spending more on marketing are not sufficient.
But rather, acquiring more users is the only real pathway to achieving scale and eventual profitability – especially within highly competitive markets with significant burn rates.
Once user traction takes off, ideally, new customer acquisitions can be practically nothing for platform companies, typically due to word-of-mouth marketing among users.
For example, after Uber and Lyft built out the user interface and app development – i.e. incurred substantial costs, largely funded by venture capital (VC) and growth equity – the marginal costs related to distribution diminished with increased scale.
More drivers do not necessarily improve the user experience, but demand does attract more drivers to submit applications, which indirectly improves ride quality for all users.
The five stages of Uber’s outlined network effect cycle are as follows:
- Increase Driver Supply
- Reduce Wait Times and User Fares
- Higher Number of Rider Sign-Ups
- Greater Earnings Potential (Increased Riders, More Rides Per Hour)
- More Drivers Join Uber
Uber Liquidity Network Effect
“Our strategy is to create the largest network in each market so that we can have the greatest liquidity network effect, which we believe leads to a margin advantage.”
Uber Network Effect (Source: S-1)
For both Uber and Lyft, if there were not enough supply (i.e. the drivers) to match the demand (i.e. the riders), both companies would have failed.
Both appear to have moved past the near-term risks and the major hurdle of establishing strong network effects, which continues to serve as a competitive edge to this day, especially with their other divisions (i.e. UberEats) now generating revenue.