Reaching the point of Critical Mass for a Network Effects driven startup is a do or die task. It defines the near-monopolies developed by AirBNB, Uber, Facebook, and countless other software startups.
Think of Critical Mass a turning point in how a growing Network Effects platform functions. it’s a shift in gear from standard, linear growth to non-linear, exponential growth. What used to get you 5 steps forward now gets you 25 steps.
Critical Mass is a core concept for building a competitive software startup, and it is best explored by direct comparison against an older model.
Below is a thought experiment following two startups developing in parallel. One, fueled by Network Effects fighting to reach Critical Mass. The other, a standard company in the same industry functioning with Network Effects.
Thought Experiment Section Title
Let’s imagine two startups competing over the same market. For the sake of this example, let’s keep the scope limited to some service-based product. Each startup’s goal is to take dominant market share.
Startup A understands how to build products that take advantage of Network Effects. Startup A is providing a platform for two groups within the market to interact and provide services for each other.
This could be AirBNB pairing Hosts and Guests or Uber pairing Drivers and Riders, for example. Startup A grows by expanding the features of their platform and through promotion. They only need to invest in resources for promoting and feature development.
Startup B is pursuing a more traditional model, in the industry, the above startups are disrupting. Startup B is providing services to users that the company makes itself. These are hotels vs AirBNB or taxi companies vs Uber, etc.. For Startup B to expand they must expand their ability to directly perform their service. Startup B’s resources are physical. This means their growth is limited by how thin their resources can be stretched to meet market demand. To increase their ability to meet demand, they must invest in more physical resources.
Phase One: Early Adoption
In the very beginning, both startups need to focus only on getting eyeballs on their products. They’re selling their idea to early adopters, people willing to take a chance on a new service. In this context, we’ll refer to amount of new users that come to the platform per month as “growth”.
Each box in our graphic below will represent one month at the standard growth rate for each startups.
Both startups grow at roughly the same rate at this early stage. Each picking up early adopters in their target markets. They grow at “Order n” or O(n). This is another way of saying you’ll grow at your standard growth rate. When “N” is being multiplied by another number, it’s a way of saying you’re growing at those many multiples of your normal growth rate.
If both startups are growing at the same rate, what separates them?
At this moment, the main difference between these two companies is how new resources they each bring to the table affect growth.
Since Startup A is building a software platform, they live in a world of 1’s and 0’s. They can have users in every corner of the world bringing their platform to local markets; the only cost to them is an increase in server cost. For AirBNB to expand from San Francisco to Paris, they only to need to expand their marketing efforts. Startup A’s scaling is attention intensive.
Startup B doesn’t live in the world of 1’s and 0’s, they live in the physical world. To expand into a new city they must hire new employees in that city and invest in the resources necessary for them to perform. For a hotel to expand from San Francisco to Paris, they must buy or build a hotel and hire new employees, then market. Startup B’s scaling is resource intensive.
Let’s fast forward a bit to get a better view of how these different growth mechanisms work out in competition with each other.
Phase Two: Software Leverage
Each startup now has a solid grip on their early adopters. Startup B is starting to feel overwhelmed by their market demand and they feel it’s time to expand. Startup A has a solid following of users interested in their new type of product; they’re ready to expand their development and marketing teams. Each company has the same amount of capital to expand.
Startup A adds new members to their marketing and development teams and set their focus on deploying new features and marketing in 4 new cities. Startup B invests in building up resources in 1 new city, investing in land, infrastructure, and new employees.
Both startups are now expanding in more than one market. For simplicity’s sake, we’ll say that the startups keep their same growth rates in each new city. For Startup B, this means they’re now growing at O(2n), or twice of their starting rate. Since Startup B’s product delivery is physical and cannot be easily updated, they’re bounded at this growth rate.
Startup A sees a boom in growth. On marketing efforts alone, they’re now growing at O(4n). But marketing isn’t their only tool for expansion. Startup A can also leverage their software foundation and expand their product’s features to reach new sets of users that were previously uninterested. We’ll say this adds the effect of another city being on the platform, boosting Startup A to O(5n).
Let’s see what this looks like.
Startup A, for the same amount of capital, is now outpacing Startup B’s growth 5 to 1! It’s important to note, we’re still growing linearly. Keep in mind that we still haven’t reached Startup A’s point of Critical Mass.
This doesn’t get better for Startup B. More users bring more revenue, and more revenue means more expansion. As we learned above, Startup A gets much more bang for their buck when expanding. And at these higher growth rates, Startup A starts to see a new phenomenon arise: the Network Effect.
For those who haven’t read our article on Network Effects, a Network Effect occurs when a shared resource becomes more valuable to its users as more people use it. Since Startup A is building their platform to make use of Network Effects, it’s users become more attached to the platform as more of their peers join it. Network Effects ramp up over time, building strong bonds between users, giving them a reason to promote the use of the platform to others.
Phase Three: Critical Mass
Soon, Startup A reaches a turning point, our point of Critical Mass. Startup A’s user base begins to support the promotion on a new level. The brand begins to be known beyond the current user base, becoming synonymous with the service offered. The current users value the product of Startup A so highly that its wide usage alone attracts new users. This growth is in addition to the growth and marketing tactics already being performed.
Startup A’s growth now becomes exponential, O(n²).
After crossing the point of Critical Mass, Startup A’s growth is no longer just a multiple of Startup B’s. Startup A is now seeing non-linear, exponential growth, O(n²). Non-linear advantages improve more and more over time. This is even more valuable given Startup A’s previous growth advantage in Phase Two.
This non-linear growth is what fuels the disruption of entire industries. Outside of this thought experiment, it’s not a single startup fighting Startup A’s exponential growth, it’s an entire incumbent industry. No single business can grow fast enough to compete against a Network Effects competitor at Critical Mass offering a competing product. They can survive and adapt, but their market prominence isn’t likely to return.
It’s now clear that Startup B will never be able to catch up with Startup A, but what does Startup B have going for it that Startup A doesn’t?
Startup B’s weakness — it’s resource intensiveness — is a great strength in competition to a software startup if that software startup cannot reach Critical Mass.
Up until Startup A’s crossing of the point of Critical Mass, the entire effort of their company is dedicated to promoting Network Effects on their platform. In our example above, they were successful. What if they weren’t?
In our example, Startup A managed to focus on features their users found valuable, and they targeted new user bases correctly, but this isn’t a predestined outcome. Plenty of startups try to bring a Network Effects model to an existing industry and fail to generate any interest from users. This could be because the users aren’t flexible to new ideas, or because a startup didn’t approach the problem from the right angle.
In these cases, Startup B survives exactly because they’re being conventional. If Uber had never managed to reach Critical Mass, then a competing taxi company of the same age would still be a valuable business, just one with slower growth dynamics.
Critical Mass is a crucial concept to keep in mind when sizing up systems built on Network Effects. The two concepts are tightly linked. If a Network Effects driven startup can manage to survive long enough to find their way to Critical Mass, they will be well on their way to developing a very powerful defense against competitors. This advantage comes at a high cost because if the maneuver cannot be completed, it will cripple the startup. Silicon Valley is home to hundreds of former startups that never gained this traction.
If you’d like to read more about Network Effects, read our Mental Model article and study AirBNB’s platform.
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