network effect facebook case study

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How Facebook Became a Powerhouse Through Network Effects

https://www.wired.com/2012/05/network-effects-and-global-domination-the-facebook-strategy/

A network effect is any situation where every new user that joins a platform provides benefit to all users that already are a part of that platform. The network therefore gains more value for users as more people use it. In the social media industry, Facebook takes a lead by targeting people’s innate drive to project their identity and network to other users. As the user-base expanded, this network effect compounded into a bandwagon effect – nobody wanted to be left out from this platform.

This positive feedback loop of new users made Facebook a monopoly in the social media industry. As it had more opportunities for connections than its competitors, new users chose it over other platforms. “I think that network effects should not be underestimated with what we do,” explained Facebook CEO, Mark Zuckerberg, whose platform now has over 2.6 billion users, due to the clever play on innate human tendencies to not want to be left out (Vogelstein). This idea is further compounded by a professor at the University of Halle, who stated that “once a social network effect-based company gains a clear leading position compared to competitors, it becomes so clearly more attractive than its competitors that a winner-takes-all trend sets in. ”

This directly relates to the idea of a global friend network – it is unlikely that there will be any single person that is completely isolated and unconnected. Humans are social creatures, and huge network effects can have large impacts on how the world works. The above article further emphasizes this idea by highlighting that network effects play such a huge role in Facebook’s power that the company could perhaps overtake Google’s Search engine. As so many people are present on Facebook, Google feels threatened by the fact that there may be enough information on Facebook itself when it comes to searching for answers online.

November 6, 2020 | category: Uncategorized

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Network effects in Facebook

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ICEEG '18: Proceedings of the 2nd International Conference on E-commerce, E-Business and E-Government

Network effects are one of the defining properties of the digital economy. It has been shown, both in theory and practice, that network effects contribute to the growth and potential success of businesses in the digital economy. In this paper, we present a quantitative case study of network effects in Facebook for the period 2011 to 2017. We estimate the value of Facebook, and analyse how this value depends on the number of Facebook users. Our results show that there are strong network effects in Facebook, as the value per user increases more than estimations obtained from Metcalfe's law. We also outline a general theory of the strength of network effects, and quantitatively estimate the strength of network effects in Facebook.

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Software organization and properties

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E-commerce infrastructure

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The Hong Kong Polytechnic University, Hong Kong

Korea University, South Korea

Tamkang University, Taiwan

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Feng Chia University, Taiwan

St. George's University, Grenada

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• Published: 13 June 2018

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• VC Investing

Network Effects: What VCs Really Look For (With Case Studies)

• 13 minute read

I'm often surprised by how little many Venture Capitalists know about network effects. When I ask them to define network effects and explain how to spot whether a startup is taking advantage of them, they often miss significant aspects. Most Investors confuse network effects with virality, winner-take-all dynamics, and even product-market fit. It's paramount for VCs to correctly identify network effects, as they are highly advantageous to the companies they invest in. Incorrectly calling network effects, on the other hand, may lead to a bad investment decision. In this post, I explain the fundamentals of network effects and provide a framework for VCs to quickly assess whether or not a startup is taking advantage of them. I illustrate this analysis grid with case studies of startups exploiting network effects—and others that seem to do so, but don't.

In This Article

• Bell's phone: the textbook case study

Characteristics of network effects

Opentable: exploiting network effects to increase market penetration, vcs beware: network effects are not just virality.

• Case Study 1: Hotmail And Viral Marketing
• Case Study 2: Dropbox And Referral Marketing
• Facemash: Zuck's Foray into Network Effects
• Facebook: Lesson Learned
• Utility vs. Community
• Conclusion: tl;dr

What are network effects and where do they come from?

Venture Capitalists frequently struggle to give a simple definition to explain this central concept in Venture Capital investing. Here's the one I use to train VCs in my program:

A network effect occurs when the value of a product or service increases with each additional user.

Let's use a simple example to illustrate this definition.

Bell's phone: the textbook case study

Alexander Graham Bell's invention of the phone in 1876 revolutionized the way people communicated. Bell saw the potential for a device that could transmit sound through electric signals over long distances. After several years of hard work and experimentation, he was able to patent the telephone.

At first, people were skeptical about using telephones since there wasn’t any infrastructure for them. But Bell had a plan. In a move that could be considered the first tech growth hack in history , he let people use the phones for free if they agreed to have them installed in their homes and businesses. This was a stroke of genius as it incentivized more people to get on board with the new technology and start using it.

As more people began using phones, they started to realize how useful they were and how convenient it was to be able to communicate with someone far away simply by picking up a receiver and speaking into it. This created a network effect whereby each additional user increased the value of phone service for everyone else who was already subscribed.

By 1900, there were nearly 600,000 phones in Bell’s telephone system ; that number shot up to 2.2 million phones by 1905 and 5.8 million by 1910. It was clear that Bell’s invention would be incredibly successful and forever change how we communicate.

It is easy to get lost in the various types of network effects at play in tech today. The folks at VC firm NfX are probably the most sophisticated on the topic (hint: it's in their name). They've listed no less than 16 different kinds of network effects, including social network, product, infrastructure, and data network effects.

However, since most VCs don't have time to dig into the subtleties of network effects, here are some rules of thumb to identify them:

• Is one more user making the experience better for existing ones? Again, let's go back to the telephone. If you have the only telephone ever built, it's not a fun experience. Two connected users vastly improve it. Three even more. And so on.
• Is the value of the product or service increasing exponentially or proportionally? Social networks are a great example of an exponential increase in value. As users multiply, it becomes easier to find more people to connect with and discover more content. Even with a small number of users in its early days, Facebook was able to generate incredible growth.
• Is the impact direct or indirect? In some cases, it is harder to identify network effects because they are indirect: adding users strengthens the product for all. Think of operating systems such as Windows, which is successfully integrated into third-party applications and with hardware manufacturers.
• How fast will the network effects materialize? The velocity of network effects is a significant characteristic—the faster they do so, the stronger their impact on a company’s performance. Consider WhatsApp: its rise to becoming one of the most popular messaging platforms was incredibly rapid. It took WhatsApp four years to reach 100 million users and two more years to quadruple to 400 million. That number grew again by 2.5x in the next two years.

One more concept central to network effects is the ability to generate increasing returns . Standford professor Brian Arthur described them as the tendency of products and services to become more valuable as they gain in popularity. In a 1996 Harvard Business Review article containing one of the first mentions of network effects , he examined how increasing returns can create a cycle of advantage for certain firms and disadvantage for others.

That's where we go next: understanding why VCs are so crazy about network effects.

Why Do Network Effects Make Companies Valuable?

Network effects are incredibly advantageous for businesses capitalizing on them because they provide an edge over competitors. In Venture Capital lingo, we call it a moat , a fancy word for a barrier to entry or to exit.

Businesses want two things: repeat customers (people loyal enough to return) and new customers (people willing to try out something new). Network effects address both of these needs. They incentivize existing customers not only to stay but also to recommend the product/service by sharing with their friends who may be willing to try something new.

The snowballing nature of network effects creates exponential growth potential. Small numbers can add up quickly.

In a recent interview with Tim Ferris, Benchmark's Bill Gurley illustrated how network effects made OpenTable, the restaurant reservation app, so successful . Like most platforms, adding more restaurants made the experience better for users; and adding more users made it more profitable for restaurants to use OpenTable's service.

The startup solved the “chicken & egg” problem inherent to platform business models by providing a valuable service for both restaurants and customers. By connecting these two groups, OpenTable was able to establish an extremely valuable revenue stream for itself and become one of the most successful companies in its field. The key to its success was recognizing how powerful network effects can be in this type of business model and leveraging them accordingly.

We started looking for startups exploiting network effects because they tend to cause outlier outcomes. Bill Gurley - Benchmark (Source: The Tim Ferriss Show)

Bill Gurley also tells a story involving OpenTable's CFO, who warned him that he was going to quit after realizing that the service would never become profitable. It turned out that the veteran retail professional had greatly underestimated OpenTable's potential market penetration, capping it at 17% in his model. However, as the company grew, it got close to 90% market penetration in the cities in which it was active.

Once a significant enough base of restaurants had joined the service, it was hard for others to ignore it. The momentum reached by OpenTable's network effects allowed it to rapidly expand its user base and become one of the leading providers of reservation services in the world.

Venture Capitalists evaluating investment opportunities often confuse network effects with virality, and sometimes with adjacent concepts such as winner-takes-all markets. Although they are often necessary for network effects to develop, these characteristics are only peripheral. As a result, Investors may lend incorrect qualities to the startups they invest in, ending up surprised when the unassailable moat does not materialize.

I believe that Investors confuse virality and network effects because both are related to the idea of rapid growth. However, there is an important distinction between the two concepts:

• Network effects occur when customers become more valuable as the user base grows
• Virality is a measure of how quickly a product or service spreads through word-of-mouth

Network effects are driven by customer satisfaction and loyalty, while viral strategies rely on incentives and referral programs to encourage people to share a product or service with their colleagues, friends, and family.

Let’s take an example.

I've been using the online appointment booking tool Calendly for a couple of years, and I'm highly satisfied with it. It's helped me eliminate considerable time I used to spend (re)scheduling meetings. Before it was as popular as it is now, I systematically recommended Calendly to clients, friends, and everyone in my network. Word-of-mouth at its best.

When a new user joins Calendly, booking appointments becomes easier for that person. But does it add anything to existing users’ experience? Not really. Existing users act as ambassadors. The first adopters are so committed that they take time to explain to their friends why they should also use such a tool. It’s virality at its best and helps reach product-market fit faster.

But there are no network effects. Competing services have met some success, and although there is no reason to stop using Calendly, it's not difficult to switch to another provider—compared, say, to moving out of Facebook or LinkedIn to join a competitor.

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What Are Network Effects and Why Are They Important?

A guide to the principle that powers some of the biggest platforms and startups today.

Many of the most successful companies of the 21st century — from Facebook to Twitter, Venmo to eBay — have been designed with network effects in mind. Their products are platforms that facilitate interactions. Sometimes the interactions are between peers, sometimes they’re between buyer and seller. Whatever the case, one principle tends to hold true: The more users that join the service, the more beneficial the service becomes for each user. That’s the basic definition of a network effect.

What Are Network Effects?

Economist Jeffrey Rohlfs articulated the idea of network effects in a 1974 paper , in which he observed that the utility a person derived from a telephone went way up as more people bought them, since a phone does you no good if you’re the only person who has one, but is quite handy if everybody you know is just a dial away. So as more people adopted the phone in the ensuing years, more interactions became possible, which made phones more useful, which enticed even more people to get them — increasing their value even further. This positive feedback is the network effect at work.

In today’s context, understanding network effects helps make sense of the massive growth experienced by many startups whose successes are tied to the interactions that take place on their platforms or marketplaces.

Unlike huge companies of the industrial age, which grew bigger as they produced more efficiently, these startups aren’t valuable because of “the capital they employ, the machinery they run, or the human resources they command. They are valuable because of the communities that participate in their platforms,” wrote the authors of the book Platform Revolution .

Take Airbnb. It doesn’t own any hotel rooms. It has millions of users interacting with each other on its site, though, and that is what gives it a competitive advantage.

And if you’ve ever shared a file through Dropbox, hailed a ride on Uber, booked a reservation on OpenTable or bought a coffee table through Craigslist, you’ve experienced value because of the number of people using that service and the quality of those users. The success of each of these companies is due in part to each one figuring out how to get enough people to join their platforms. It’s a “critical mass inflection point” that kicks growth into an exponential trajectory.

Companies boosted by network effects gain a significant edge, keeping users engaged and competitors at bay. And investors take notice; an investor gets a twinkle in their eye when they come across a startup with a business model that’s conducive to network effects.

Network effects are responsible for 70 percent of the value created by all tech companies since 1994, according to a 2017 report by the venture firm Nfx.

“When you think about what company to build, or where to invest, make sure to think network effect,” wrote investor and Nfx co-founder Gigi Levy Weiss. “Otherwise you’re giving up on the best defensibility for a startup and may not get the best result.”

Network Effect Examples

There are two main types of network effects — same-side network effect and cross-side network effect — each of which can be either positive or negative for a business.

Same-Side Network Effect (Positive)

In this case, more users of the same kind make the service more valuable to each user. This describes the telephone example above. Here’s another: If you write on Microsoft Word, it benefits you when others have Microsoft Office, too, so you can share compatible files with them.

The same goes for mobile payment services Venmo and Zelle, whose users can only capitalize on the convenience of exchanging money via phone if others register. Online gaming platforms, like Xbox Live or Playstation Network, also fit the bill.

A platform’s network effect refers to the interactions that take place between consumers (or between producers), not between consumers and producers.

Related Reading What Does a Growth Hacker Do in Marketing?

Same-Side Network Effect (Negative)

Same-side network effects can sometimes have negative consequences. Consider ChatRoulette as an example. The video chat site, which randomly connects users with each other without the need to register a login, exploded in popularity in the late 2000s. But for many users, the increasing number of unsavory participants on the site polluted the experience. Eventually this drove high-quality users away and the value of the platform shrank.

Another example is the congestion that clogs up a ride-hailing app like Lyft. As more users sign up and request pickups, customers may be waiting longer or spending more for a ride.

Cross-Side Network Effect (Positive)

This type of network effect occurs when people on one side of a market benefit from an increase of participants on the other side of the market. In view here are companies like Airbnb, OpenTable, Craigslist and eBay, all of which cultivate interactions between buyers on one side and sellers on the other. It’s easier, for example, to find an overnight accommodation on Airbnb when there are more available houses to choose from. And it becomes easier to rent out your house when more and more users join the site.

You can also see this with food delivery services GrubHub and Doordash. Delivery drivers benefit when there are more customers submitting orders and vice versa. Drivers earn more money fulfilling more orders and customers see shorter wait times when there are ample drivers to handle the workload.

Cross-Side Network Effect (Negative)

There are times when too many producers on one side of the market leads to drawbacks for the users on the other side. For instance, a search engine website that features an increasing amount of advertisers on the first page of results might annoy some users and eventually push them to abandon the platform. The same can be said for a streaming service, news site or e-commerce platform.

Related Reading Why Many Would-Be Network Effect Companies Fail in Their Infancy

Network Effect Case Study: OpenTable

The story of OpenTable serves as a helpful example to demonstrate the power of cross-side network effects. The company, founded by Chuck Templeton in 1998, was the internet’s first restaurant-reservation-booking service.

Michael Xenakis worked at OpenTable from 2000 to 2016, serving as managing director of its international operations, and, before that, as senior vice president of product management. He spoke with Built In about how the company experienced network effects and grew into an internet giant.

Solving the Chicken-and-the-Egg Problem

The story begins with founder Templeton’s lightbulb moment. Making a dinner reservation over the phone can be a lousy experience — the phone rings and rings and never gets answered, or you get put on hold for several minutes and the host eventually forgets you’re there. If people can quickly and easily book reservations online for hotels, flights and rental cars, why can’t they do the same for restaurants?

OpenTable’s solution was to bring diners and restaurants together on one online platform, so the two sides could find each other and make a reservation happen without a hitch.

The company had a chicken-and-egg problem to deal with first. How would it convince restaurants to sign up? The allure of more diners to fill empty seats was nice, but at this point in time, there weren’t any diners on the site. Tough sell. But how would OpenTable attract diners to its site if there weren’t any restaurants listed?

OpenTable’s strategy was to get restaurants on the platform first. But in order to convince them to sign up, OpenTable sold the restaurants something of stand-alone value, a service that would benefit them immediately — regardless of whether extra diners ever filled their tables. It sold them software.

The software functioned as an electronic reservation book that helped restaurants keep track of patrons and manage tables — tasks for which hosts had long relied on pen and paper (or their memory). Buying OpenTable’s software also meant that restaurants would join its online reservation service, but that part, Xenakis said, was not yet a selling point. In his estimation, when OpenTable was starting out, about 90 percent of its value proposition was tied up with its software, while only 10 percent was its reservation platform.

That was good enough to get started. OpenTable sold its software to dozens and dozens of high-end San Francisco restaurants, priming the supply side. Eventually, it had enough of them to create real value for diners.

“Fifty to 100 restaurants in San Francisco definitely would have an inflection point where the value to the diner was real,” Xenakis told Built In. “In the world of fine dining, that’s plenty of restaurants.”

Network Effect Kicking in

People started using the site. Whether they wanted steak or sushi — tonight or next week — they could book a reservation online at one of many local restaurants listed on OpenTable’s website. The service was fast and easy.

Buzz quickly spread about the site, and more and more hungry Bay Area patrons started using it instead of reaching for the phone when they wanted to make a reservation. “We didn’t spend any marketing dollars,” Xenakis said. “We just relied on the word-of-mouth of diners.”

“The diner proposition grew and it truly was a quintessential network effect that took root. And it took off from there.”

The influx of patrons flooding the site made joining OpenTable seem like a no-brainer for restaurants in the area; they wanted to go where the diners were.

“The diner proposition grew and it truly was a quintessential network effect that took root,” Xenakis said. “And it took off from there.”

Growth accelerated at a blinding rate in OpenTable’s early days, according to Xenakis. The company would concentrate on one market, drill down deep, then move on to the next one. First, they targeted all the U.S. cities in which there was an NFL team. Then it targeted the outskirts of each of those cities. Over time, there was scarcely an urban area that didn’t have a presence on OpenTable.

Recommended Reading 8 Things I Wish I Had Known Before Founding My Startup

Sustaining Growth

Since both diners and restaurants were on OpenTable — and each wanted something from the other — both parties were incentivized to stick around and keep using it.

In the book Platform Scale , business scholar Sangeet Paul Choudary writes , “Network effects also create stickiness,” later adding, “For a platform business, user commitment and active usage, not sign-ups or acquisitions, are true indicators of customer adoption.”

OpenTable explored additional ways to keep users engaged on its platform rather than booking a reservation the old-fashioned way (or flocking to a competitor). One way it did this was implement a loyalty program.

Customers of OpenTable received loyalty points for booking reservations at certain restaurants on off hours and off days. This was good for all parties involved: the restaurants (who didn’t want table space to go to waste), OpenTable (who got a kickback from the restaurants) and customers (who received points, which could be redeemed for money).

By Xenakis’ account, OpenTable didn’t anticipate just how much customers would be driven to use the platform so they could rack up points.

“Once we had [the points system] out there, it turned out that diners just loved collecting points — without even knowing what they were for,” he recalled. “So our biggest thing early days was [diners asking] ‘Where are my points?’”

The stickiness kept users coming back to the platform and filling seats.

Eventually, OpenTable’s growth began to flatline, at which point, according to Xenakis, the company beefed up its search engine marketing to stimulate growth on the demand side.

The company invested in the supply side too. It launched international operations, doubling its total addressable market.

Network effects helped propel OpenTable to an IPO in 2009 and an acquisition by Priceline (now Booking Holdings) for $2.6 billion in 2014. Today, more than 54,000 restaurants use OpenTable’s platform, which, according to the company’s internal data, books an average of 12 diners every second .

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What Is the Network Effect?

Companies that rely on signups, memberships, or registrations truly rely on their users to expand their business. By harnessing the influence of the network effect, digital platforms can gain momentum and work toward becoming industry leaders.

What are network effects, and why do they matter to digital platforms? Learn how the network effect can help businesses scale and establish strengths in their industry.

The Network Effect Defined

The network effect is a business principle that illustrates the idea that when more people use a product or service, its value increases. The network effect significantly applies to digital platforms, dating all the way back to the internet itself. When the internet became more widely used, more people relied on it to conduct work, deepen personal connections, and for research and other functions.

Example: social network platforms

The network effect is especially prominent on online platforms that encourage users to add other users to the platform’s network. Social networks such as Facebook and Instagram are key examples of the network effect. The more people that use the platform in an individual’s network, the more likely it is that individual will use the platform, too.

Example: E-commerce platforms 

E-commerce platforms, such as Amazon, also benefit from the network effect. As more retailers opt to sell their goods on Amazon, the more consumers will look to Amazon to shop. The reverse is also true.

Sustaining User Engagement

As platforms scale, they need to be prepared to keep users engaged on the platform or risk losing their users to competitors. For example, a website that is slow to load or that isn’t equipped to handle an exponential amount of users can crash, leaving users frustrated. 

A company that doesn’t grow their hiring practices as the platform grows may lead to user experience problems that lead to customer drop-off. For free platforms, the user experience must provide value to users to continue to fuel network effects benefits.

Types of Network Effects

There are two main types of network effects: one-sided network effects and two-sided network effects. Both rely on growth, but the difference is in the parties that grow the network.

One-sided network effects

One-sided network effects relate to the growth of a single group of users of the platform. The more users on a platform, the more market share that platform will likely claim. The platform’s business growth can lead to more innovation on the platform itself and more value to its users.

A few examples of one-sided network effects include:

• WhatsApp messaging platform 
• Skype communication platform 

Two-sided network effects

Two-sided network effects relate to platforms that benefit from the growth of two groups. In a marketplace platform, the more consumers on the platform, the more sellers will want to secure their presence there. 

The more sellers there are on a platform, the more choices users have. This can benefit the user and the seller when users don’t leave the site in further search of what they want.

Examples of two-sided network effects include:

• eBay online retail bidding platform
• Airbnb home-sharing platform

In two-sided platforms, ideally the growth of one side moves at a pace that benefits the other side. For example, too many home searchers on Airbnb, but not enough homes may lead to user drop-off. Not enough consumers on a platform may discourage sellers from focusing their time there.

Complementary Network Effects

Complementary network effects relate to occurrences in which separate companies mutually benefit from the growth of related companies. For example, an app developer that specializes in iOS mobile applications will likely benefit from more smartphone users buying iPhones.

Another type of complementary network effect occurs when one platform type becomes popular and leads to platform competition, resulting in more users among similar types of platforms.  For example, the popularity of one food delivery platform could lead to the expansion of others in a geographic area. Users of GrubHub may also sign up for Uber Eats to compare delivery prices before ordering.

This type of network effect can lead to more users signing up for similar types of platforms. That can boost budding platforms and create a more saturated market.

That can lead to more innovation among platforms to differentiate their offering. Or, in the case of Facebook and Messenger parent company Meta acquiring competitors like Instagram and WhatsApp, some companies choose to purchase their competition when the network effect starts to diminish.

Examples of the Network Effect

For startups that have a goal to quickly scale their businesses, the network effect is key.

Different kinds of digital platforms are influenced by network effects, including:

• Social media networks : Facebook, LinkedIn, Instagram, Snapchat, Twitter, Pinterest, TikTok
• Digital transaction platforms : PayPal, Venmo
• User review sites : Yelp, Tripadvisor, Google
• Travel platforms : Expedia, Hotels.com
• Communication applications : WhatsApp, Messenger
• Ecommerce platforms : Amazon, eBay, Etsy
• Ticket resellers : Ticketmaster, StubHub, SeatGeek
• Food delivery platforms : Uber Eats, Grubhub, DoorDash
• Freelancing sites : Fiverr, Upwork, Thumbtack

Dating applications and ridesharing platforms also rely on user growth to continue to gain interest, sign up new users and sell services.

Tinder is an example of a platform that has relied on one-sided network effects to grow its platform. The dating application requires an abundance of users to give its users more choice and a greater ability to find their perfect match.

Uber is an example of a platform that relies on two-sided network effects. The more “riders” who use the platform, the more “drivers” will be motivated to sign up to provide driving services.

Benefits of Network Effects

As platforms grow their user base, so does their ability to sell services on the platform or make money by charging fees for users. That’s why many platforms operate on a free or “free-mium” basis. Getting the user to sign up is critical to enable the platform to grow and start to monetize.

Free-mium apps

Facebook, for example, has always offered free signups. For several years, the site operated as simply a social networking site where users could connect with other users. Then, Facebook Ads launched, giving the platform a way to make money while continuing to offer a free service to users and invest in making it better.

For two-sided platforms like Etsy, the platform can charge listing fees to host product sales on the platform, or charge sellers a commission percentage.

For free-mium apps, the app can offer free signups and play, but then give users the ability to purchase upgrades. For example, Pokemon Go, a smartphone videogame, charges nothing to sign up and play. Users do have the option to purchase tickets for special events or buy unique clothes for their avatars. The “add a friend” aspect to Pokemon Go motivates users to share the game with their actual friends, which can lead to more purchases for the game developer.

Network effects underly startup success

Venture Capital firm NFX says that in 2022 network effects contributed 70% of the value in tech. To arrive at that percentage, NFX began studying 336 top tech companies in 2017 that were valued at over $1 billion. They found that network effects underlie the success of tech startups that flourished.

NFX notes that Facebook Messenger uses the personal network effect to get more users to join when their friends join. It additionally uses the personal utility network effect for profound daily communication, for example, in communicating with friends and relatives. 

Learn More About Network Effects in the Global Digital Economy

Network effects can influence everything from product or service pricing to product positioning and development in the digital economy . It makes perfect sense for some startups to create completely free products when they intend to use the network effect to scale their user base and monetize their service or offering later. 

For innovative companies that are introducing novel products to the marketplace, they could achieve a coveted position as a winner-takes-all product or service. As we’ve seen with platforms ranging from social networks to ridesharing brands, achieving dominance in a space can lead to lasting profits and loyal users.

If you’re interested in learning more about topics like network effects and digital transformation, enroll in the Managing in the Global Digital Economy course . This four-week online course requires just two hours of time each week to complete. You’ll learn about how to use new technology and omnichannel strategies to adopt a competitive advantage for your business.   Request information for yourself or your team today. 

The Wharton School is accredited by the International Association for Continuing Education and Training (IACET) and is authorized to issue the IACET CEU.

Assessing the Strength of Network Effects in Social Network Platforms

Author abstract.

Network effects have risen to the forefront of platform competition discussions (e.g., the House Judiciary investigation of competition in digital markets, claiming that Facebook, for example, is entrenched due to strong network effects and high switching costs). While newer literature has developed much more sophistication in characterizing network effects, common regulatory perspective often assumes more simplistic views.

Paper Information

• Full Working Paper Text
• Working Paper Publication Date: February 2021
• HBS Working Paper Number: 21-086
• Faculty Unit(s): Technology and Operations Management
• 18 Jun 2024
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• Network Effects

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The Dynamics of Network Effects

D'Arcy Coolican and Li Jin

• Hacker News

The most successful companies and products of the internet era have all been predicated on the concept of network effects , where the network becomes more valuable to users as more people use it. This is as true of companies like Amazon and Google as it is for open source projects like Wikipedia and some cryptocurrencies. At its core, the theory behind network effects suggests that platforms and products with network effects get better as they get bigger — not just in value to users, but also in accruing more resources to  improve their product , thus strengthening the “flywheel”.  

Instead of seeing winner-take-all markets, we’re seeing all kinds of network effects companies — from messaging apps to sneaker marketplaces —  splitting  markets among multiple players. Furthermore,  even companies that appear to have initially won the market and seem to have established a  deep moat  — from dating apps to trading platforms — are struggling to maintain their position against copycats and new entrants. Just look at what Instagram Stories is doing to Snap, surpassing it recently even among its dedicated teen demographic. 

Does all this mean that network effects as we know them are dead? No, but they’re more dynamic than ever. 

While we know that not all network effects are created equal, they don’t evolve equally either. Every product has different types of network effects that mature and develop differently over time. If anything, most network effects businesses are changing faster than ever before. So how can entrepreneurs and founders navigate this era of seemingly diminishing network effects? The trick is to know what your network effects look like today, but also project how they’ll evolve over time. To that end, you’ll need to understand three aspects of your company and how they could change going forward: 1) your value proposition , 2) your users/inventory , and 3) your competitive ecosystem . Otherwise you could get caught flat-footed, claiming that network effects are dead.

Here’s some principles for forecasting future network effects beyond a present-day, static view. Then, once you know you have network effects, see this post for how to measure and keep them.

1) Value prop: Not all products are created equal

A company’s or product’s network effects don’t always remain on an increasing-returns (or even straight) line trajectory as it grows; it could asymptote, or hit an inflection point and even reverse. The key for founders is to know what value proposition drives their network effects, understand whether they’re weak or strong, and then pay close attention to how they will evolve — especially as you iterate your way to new value propositions and additional layers of product-market fit.

Let’s take a look at a few examples:

Decentralized platforms. If one thinks of bitcoin, for instance, as digital gold, then the network effect would be that more buyers/sellers mean more liquidity which increases the value of the platform for all. But if one thinks of bitcoin as a payments platform, then more is not necessarily better as long as it experiences network congestion or other friction. It’s an interesting case to consider (not for investment purposes), simply because it’s an example of how different value propositions for the same platform can strengthen or weaken network effects accordingly. It’s also a great example of how additional features (e.g., scaling, increased throughput, and improved transaction speeds) can help the value proposition evolve, shift, or even create a new trajectory of network effects.

The goal of sharing the above examples is to show both the nuances and evolution of network effects. If you’re not paying attention to these factors, you could be left believing that network effects just don’t exist anymore in a particular business, when it might be a matter of unlocking new value.

2) Users and Inventory: Not all users are created equal

The type of users and inventory your product or platform has today, and the types you’re adding, are fundamental in understanding and projecting the trajectory of your network effects.

Commoditized vs. differentiated supply

An important factor in projecting network effects — especially in two-sided platforms/marketplaces — is whether users/inventory are commoditized or differentiated.

In ridesharing, the customer (rider) is relatively agnostic to the underlying service provider/inventory because they perceive the supply (drivers/cars/transport) as interchangeable and therefore commoditized. Platforms with relatively commoditized inventory — from on-demand storage companies to delivery companies — are more likely to see their network effects asymptote once they reach a base level of liquidity. For a category like ridesharing, moving into adjacent businesses (like Lyft has done with their healthcare initiative or Uber has done with food delivery) allows differentiated — yet still substitutable — inventory, potentially increasing the strength of the network effect.    

Platforms/marketplaces with more differentiated inventory have stronger and longer-lasting network effects, because they have a diversity of inventory that suits the unique preferences of customers (while maintaining just-enough substitutability across that inventory as well). For example, AirBnB can show users every iteration of lodging from $225-$325/night in Los Angeles, which overlaps with someone else’s search for something that costs $150-$250 and has a both a balcony and a hot tub. The platform is therefore more valuable on both sides of the marketplace than a site that just shows a commoditized set of standard and executive rooms. The network effects remain strong not only because it reaches a base level of liquidity across all these different types of inventory (making them valuable to more users), but because it also continues to see increasing returns with new supply.

But the more differentiated the inventory, the more the platform needs to do a good job of curation and matching. That in and of itself also increases the overall defensibility of the platform, and keeps the network effects curve strong over time.

Type of incremental user

Beyond the commoditized nature of users and inventory, however, not all members of a given network are equal. Some are more — or less — valuable than others. For instance, a restaurant that is very popular and located nearby a lot of users adds more value to the OpenTable network than a restaurant with bad food in the middle of nowhere.

When you forecast out your network effects — and more importantly, your growth strategy for acquiring and engaging more users — you will need to pay attention to the incremental users you’re likely to attract. Are they network “ contaminants ”, “neutrals”, or “contributors”? For a social network, adding a troll that disengages other users is a pollutant who removes value. Adding a lurker is neutral since that person doesn’t add or subtract any value from the network. Adding a great content producer contributes an enormous amount of value to the network.

So, making sure to incent the users you want while disincenting the ones you don’t want, is key. This is why most great platforms also invest heavily in curation mechanisms to screen and remove bad inventory/users (e.g., Wikipedia’s editors, Airbnb’s reviews/onboarding, etc.). Unfortunately, these screening mechanisms don’t always work and sometimes the cost of finding strong contributors becomes very high, so the calculus of growth relative to cost matters a lot here.

3) The Competition: Not all markets are created equal

The nature of your market as well as competitors and substitutes is also critically important to understanding and forecasting network effects.

Network overlap

While network effects businesses tend to be more defensible at scale, they are not immune to competition. But for these types of businesses it’s not just a matter of figuring out who your direct competitors are — you also need to think about the network overlap . If someone else has a similar network to yours, there’s always existential risk they’ll move into your market. Because they have a similar network already, they’ll more easily be able to enter your space (Instagram’s foray into Snapchat-like disposable “Stories” is a good example of this). This is also true where the competition may already have registered a superset of your network (e.g., DoorDash and Uber Eats; Didi and Uber in China).  

Switching costs

Low switching costs to competitors can also lower network effects. Seamless sign-up and onboarding is usually great for adding users to your product, but if your competitors have that same seamless onboarding, users might multi-tenant. It’s easy to use multiple dating apps or maps products because of low barriers to entry and switching costs.

Multi-tenanting to meet demand

Network effects are weakened when users are unable to use a single platform to accomplish their goals. Jobs marketplaces are a good example here: companies are likely to list their job openings on multiple hiring platforms (i.e., multi-tenant), since hiring is a critical part of running a business that absolutely needs to be fully fulfilled, and no single platform is likely to fulfill all of their hiring needs. Where one side of a platform is multi-tenanting, there will usually be increased pressure on the operator — in terms of pricing, features, and necessary liquidity — which can turn the economics upside down.

We go into detail on how to measure all of these in our other post, 16 metrics for measuring network effects:  https://a16z.com/2018/12/13/16-metrics-network-effects/ . 

*     *    *

Fast followers can move faster than ever. Instagram Stories can challenge Snap in, well, a snap. Hiring marketplaces can get thousands of employers in no time. The API-ization of everything makes it easier to do anything and everything: Stripe alone means that every marketplace can incorporate payments in an hour, whereas previously, eBay had some protection by building and acquiring its own payment systems.

The increasing speed of product iteration, the pace at which networks can scale, and the ease with which competitors can get started has therefore dramatically changed how we project network effects in businesses. Instead of winner-take-all markets where early movers may have once had a seemingly lasting advantage, network effects change more quickly than ever. Especially where specific factors — an asymptotic value proposition, network overlap, increasing number of contaminants, etc. — can lower the platform’s ability to generate a sustainable network effect in the future.

This is not to depress anyone! Network effects will continue to underpin the most impactful software businesses. It’s merely a reminder to founders and other product builders to be aware of what will change and why, so you can do more to plan and address these issues instead of being blindsided by them. Network effects aren’t dead, but they’re more dynamic than ever. By understanding what your network effects look like today and where they’re going tomorrow, founders can design network effects and other moats with intentionality, instead of being tossed on the winds of change.

Long live your network effects!

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D'Arcy Coolican Prior to joining a16z, he co-founded Frank, a social lending platform that used behavioral economics to make it easy to lend and borrow money with friends and family. He began his career at McKinsey & Co, where he was an engagement manager in the TMT practice.

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What Are Network Effects?

• 12 Nov 2020

When pricing your products, services, or platform, you should consider several factors to ensure you’re doing so effectively. You need to understand the unique value your product offers , your customers’ willingness to pay for that value, and the profit margins your company requires to reach its goals.

It’s also important to consider whether your product or service is subject to any network effects, which could impact your pricing strategy.

Not familiar with the concept of network effects? Below is an overview of what network effects are, how they can impact your business strategy, and what skills you need to properly leverage them to increase your profitability.

According to the online course Economics for Managers , the term network effect refers to any situation in which the value of a product, service, or platform depends on the number of buyers, sellers, or users who leverage it. Typically, the greater the number of buyers, sellers, or users, the greater the network effect—and the greater the value created by the offering.

“In other words, the willingness to pay, for a buyer, increases as the number of buyers or sellers for the business grows,” says Harvard Business School Professor Bharat Anand in the course.

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Examples of Network Effects

Many of today’s most popular companies and startups are heavily influenced by network effects, such as:

• E-Commerce: eBay, Etsy, Amazon, Alibaba
• Ticket Exchange: StubHub, Ticketmaster, SeatGeek
• Rideshare: Uber, Lyft
• Delivery: Grubhub, DoorDash, Uber Eats, Instacart, Postmates
• Social Media: Facebook, Twitter, Instagram, LinkedIn, Snapchat, Pinterest

What each of these companies has in common is that the value they provide to customers increases as they scale and acquire more users. Etsy and eBay offer vastly more value to users if one million, instead of 100, sellers use their platforms. Uber and Lyft provide greater convenience and reliability to riders when more drivers join their platforms. When it comes to social media sites, users find the channels more interesting and varied as more people sign up.

Direct and Indirect Network Effects

Not all network effects are the same. They’re often broken into two different types: direct and indirect.

Direct network effects occur when the value of a product, service, or platform increases simply because the number of users increases, causing the network itself to grow.

Social media platforms primarily benefit from direct network effects because the service's value grows as a direct result of attracting more users.

Apple also benefits from direct network effects. The preferential treatment of messages sent from an iPhone to another Apple device (through iMessage) has helped the company expand its moat in the market.

Indirect network effects, on the other hand, occur when a platform or service depends on two or more user groups, such as producers and consumers, buyers and sellers, or users and developers. As more people from one group join the platform, the other group receives a greater value amount. This is best illustrated by the e-commerce and ridesharing examples noted above.

Why Are Network Effects Important to Understand?

According to Economics for Managers , the underlying principles of network effects imply that the business, website, or platform with the highest market share will be more successful in the long run. This means that its market share is likely to grow more substantially. For this reason, markets in which network effects play a major role are often referred to as winner-takes-all markets .

“Companies that can leverage or exploit network effects often experience rapid rates of growth,” Anand says. “Not just that: Once you’re ahead, you tend to stay ahead. Your demand keeps growing even faster as you get bigger.”

For a real-world example of this concept, look no further than eBay. When the company “wins” in a particular country, it tends to win big due to its prominence over the competition.

Network Effects and Pricing

Before pricing your product, service, or platform, it’s crucial to understand whether your market is subject to network effects. Why? Because the underlying logic that guides a typical pricing strategy reverses itself in markets where network effects are felt strongest.

To maximize profit margin, businesses typically price their products as high as possible without exceeding their customers’ willingness to pay. But when a market is subject to network effects, the driving concern isn’t so much profit as it is market share—especially early on.

This is because future customers’ willingness to pay depends on the number of existing users. By growing your market share early, you increase your ability to raise prices at a later date, once you’ve taken advantage of network effects and driven adoption of your offering as much as possible. For this reason, many companies price their products low early on or give them away for free.

The emergence of Facebook as a social media giant is an excellent example of this premise in practice. When Facebook launched in 2004, it was a free social media platform. By virtue of being free, the platform became more popular, capturing greater market share and eventually displacing Myspace, its primary competitor at the time. It wasn’t until 2007 that Facebook introduced ads in an effort to monetize its user base, and it wasn’t until 2013 that the company noticeably ramped up those efforts.

Using Network Effects to Your Advantage

By understanding the principles that drive network effects and the impact they can have, you can leverage them to increase profitability and grow your business.

“Once you’ve gained significant market share, you can often sit back and let the network effect take over,” Anand says in Economics for Managers . “Your existing buyers and sellers are, in effect, your sales force in attracting more buyers. You often have to do very little. This is also why, in markets with network effects, you see companies competing fiercely early on to get customers—even giving away the product for free—but then raising prices afterward once they have network leadership.”

Are you interested in further exploring network effects, willingness to pay, and other frameworks that can guide your pricing strategy? Explore our eight-week course Economics for Managers and other online strategy courses , and learn more about how to develop effective pricing strategies.

About the Author

Digital Innovation and Transformation

Mba student perspectives.

• Assignments
• Assignment: Competing with Network Effects

Textbook Network Effects: How Instagram Achieved Instagrowth

Instagram is the textbook example of how a business initially attracts users in the absence of network effects and quickly grows by leveraging those network effects.

Instagram is widely known for its “hockey-stick” growth ($0 to $1B valuation in two years!), but it is the textbook example of how a business grew so quickly with direct network effects.

A product displays network effects when more usage of the product by any user in the network increases the product’s value for other users in the network.

Suppose you developed a product that you believe has strong network effects. I’m sure this is the first question you’ll ask; if the platform is most valuable with other users on it, then how do you attract those initial users to use it? This classic chicken-egg problem has recently seen many innovative solutions in this digital age.

Pathways to a Just Digital Future

One way Instagram tackled this problem was to mitigate its adoption risk by providing standalone value. In other words, provide a feature that is useful if only ONE user were using your product. In a time when multiple photo apps provided cool photo filters for a small fee, Instagram provided them for free. Many of the initial users were initially attracted to Instagram because they could easily apply their favorite filters and then send to family and friends via facebook, twitter, or email for free.

Of course, the hope was that users would share on Instagram’s network, which they eventually did as more of a user’s family and friends adopted the platform. Many of those early users came for the tool and ended up staying for the network.

Another key way Instagram attracted users en masse was simplicity. Instagram was the personification of simplicity: not over-engineered, not too many features, no confusion. It focused on doing ONE thing extremely well. Within 5 seconds and a few straight-forward taps, you have shared your photo with your friends in the way you want it.

These two strategies created and accelerated this snowball at the top of this digital mountain. The network effects determine the slope of this mountain. Each additional user provided additional value to the network. Instagram’s user base became its strongest recruiters. Eventually, the mainstream user simply had to be on Instagram to keep up with their closest friends. In other words, Instagram was able to generate natural virality and maintained it by staying true to its original intent, focusing on simplicity and user experience.

Instagram’s meteoric rise is obviously only the beginning of its story. Facebook’s purchase of Instagram was both smart and fitting; together they are learning how to monetize these mobile platforms with strong network effects. Will Instagram sufficiently capture enough value it initially created?

Student comments on Textbook Network Effects: How Instagram Achieved Instagrowth

I enjoyed reading this post. I completely agree that simplicity and a strong product (filters) drove initial growth. I think another contributing factor was that users could essentially re-set their friend group. While facebook users have hundreds of friends and their news feeds are cluttered with everything from political rants to cat pictures from friends they can barely remember, Instagram allows users to more carefully select those they want to follow. It will be interesting to see how their monetization strategy works out. The ads on Instagram feel a bit intrusive because they take over your entire phone screen and leave the user feeling tricked into even looking at the advertisement. I wonder if they should have taken an approach similar to pinterest where advertisements are curated and are more difficult to differentiate from regular pins.

This is a great post and I really enjoyed the perspective that IG was able to provide standalone value. The fact that the platform has value even without the network effects is really powerful. I am curious as to your thoughts on how the tie-up with Facebook will allow IG to capture value. Ads have started popping up on my IG feed but I’m not sure how users will react to this. It certainly makes sense from a business perspective, but will this in any way weaken the network effects that IG has built up? Or do we think that the network is strong enough to withstand any backlash?

This was a great read – thanks for sharing. I agree that Instagram had hockey stick growth and a meteoric rise in users and engagement in a very short period of time. However, I think there were other apps out there that also offered similar functionality for free (e.g. Hipstamatic). These other competing apps eventually fell to the wayside for the reasons you mentioned, and also because the quality of the Instagram app and product was so much higher than the rest. I read previously that Instagram actually worked with professional photographers to develop the limited filters they had, and also had strict “community management” to ensure the content on the platform was appropriate. The easy integration with Facebook was definitely a key factor in Instagram’s success as well. I wonder whether the acquisition strengthens Instgram’s network effects or detracts from them, as Facebook is now implementing advertisements on Instragram which would likely turn people off of the platform.

Great article!

It’s crazy to think back to Instagram’s debut on the digital scene. I still remember taking a test selfie photo and accidentally posting it on my Insta-feed! I believe Instagram has already begun to capture the value it initially created. For example, Instagram currently has sponsored ads that appear in an individual’s feed and takes a margin from the advertiser when users click on the content. Furthermore, Instagram has created opportunities for other companies to monetize and capture value. “Like to Know.IT” allows users with large followings to make money by linking items in their pictures to products available for purchase.

Great example of how quality, and subsidizing users made a difference in user adoption, and ultimately beating out other platforms with similar functionality. I think another important strategy Instagram had when it launched was to generate a lot of hype around the app by getting influential people to be amongst the first ‘posters.,’ as well as getting influencers to spread the word about the app. I think getting the word out in this way made a huge difference, in addition to capitalizing on Facebook’s and Foursquare’s already existing networks by integrating with those platforms.

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A New, More Rigorous Study Confirms: The More You Use Facebook, the Worse You Feel

• Holly B. Shakya
• Nicholas A. Christakis

Online social interactions are no substitute for the real thing.

Research has long suggested that social media can be harmful to users’ wellbeing. But past studies have often failed to acknowledge people’s baseline sociability or social media usage levels. In a comprehensive new study, the authors examined the impact of Facebook usage on wellbeing over time, and found that using Facebook was consistently detrimental to mental health. Specifically, constant exposure to people’s carefully curated posts led people to make negative comparisons to their own lives, and the sheer quantity of social media interaction often detracted from their ability to enjoy real-life experiences. Although social media can often feel like meaningful social interaction, this research demonstrates that it’s no substitute for the real thing.

The average Facebook user spends almost an hour on the site every day, according to data provided by the company last year. A Deloitte survey found that for many smartphone users, checking social media apps are the first thing they do in the morning – often before even getting out of bed. Of course, social interaction is a healthy and necessary part of human existence. Thousands of studies have concluded that most human beings thrive when they have strong, positive relationships with other human beings.

• HS Holly B. Shakya is an Assistant Professor of Global Public Health at UC San Diego. She specializes in social network analysis and social norms theory, and is currently on an NIH funded project to understand the social network and social normative determinants of adolescent fertility in the developing world.
• NC Nicholas A. Christakis  directs the Human Nature Lab at Yale University and is the Co-Director of the Yale Institute for Network Science. He is the Sol Goldman Family Professor of Social and Natural Science, appointed in the Departments of Sociology, Medicine, Ecology and Evolutionary Biology, and Bioengineering at Yale University.

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In light of Uber’s IPO, we’re releasing our Inside the Marketplace podcast about the 5 Lessons Founders can learn from their path to market dominance in addition to the in-depth teardown of Uber’s network effects strategy below.

Uber’s biggest asset is their network effects, and they know it. In their S-1 they emphasized that “the foundation of our platform is our massive network.” But there are cracks in that foundation.

Although network effects are the best type of defensibility still remaining in the internet age, Uber’s core network effects are deceptively weak. As we’ve described elsewhere, not all network effects are created equal. Uber’s network effects are asymptotic rather than true 2-sided marketplace nfx . Not nearly as strong.

Realizing this, Uber has made a series of moves to reinforce their core network with other defensibilities. Heading into their IPO, their success as a public company will depend on how effective those efforts prove to be.

In this essay, we look at Uber from a few angles:

• Why Uber’s core network effect makes them vulnerable
• What moves they’ve made to reinforce that core vulnerability
• 5 lessons Founders can take away from Uber’s story [Podcast]

Why Uber’s network effects are vulnerable

Knowing that network effects are central to their business, Uber included a diagram of their nfx in their S-1, describing it as a “liquidity network effect”.

With this diagram, Uber tries to show exactly how each new Uber user adds value for all the others — the fundamental definition of a network effect. They see their network effect as having 5 stages, each stage leading to the next.

Stage 1: Driver supply Stage 2: Lower wait times and fares Stage 3: More riders Stage 4: More riders per hour, higher earnings potential for drivers Stage 5: More drivers

This sequence explains how Uber’s cross-side network effects are supposed to work. But if you look closer and think a bit, you may notice that Uber deviates from the formula for a true 2-sided marketplace nfx in the transition from stage 1 to stage 2 — increased driver supply leading to lower wait times.

The loop breaks down at that stage because it’s impossible to go below zero minutes of wait time without violating causality and/or spacetime. Drivers can’t pick up riders sooner than instantaneously. What’s more, the difference between 0 minutes of wait time and a nominal 3 or 4 minutes of wait time for the average rider is very small from a utility perspective. Indeed, those 3 or 4 minutes of lag could even be useful for the rider grabbing their coat, waiting for the elevator, using the bathroom, saying goodbye to someone, etc.

Data bears this out. The average wait time for an Uber rider in late 2018 was 5 minutes. That’s probably close to optimal. The benefits of increasing supply beyond this point has steeply diminishing returns: you’d have to more than double the “fleet” of drivers to halve the wait times from 4 minutes to 2. That’s why Uber’s nfx “asymptote” early on — each marginal supply-side user after the 4-minute inflection point has diminishing incremental value for all other users, and the supply side will settle into an early equilibrium.

Barriers to entry for ridesharing businesses are relatively low as a result — it’s not hard to get down to 4 minute wait times. New ride-hailing apps can easily gain critical mass on the supply side in a given geo and try to compete, like Juno in New York did. Unlike true marketplaces such as OpenTable or eBay, Uber can’t establish an escalating supply-side advantage with their network effect, and their core business is vulnerable to new entrants.

Compounding this are the extremely low switching costs of ridesharing for both the demand and supply side, leading to rampant multi-tenanting . It costs nothing but a couple of seconds for riders to switch from their Uber to their Lyft app on their smartphone, meaning that many riders have both apps downloaded and decide based on price. Drivers can also simultaneously drive for both Uber and Lyft (and some third app) with little to no cost to them.

“The personal mobility, meal delivery, and logistics industries are highly competitive, with well-established and low-cost alternatives that have been available for decades, low barriers to entry, low switching costs, and well-capitalized competitors in nearly every major geographic region. If we are unable to compete effectively in these industries, our business and financial prospects would be adversely impacted.” – Uber S-1

Left unaddressed, competitive exposure due to this flaw in their network effect could get Uber into a situation like we see with the airline industry, where there’s razor-thin margins and high sensitivity to price competition. That’s a stark contrast to the high margin, winner-take-most market you normally see with network effects companies.

Uber’s ~$80+ billion valuation, not to mention its stated ambition to corner the $12 trillion global transportation market, is based on the assumption that it will end up looking more like a company with true network effects, not like an airline. So it stands to reason that they’ve been doing whatever they can to avoid the commoditized fate of the airlines. Enter reinforcement .

Mapping Uber’s reinforcement strategy

Having realized that it’s vulnerable at a time when the stakes are high, Uber is furiously building other defensibilities. Starting with a core network effect (even a weak one) makes this easier because they can leverage their large network to roll out new defensibilities.

As Uber puts it in their S-1, “each new product adds nodes to our network and strengthens shared capabilities, enabling us to launch and invest in additional products more efficiently”. The diagram below is a map of Uber’s attempts to add these defensibility “nodes” sequentially over time. At this point, we count no less than 9 additional defensibilities Uber is pursuing to reinforce their core network effect.

• Brand – There was a period of time where barely a day would go by without some new news story about Uber. They really took the saying “no press is bad press” to heart and have been very effective at staying in the press almost from day one. Their brand recognition is quite strong as a result, though not always with the best connotation.
• Scale – By rapidly scaling to over 700 cities in less than 10 years, Uber is the world’s first global ridesharing network. Although they recently scaled back somewhat in Asia, they’re still active in 63 countries where they can claim 2% or more of the population as users. And with $41.5 billion in gross bookings in 2018, they have a significant scale advantage over new entrants.
• Embedding – Embedding Uber in other apps as a default option is an additional advantage against new ridesharing entrants, heightening those low barriers to entry. Users of Facebook Messenger, Google Maps, Apple Maps, and other popular apps automatically see Uber suggested as a ride service.
• Language nfx – The company name, Uber, means “topmost” or “superior”. Choosing this word was a great move by Uber. As we know from Eugene Wei’s great essay Status as a Service , people are “status-seeking monkeys” and Uber’s name appeals to that impulse. Why would you want something less than superior? Their name let them develop a language network effect, which happens when the dominant brand in a market category becomes shorthand for the entire category, like Kleenex with tissues, or Google with search engines. Phrases like “Can you grab a Kleenex?” “Google it” and “Let’s Uber there” are now in common usage.
• 2-sided marketplace: Uber Freight – Although Uber’s core business isn’t a true 2-sided marketplace, they do have a true 2-sided logistics marketplace in the form of Uber Freight. On the demand side are businesses, and on the supply side are trucks. The more trucks (supply), the more powerful the network effect, and there’s no lower asymptotic limit like with drivers and riders.
• 3-sided marketplace: Uber Eats – Uber built a 3 sided marketplace with Uber Eats, comprised of drivers (supply), restaurants (supply), and users (demand). The defensibility of Uber Eats, like Uber’s core business, does suffer from multi-tenanting on the supply side, where many restaurants also list on other food-delivery apps like DoorDash.
• Data nfx – Across all of their products, Uber gets better with each user because of the data they provide lets them improve demand prediction, matching and dispatching, pricing, and routing. The more people use Uber, the better the product becomes, leading to even more usage.
• Tech Performance nfx – Uber ATG, which has raised billions of dollars to develop autonomous vehicle technology, is a play for a tech performance network effect defensibility. In the digital age, proprietary tech advantages have a short half-life and aren’t very defensible — unless the technology gets better the more the underlying technology is used. Autonomous vehicles, because of the strong ML component, have that property. If Uber can get a tech performance network effect going, it will make their business massively more defensible at a rate of increasing returns and more than justify the investment.
• Personal Utility nfx – Uber Commute, a carpooling service that hasn’t been rolled out to Uber’s global platform yet, is an online peer network of commuters currently in Uber’s India market — a direct network with personal utility to each user. Products with personal utility network effects are highly defensible, so if Uber Commute ever achieves significant scale it could be a game-changer.

5 Lessons for Founders: Inside the Marketplace Podcast

The case study above takes a broad look at Uber’s defensibility moves, but I also want to leave Founders with some lessons they could take away for their own startups. There are 5 lessons I think Founders should take away from Uber:

• Be fast and be aggressive
• Focus on defensibility
• Use capital as a competitive weapon
• Make your vision as big as you can
• Understand that language and naming matters

To hear these lessons fleshed out, listen to the NFX podcast below.

As Founders ourselves, we respect your time. That’s why we built BriefLink , a new software tool that minimizes the upfront time of getting the VC meeting. Simply tell us about your company in 9 easy questions, and you’ll hear from us if it’s a fit.

Try ChatNFX

You are here: Influencer Marketing Hub » Social Media » Valuable Insights from Social Media Platform Case Studies Based on Real-Life Success Stories

Valuable Insights from Social Media Platform Case Studies Based on Real-Life Success Stories

For brands, it can be a struggle to understand how each social media platform works and how to use them for marketing. To grow your brand this year, get inspiration from actual social media platform case studies where we go through real-life applications so you can learn what works and what doesn’t when it comes to social media marketing.

Valuable Insights from Social Media Platform Case Studies Based on Real-Life Success Stories:

Why social media marketing is still relevant, social media platform case studies to gain insights from, key metrics to monitor the effectiveness of your social media campaigns, be guided by learnings from social media marketing case studies, frequently asked questions.

More than 50% of shoppers discover and purchase new products through social media . By 2024, the number of these social buyers is projected to reach over 110 million—and that’s in the US alone. Millennials, along with digital natives Gen Zs and Gen Alphas, are known to purchase directly from social channels. This consumer base is just too massive to ignore. Moreover, the power of social media also goes beyond the internet. After discovering products online, many social media users eventually buy them in physical stores.

Social commerce stats also prove how effective these social media platforms are for marketing. With most consumers spending several hours each day on social media, these platforms are undoubtedly the best places to reach them. Implementing social media marketing strategies to stay visible in these spaces—especially ones frequented by the younger generation—is a prime opportunity for revenue growth.

The following case studies give us a glimpse of how different brands successfully implemented their campaigns. These studies contain a wealth of insights and strategies that you can apply to your own marketing initiatives.

1. Purr-fectly Viral: Whisker’s Litter-Robot LR4 Conquers the Internet

Agency: Ubiquitous

Platforms: YouTube, Instagram, TikTok

Whisker has been providing paw-parents with self-cleaning litter boxes for over two decades. In 2022, they launched their 4 th generation Litter-Robot: the LR4. Wanting to dominate the pet market with their latest innovation, they sought the services of Ubiquitous to amplify the marketing of Litter-Robot LR4.

Strategy: 

Ubiquitous used a combination of in-house influencers and existing relationships with other agencies to expand the influencer pool. The budget was strategically divided among Instagram, TikTok, and YouTube. Instagram and TikTok aimed for broad awareness, while YouTube targeted serious buyers with high-intent content like tech reviews.

The agency used their proprietary technology to find influencer pet owners with engaged audiences and disposable income. Once the key influencers were identified, they were given specific guidelines to create content with some space for creative freedom. During an eight-week period, multiple posts were published on the three platforms, such as unboxing videos or clips of cats using LR4.

The campaign was a success across all platforms. The 130 posts that went live amassed a total of 68.1M views, 7.3M engagements, and a cumulative CPM of $7.27. Litter Robot saw site visits surpassing the 100K mark, and over one hundred transactions translated into an additional six-figure revenue.

Key Takeaway:

The Litter-Robot LR4 campaign underscores the power of strategic influencer marketing and the effectiveness of a multi-platform approach to maximize impact. The careful selection of influencers, diversification of platforms, and leeway given to campaign partners in terms of creative expression all contributed to the success of the campaign.

2. Illuminating Holiday Success: Energizer Makes Portable Lighting “Bunny-Famous”

Agency: Viral Nation

Platforms: YouTube, Facebook, Instagram

Most well-known for its iconic bunny and long-lasting batteries, Energizer sought to extend its brand appeal by positioning itself as the top choice for portable lighting. It was set on making its new products—the Energizer Headlamp and the TAC 700 Flashlight—household must-haves during the holidays and beyond. To do so, they enlisted the help of Viral Nation to launch a multi-platform marketing campaign. 

To boost sales during the holiday season, the campaign was focused on highlighting the new Energizer products as great stocking stuffers. Viral Nation engaged six influencers to publish content on YouTube, Facebook, and Instagram and drive viewers to purchase directly from Energizer’s dedicated Amazon product pages.

Each influencer received both the products and integrated Energizer into their holiday-themed content.  The partners did unboxings, point-of-view reviews, and even hosted appearances by the beloved Energizer Bunny.

Viral Nation’s metrics speak for themselves: 832K interactions, 806K views, and 4.6K clicks. The campaign achieved substantial reach across platforms and successfully directed traffic to Energizer product pages.

The authentic and diverse promotional content led to a success that lit up the holidays, and the influencers effectively positioned Energizer as the go-to brand for portable lighting. The content they created not only drove awareness but also contributed to increased sales during the crucial retail period.

This Energizer campaign shows influencer marketing as a bright approach to holiday marketing. It highlights the effectiveness of tailored social media marketing strategies during key seasons. The strategic collaboration with influencers and campaign implementation on multiple platforms help brands shine brightly in the eyes of consumers.

3. Astronomical Success: Samsung Sees Galactic Growth of its Galaxy App Store 

Agency: Moburst

Platforms: Facebook, X (Twitter), and other channels

To enhance user experience, Samsung launched a dedicated app store in the US called the Galaxy Apps. Noticing slow engagement, they aimed to boost marketing to increase app downloads, user retention, and revenue for the app. They also wanted to raise brand awareness and promote the personalization features of Galaxy devices. To create and implement effective marketing campaigns, they sought Moburst's services.

Moburst implemented a comprehensive strategy of leveraging various media channels to target distinct audience segments. They did micro-segmentation and tailored their campaigns to specific user interests: Gamers were directed to the gaming section, Galaxy advocates were guided to the Made for Samsung section, and design enthusiasts were offered font and theme options for personalized phone customization.

The targeted campaigns and personalized approach led to a significant increase in app downloads that translated into a substantial revenue boost. Galaxy Apps downloads surged by 79%, and revenue increased by 36%. Social media engagement also saw an astronomical rise, with Facebook followers skyrocketing by over 1,400%.

Samsung’s collaboration with Moburst is a good example of how to run micro-segmented mobile marketing strategies on social media platforms. By understanding the diverse preferences of Samsung's vast user base, Moburst was able to craft tailored campaigns that resonated with specific audience segments.

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4. Cobra Kai Chop: Netflix High Kicks Campaign on TikTok

Agency: NeoReach

Platform: TikTok

The Cobra Kai series is one of the most successful shows in Netflix history. Following the immense success of the first two seasons, Netflix pulled out all the stops to make the third season equally successful.

To build hype before the release of Cobra Kai 3, Netflix collaborated with NeoReach in crafting social media marketing campaigns. The agency formed an eclectic roster of influencers from its vast pool and designed a campaign focused on creating buzz. The platform of choice for this campaign was TikTok, which at the time was still a burgeoning social media platform with highly engaged users.

NeoReach tapped its pool of influencers to heighten the anticipation for the upcoming season. The lineup included creators from various backgrounds to ensure a wide-reaching and culturally diverse promotion in major countries worldwide.

The agency used TikTok’s interactive Cobra Kai Chop filter that allowed influencers to creatively incorporate the brand into their content. Using the hashtag #CobraKaiChop also enhanced visibility and awareness.

The campaign achieved remarkable results, with 2.8M+ views from paid content and 4.6B views from #CobraKaiChop. But the campaign’s success was not just in views. It also delivered 24K+ in engagement value and an ROI of 2.76X.

The success of the Cobra Kai Chop campaign gives marketers a glimpse of how to effectively leverage emerging social media platforms. It emphasizes the need for creative content strategies and diverse influencer rosters to resonate with different audience segments.

5. Fresh Strategies: HelloFresh Delights Users with TikTok Culinary Adventures 

Agency: Hype Factory

HelloFresh—the pioneers of fresh food and meal kit delivery services—wanted to boost views and conversions within its niche market. To achieve this, they collaborated with Hype Factory to create a recipe for marketing success.

Hype Factory engaged 24 large entertainment influencers and gave them full creative freedom to make content that highlighted the features of HelloFresh. To enhance visibility and engagement, the campaign used Spark Ads with discount codes and direct links in the bio. Reposting the content as Reels or Stories on Instagram further expanded the reach beyond TikTok.

With a 4.03M reach and 13.3K clicks, the campaign definitely elicited strong engagement. The genuine interaction of the creators with their audience was key to the campaign’s success.

The HelloFresh campaign serves as a reminder to adapt to evolving consumer trends and the constantly changing dynamics of social media platforms. It also stresses the importance of embracing the creative freedom of influencers to pave the way for more authentic brand engagements.

Monitoring metrics is important for measuring the effectiveness of your current campaigns and optimizing subsequent ones. For social media marketing, here are some of the key metrics you need to keep a close eye on:

This refers to the number of social media users who have seen your posts and gives you an idea of the approximate size of your audience. A broader reach often translates to increased visibility. Facebook, Instagram, and TikTok are forecasted to be the leading channels for social commerce , so you might want to focus on amplifying reach on these platforms. 

Impressions

This measures the overall visibility of your content. It refers only to the total number of times your posts have been viewed, regardless of whether they have been clicked or not. This will help you gauge how frequently your audience encounters your posts.

This metric consolidates the number of likes, comments, mentions, shares, and clicks on your posts. High engagement shows that your audience finds your posts interesting or valuable. 

Bounce Rate

The bounce rate is expressed as the percentage of users who navigate away from your social media page after a few seconds. A high bounce rate indicates that your content does not resonate with your audience and signals a need to revamp your strategy and tailor your content to align with your audience’s needs.

Customer Sentiment

This indicates the overall attitude or feelings of your audience towards your brand and is often assessed through comments and sentiment analysis. By understanding customer sentiment, you can tailor your future marketing content and shape your online reputation.

Each case study offers valuable lessons that emphasize the importance of strategic influencer collaboration, multi-platform approaches, and tailored strategies. The proven success of these campaigns brings to light the universal principles that underpin effective social media marketing.

Still, every business is unique. Tweak the strategies you learned from these social media platform case studies to align them with your brand’s distinct goals. Determine the right social media channels for your business and focus your efforts there. The future unfolds a vast canvas for innovative marketing campaigns—paint it with insights gleaned from the compelling case studies we shared.

How can social media platform case studies help me craft marketing strategies for my brand?

Case studies show how marketing strategies are implemented in real life. They showcase proven tactics and actionable insights that help other brands implement similar strategies for their own businesses. 

Why is the selection of social media platforms important for marketing success?

While most people have multiple social media accounts, each platform still has its own nuances and appeal to different consumer segments. Choosing a social media platform is the same as choosing the right channels for broadcasting your message to the right audience. To amplify reach and engagement, focus on platforms where your target audience is most engaged.

How do I apply the key takeaways from social media platform case studies to my brand?

Identify similarities between your brand and the featured case studies, then tailor the successful strategies to align with your unique goals, audience, and industry. Use the case studies as inspiration to innovate and optimize your own social media marketing efforts.

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Treasury sanctions network connected to separatist bosnian leader.

Fatima Hussein

Associated Press

WASHINGTON – A network of people and firms that support the sanctioned president of Bosnia’s Serb-run portion , Milorad Dodik, has been hit with a new round of sanctions.

Treasury’s Office of Foreign Assets Control on Tuesday designated two people and seven companies that provide revenue for Dodik and his family, including his son Igor Dodik.

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Treasury says Dodik used his presidency to direct government contracts to a network of private companies that he and his son oversee.

Dodik was initially sanctioned by the U.S. in 2022 when the Biden administration accused him of “corrupt activities” that threatened to destabilize the region and undermine a U.S.-brokered peace accord from more than 25 years ago. A set of sanctions were imposed on a network of people connected to Dodik last October.

Dodik, who has been calling for the separation of the Serb entity from the rest of Bosnia for over a decade, has had Russia’s support.

There are widespread fears in the U.S. that Russia is trying to destabilize Bosnia and the rest of the region to shift at least some world attention from its war in Ukraine .

Brian Nelson, Treasury's under secretary for terrorism and financial intelligence said, "We will continue to expose the fraudulent schemes that enable Dodik and his family to exploit their own people for their personal benefit.”

Radul Radovanovic contributed to this report.

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