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”.
But recently, reality seems to be diverging from theory.
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.
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:
Ridesharing. Within any geography, driver supply and passenger demand reinforce each other, so more drivers mean lower wait times, which means more passenger demand, which means more drivers want to drive… the flywheel spins! But there’s a hitch to that value proposition: Once you hit the 5-minute wait time in a particular geography, riders are indifferent to whether there are more drivers available in the network. And once multiple platforms hit that level of relative liquidity (e.g., enough drivers to satisfy the riders 5-minute wait time), the specific platform matters less to riders. In those markets, they’ll need to compete on other vectors, like brand/reputation, price, user experience, loyalty programs, and more. In other words, the value proposition needs to evolve.
Social lending. Sometimes changes in network effects are driven not by relative liquidity (like wait times), but by absolute liquidity (like the number of people in a network). Take the case study of Frank, which let people borrow money from and lend money to friends and family [full disclosure: one of us co-founded Frank]. Early on, more friends in Frank groups meant more demand and more liquidity, which created a bigger incentive for people to join those groups. But once a group had more than 7 people, they became less likely to lend or borrow: turns out people only have ~7 friends/family members they have that level of trust with! The network effects in this case went from positive to negative as an individual’s network outgrew the value proposition. This pattern has also held true for a number of other highly social products.
Social networks. As Facebook’s use case went from sharing status updates with friends to surfacing news and content, the network effects weakened. Too many friends/followers meant people didn’t feel as comfortable sharing personal content, and the experience shifted more to news and public content. The value proposition and network effects shifted accordingly — from social networking to social media. Importantly, adding additional nodes in the context of media discovery was less valuable than it was in the earlier days of a pure social network. So the shift in value proposition meant the network effect curve hit an inflection point. And while some features (e.g. curation algorithms, timelines) helped defer this tipping point by letting users add more friends while still letting the content feel relevant, eventually the content mix, the value proposition, and the network effects all shifted.
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.
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.
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.
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.
The nature of your market as well as competitors and substitutes is also critically important to understanding and forecasting network effects.
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).
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.
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/.
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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!
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.