Hidden Network Effects

Last week I read one of the best business model blog posts I’ve read in a long time, Hidden Networks: Network Effects That Don’t Look Like Network Effects by D’Arcy Coolican at a16z. I’ve long admired the power of network effects to build strong competitive moats and we’ve seen these become the foundation for massive companies such as eBay, Facebook, and Uber. However, network effects come in all shapes and sizes as NFX wrote about a while back.

As described by Coolican, Hidden Networks peel the onion even further and reveal potential future network-related moats in a company’s repertoire. These are special because they are not immediately noticeable – when they become apparent it is often too late for a competitor to spring up and take the market opportunity. They fall into the following buckets:

Slow Networks

Characterized by slow feedback loops and infrequent interactions between nodes in the network. Thus, they are hidden over a long period of time before the network flywheel starts to spin.

Examples

  • Lambda School – Has the opportunity to develop an “Ivy League” brand for aspiring programmers. Once this brand is cemented they will receive much more applications and presumably get their pick of the litter to train and eventually place with their job placement partners.
  • YC – Similarly, the legendary accelerator has grown its brand and prestige to the point that many founders see it as a critical “stamp of approval” and signal in the early days that helps their startups gain momentum.

Unfinished Networks

As the name implies these are situations where there is a missing product feature or wrinkle in the business model that is blocking a network effect from forming.

Examples

  • OpenTable – Started out as a SaaS business but eventually once they had enough restaurants on the platform it transformed itself into the go-to platform for consumers to discover and book reservation at restaurants.
  • Hipcamp – Began by creating a one stop shop tool to find camping grounds across the country. Once they had enough users, this unlocked demand from private camping grounds and private property owners who wanted to take advantage of this pent up demand to monetize their land.
  • Square – Began as a point of sale system for small businesses to complete transactions with credit/debit cards. Since then, Square has expanded into many products (notably, Cash App is forming network effects on the consumer side). On the merchant side, its latest is Square Card, a business debit card that offers a 2.75% rebate on purchases made with other Square merchants. The more there are, the more valuable the rebate becomes.

Throttled Networks

When the founding team intentionally makes strategic decisions to impede the development of network effects. On its surface this sounds counter intuitive, but the logic here is to have a more curated network with the goal of making it more valuable to a particular consumer segment.

Examples

  • Chief – Networking platform for female executives has an application process and long waitlist. Over time, I believe they will increase membership either by opening up to female execs at any company or up-and-coming professionals.
  • The League – One of the first dating apps that intentionally limited its scope to Ivy League grads. Over time, the app has been expanding its criteria to allow for a larger user base but initially it leveraged the throttled network approach to set the foundation of the experience it wanted to provide for users.

Latent Networks

This one involves building a network of engaged users but without a product that monetizes it initially. Eventually a product is launched that invigorates the network even more.

Examples

  • Instagram – Initially, users came to the app to share photos. As the company started to introduce quality filters and other features, it enhanced the user experienced, boosted photo sharing, and spurred the network effect to grow stronger.
  • Snap – In a similar vein, Snap began as an ephemeral messaging app. Over the years, they have introduced a number of interested features, including many addictive video/photo filters which have boosted sharing.

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