The Cold Start Problem: How to Start and Scale Network Effects

by Andrew Chen

What is an attractive product in one context might be less so to another, which is one of the reasons why context collapse can hurt the matchmaking at the core of marketplaces.

So how do you prevent context collapse? Products like iMessage or WhatsApp give us a clue. Messaging apps are resistant to context collapse. You talk to your dozen or so friends and family, and even if the network adds millions more people, it doesn’t change your experience. Slack channels offer a different model—as more and more people within a company join, people set up smaller spaces to interact with their close teammates. This allows people to split the company-wide network into team-wide networks, or even project-based networks. If one of these channels get too big, then people can just reconstitute an even smaller one. In other words, not all networked products experience context collapse as rapidly as others. When users are able to group themselves, they prove particularly resilient.

As networks scale, functionality for people within the product to self-govern becomes both an inevitability and a necessity.

The YouTube team rolled out solution after solution to solve overcrowding, but focused on the simple ones first—displaying a list of recently uploaded videos, followed by a popularity-based sort, and eventually country segmentation. The evolution of YouTube’s solution to overcrowding evolved from manual curation to popularity rankings to algorithmic methods. This is a necessary transition that every networked product has to make to solve the overcrowding problem.

The fancy term for this is preferential attachment, defined as: “the more connected a node is, the more likely it is to receive new links.” Eventually this tamps down growth of the hard side of the network, because new users might start to seek another network where they can be successful. They want a more level playing field, and to get that, they will constantly try new products from competitors—not what a successful network wants.

What few feature updates existed focused on one thing: relevance, search, and algorithmic recommendations. In other words, the core levers to solve the overcrowding issues that might otherwise make YouTube a confusing, fragmented place. This is where Google’s expertise in dealing with massive amounts of data were crucial in developing two key features for YouTube in the ensuing years: Search and Related videos. Both helped users quickly navigate to videos they cared about, and because they were algorithmically driven, didn’t require the company to manually edit or curate the content.

One notable example of this is the ever present “People You May Know” or “Friend suggestions” feature. Every social platform at scale has some kind of implementation of it for a reason: it works incredibly well.

To me, the key learning from the YouTube story is the journey that every networked product has to take. When they started out, they needed very little organization, but as the network grew, more and more structure was applied—first by editors, moderators, and users—and then by data and algorithms. The earliest iterations weren’t sophisticated, just whatever got the job done. Algorithms came later, and even years later, keeping the network healthy is still an everyday battle.

Effective competitive strategy is about who scales and leverages their network effects in the best way possible.

If every product in a category can rely on their network, then it’s not about who’s initially the largest. Instead, the question is, who is doing the best job amplifying and scaling their Acquisition, Engagement, and Economic effects.

cherry picking is an enormously powerful move because it exposes the fundamental asymmetry between the David and Goliath dynamic of networks. A new product can decide where to compete, focus on a single point, and build an atomic network—whereas a larger one finds it tough to defend every inch of its product experience.

The Big Bang Launch is convenient for larger, more established companies as a method to launch new products because they often have distribution channels, huge engineering teams, and sales and marketing support. But counterintuitively, for networked products, this is often a trap. It’s exactly the wrong way to build a network, because a wide launch creates many, many weak networks that aren’t stable on their own.

When networks are built bottom-up, they are more likely to be densely interconnected, and thus healthier and more engaged.

To build a massive successful network effect, I argue that you must start with a smaller, atomic network. And use the success in the first set of networks to tip over the next set of small networks.

This is an easy mistake to make, and possibly the most common pitfall of trying to predict the features of startups that are trying to build new products with network effects. A product’s first network is unlikely to be its last, when the team is working furiously to refine its network forces to conquer adjacent markets and networks. What might look like an airbed company eventually comes to disrupt the entire hotel industry. A chat product for small teams and startups eventually takes over the entire market as the de facto way for teams to communicate.

Focusing on the hard sides of the network, which are usually smaller in number, provides leverage in competitive moves. For a social network or video platform, it might make sense to pursue this side by giving content creators special economic incentives, or distribution for their content. For B2B products, it might be special features and pricing for enterprises. The core goal is the same regardless of the category—move the best and most important nodes from one network to another, and it will be a competitive win.

Bigger networks are fearsome not just because of the inherent network effects that come with scale, but also because of their ability to expand into new categories and markets.

The distribution advantages don’t win when the product is inferior.

The move is to leverage a larger network across multiple touchpoints, to accelerate all of its network effects—not just acquisition.

The goal is to compete not just on features or product, but to always be the “big guy” in a competitive situation—to bring your bigger network as a competitive weapon, which in turn unlocks benefits for acquisition, engagement, and monetization.