Network Effects 101
As mentioned last week, Network Effects deserve a deep dive of their own. There’s a lot of misunderstanding out there with regards to what network effects are and are not. Which is particularly disconcerting given the critical role that they play in modern business models.
Fortunately, there also some good resources up there. The write-up below is a mash-up of three of the best resources that are out there. It contains very little to original content, other than attempting to restructure information in more coherent fashion. In my opinion at least:
- Evergreen Business Weekly — the Power of Network Effects
- A16Z’s deck on Network Effects
- Reverse Network Effects
A product displays positive network effects when more usage of the product by any user increases the product’s value for other users (and sometimes all users).
Types of network effects
- Direct — increases in usage lead to direct increases in value
- Indirect — increased in usage of the product spawns the production of increasingly valuable complementary goods, and this results in an increase in the value of the original product
Example: “standards” — Windows, VHS, USB Type C
- Two-sided — increases in usage by one set of users increases the value of a complementary product to another distinct set of users, and vice versa
Example: “marketplaces” — more Uber riders → attracts more Uber drivers → service for other riders improves (note that more riders in and of itself does not improve the service)
Where does value come from?
There are four sources of value created on networks: connection, content, clout, and data.
- Connection: Networks allow users to discover and/or connect with other users. As more users join the network, there is greater value for every individual user. Skype and WhatsApp become more useful as a user’s connections increase. Match.com and LinkedIn become more useful as more users come on board.
- Content: Users discover and consume content created by other users on the network. As more users come on board, the corpus of content scales, leading to greater value for the user base. Content platforms like YouTube, Flickr, and Quora, as well as marketplaces like Airbnb and Etsy becomes more useful as the number of creators and the volume of content increase.
- Clout: Some networks have power users, who enjoy influence and clout on the network. Follower counts (Twitter), leaderboards (Foursquare) and reputation platforms (Yahoo Answers) are used to separate power users from the rest. On networks like Twitter, the larger the network, the larger is the following that a power user can develop
- Data: Data network effects occur when the product, generally powered by machine learning, becomes smarter as it get more data from the users. The smarter the product is, the better it serves the users and the more likely they are to keep using it and contribute more data
What network effects are not
- Supply-side economies of scale — see more in the “application” section, but in general: being able to deliver a service more efficiently / cheaper when you have more users/customers is NOT a network effect.
- Virality — A viral product is one whose rate of adoption increases with each additional user. The more people join, the faster it grows. There are products that exhibit virality without network effects and products that exhibit network effects without virality. The two attributes are decoupled. For example, in a two-sided marketplace, you can drive virality in each side of the market using promotions without any connection to a network effect.
Network Effects as a business model
Using network effects as a business model means creating a dynamic in which with more usage value increases super-linearly while cost increases only linearly. This is particularly compelling approach in businesses with a substantial “brick and mortar” footprint, where the opportunities for significant cost reduction is limited.
Conversely, when economies of scale are used as a business mode, the intent is to create a dynamic in which with more usage, value keeps increasing linearly, while costs only increase sub-linearly. This is a particularly compelling approach in businesses with a substantial “digital” footprint, where opportunities for significant cost reduction exist.
It’s worth calling out that the two approaches are complementary rather than contradictory, and have a more profound impact when applied in unison.
Network Effects as a competitive advantage
Network effects, and in particular strong, direct network effects can act as a powerful competitive advantage. Since the value to the individual user is driven more by the participation of the other users in the network, rather than by the direct service provided by the company, the switching cost for the individual user remain relatively high, even when a competing company offers a better service.
Furthermore, creating a “better” network effect by a competitor is not an easy challenge, especially given the critical mass threshold (see “requirements” section below).
Network effects need to be big enough to matter
A network effect typically requires a critical mass of usage in order to be meaningful enough to matter in the user’s “value equation”. Critical mass can be reached using different strategies:
- Come for the tool, stay for the network — attract users first with a “single-player tool” value proposition — assume the network effect doesn’t exist and generate enough value without it.
Example: instagram (photo filters), Warcraft (pre-WoW)
- Dominate extremely tiny markets — fully lever the “locality”/”density” of the network, to reach a “local critical mass” sooner:
Example: Facebook (one college campus at a time), Uber (one city at a time), Twitter (focus on celebrities and VIPs)
“Locality”/”Density” — leveraging irregular network topologies
The microstructure of an underlying network of connections often influences how much network effects matter. Each user is influenced directly by the decisions of only a small subset of other users — those they are “connected” to via an underlying social or business network.
Examples: AirBnB/Uber — geo, LinkedIn — professional affiliation, Facebook — social relationships
When Network Effects backfire
One would expect that the bigger the network, the more value users derive from it.
However, as networks scale, the value for users may drop for several reasons:
- Connection: New users joining the online community may lower the quality of interactions and increase noise/spam through unsolicited connection requests.
- Content: The network may fail to manage the abundance of content created, on it and may fail to scale the curation of content created and the personalization of the content served to users.
- Clout: The network may get inadvertently biased towards early users and promote them over users who join later.
Just as network effects create a rich-becomes-richer cycle leading to rapid growth of the network, reverse network effects can work in the opposite direction, leading to users quitting the network in droves.
Examples: Friendster, Myspace, and Orkut
- What type of network effect is your product trying to generate? (direct/indirect/two-sided)
- What user base are you trying to generate a network effect for? (customer segment/side of market/3rd party users/3rd party developers)
- What is the source of value? (connections, content, clout, data)
- What are the critical drivers of irregularities in your network topology?
- What is your strategy for reaching critical mass?
- Extra-credit: how will you know if our network effect backfires?