Although software has become easier to build, launching and scaling new products and services remains difficult. Startups face daunting challenges entering the technology ecosystem, including stiff competition, copycats, and ineffective marketing channels.
Teams launching new products must consider the advantages of “the network effect,” where a product or service’s value increases as more users engage with it.
The Cold Start Problem reveals what makes winning networks successful, why some startups fail to successfully scale, and most crucially, why products that create and compete using the network effect are vitally important today.
Cold Start Theory lays out a series of stages that every product team must traverse to fully harness the power of network effects. The curve represents the value of the network as it builds over time, and is shaped as an S-curve with a droop at the end.
There are five primary stages:
- The Cold Start Problem
- Tipping Point
- Escape Velocity
- Hitting the Ceiling
- The Moat
The Cold Start Problem
Most new networks fail. If a new video-sharing app launches and doesn’t have a wide selection of content early on, users won’t stick around.
To solve this, build an “atomic network”—that is, the smallest possible network that is stable and can grow on its own
The Tipping Point
To win a market, it’s important to build many, many more networks to expand into the market—but how does this happen, at scale?
Luckily, an important dynamic kicks in: as a network grows, each new network starts to tip faster and faster, so that the entire market is more easily captured. This is the second phase of the framework, the Tipping Point.
The Escape Velocity stage is all about working furiously to strengthen network effects and to sustain growth.
Andrew Chen: I redefine it so that it’s not one singular effect, but rather, three distinct, underlying forces:
The Acquisition Effect, which lets products tap into the network to drive low-cost, highly efficient user acquisition via viral growth;
The Engagement Effect, which increases interaction between users as networks fill in; and finally,
The Economic Effect, which improves monetization levels and conversion rates as the network grows.
This trio of effects creates a powerful flywheel that can be optimized to scale your product to potentially billions of users.
Hitting the Ceiling
In the real world, products tend to grow rapidly, then hit a ceiling, then as the team addresses the problems, another growth spurt emerges.
Then follows another ceiling. Then another cycle after that, each one often successively getting more complex to address over time as the problems become more fundamental.
The final stage of the framework focuses on using network effects to fend off competitors, which is often the focus as the network and product matures. While it is not the only moat—brand, technology, partnerships, and others can help—it is one of the most important ones in the technology sector.
This dynamic drives a unique form of rivalry—“Network-based competition”—that isn’t just about better features or execution, but about how one product’s ecosystem might challenge another’s. Airbnb faced this problem in Europe when a strong, local competitor called Wimdu emerged with a boatload in funding, hundreds of employees, and on paper, more traction in its home market. Airbnb had to fight off its European competitor by competing on the quality of the network, and scaling its network effects—not via traditional competitive vectors like pricing or features.
The order of operations for most consumer-facing marketplace is “Supply, Demand, supply, supply, supply” – Andrew Chen
The ideal product to drive network effects combines both factors: The product idea itself should be as simple as possible—easily understandable by anyone as soon as they encounter it.
And at the same time, it should simultaneously bring together a rich, complex, infinite network of users that is impossible to copy by competitors.