Why tech companies are valued so highly? The MGAFA — Microsoft, Google, Apple, Facebook and Amazon — have attained all time high valuations. Hilton’s valuation is ~ $24B, while Airbnb is valued at ~ $31B without owning a single property. Similarly, Uber’s valuation (~ $70B) is significantly higher than General Motors’ valuation (~ $53B).
You might wonder what the secret sauce of these tech companies’ rapid growth and high valuation is. The simple answer to that is Network Effect. A network effect is when another user makes the service more valuable for every other user. In traditional market, more customers would drive more nits produced. That would spread fixed cost across units and lower average cost per unit. While, in digital market, more units consumed would create more value to the network and would increase value per unit. That’s why network effect is also known as the demand-side economies of scale.
Here is the best definition I found out from an article published by a NYU researcher.
A product displays network effects when more usage of the product by any user increases the product’s value for other users (and sometimes all users).
Once a company achieves a network effect, users won’t find much value in competitors’ smaller networks, which makes any business hard to catch and an existing player would achieve economic moat.
Similar to positive network effect, there is also a negative network effect. A negative network effect occurs when welfare decreases with the addition of more users. i.e. network congestion, lock-in or switching cost and conflicts of interest. I’ll mainly discuss positive network effects in this article.
Types of Network Effects
There are different types of network effects and their behaviors are different.
As James Currier mentioned:
“Not all nfx are created equal — some are stronger and tend to produce more value than others.”
There are various ways to classify network effects. I found that classification of network effects in this article is very comprehensive.
At a high-level, there are five major types of network effects.
1. Direct Network Effect
The strongest and simplest network effects are direct: increased usage of a product leads to a direct increase in the value of that product to its users.
LinkedIn, WhatsApp and Facebook demonstrate this type of network effect. Once you have friends on Facebook or WhatsApp, you wouldn’t join other services. A new entrant has to achieve significant network effect in order to create values for users.
2. Two-sided Network Effect
The real distinguishing characteristic of a 2-sided network is that there are two different classes of users: supply-side and demand-side users. They each come to the network for different reasons, and they produce complementary value for the other side.
Marketplace and platform are two-sided network effect. This type of network effect is seen in Airbnb and Uber. When more drivers join Uber, it creates value for riders by reducing wait time for riders. Thus, more riders join Uber and rider demand creates value for drivers.
3. Data Network Effect
When a product’s value increases with more data, and when additional usage of that product yields data, then you have a Data Network Effect. If data is really central to the way the product benefits users, then the data network effects of that product has the potential to be very powerful.
Examples for this type of network effects are Waze and Google Maps. Waze users consumes data as well as contributes useful data to the network. Users’ data contribute to determine traffic and Waze provides optimal route to the users. So, if network is larger, Waze can provide more accurate data on traffic to the users.
4. Tech Performance Network Effect
When the technical performance of a product directly improves with increased numbers of users, it has Tech Performance nfx. For networks with Tech Performance nfx, the more devices or users on a network, the better the underlying technology works. This makes the product faster, cheaper or easier.
Every person downloading a file from BitTorrent is also seeding files to the network. So, the services get faster for all users as more nodes are on the network.
5. Social Network Effect
Social nfx work through psychology and the interactions between people. There is an unseen network among people, where our physical bodies are the nodes, and our words and behaviors with each other are the connections.
Social network effect can add value to people in three different ways:
- Language — brand name that people will verbalize (Uber, Google).
- Belief — the more people believe in value of something, the more valuable it gets in reality (Bitcoin).
- Bandwagon — when social pressure to join a network causes people to feel they don’t want to be left out (Slack, Apple).
Why Network Effects are Important
As mentioned in this article, in digital world, there are four ways to create defensibility: Supply-Side Economies of Scale, Brand, Embedding and Network effects. Network effects is the most important way to achieve defensibility. Companies with the strongest types of network effects built into their business model can achieve scalable growth and win big.
Network effects is one of the most important strategies to achieve organic growth. Companies can grow from paid acquisition, but customer acquisition cost (CAC) would be too high and as markets saturate, CAC would rise and make it difficult to achieve profitability in long run. However, this is not true with network effects. As networks grow, all participants benefit and create value for other users which attract more users to use product. Thus, CAC remains constant or decreases over time and helps business grow organically.
For example, Dropbox employed paid campaigns to acquire new customers during its initial days. Due to this, customer acquisition cost (CAC) was very high ~$300, compared to yearly revenue ~ $120 (~ $10/month). Clearly, it wasn’t sustainable for Dropbox to achieve organic growth with this model. Dropbox employed growth hacking tactic such as double referral program to boost user base. Once users started using Dropbox and shared a folder with other people to collaborate, it created a link that other people could use. This created an eco-system where new users started using the service because their friends shared a file with them. Furthermore, it led to a barrier to switch to a different file-sharing system and helped Dropbox to acquire new customers. Thus, an existing users hooked new users to use Dropbox. In other words, Dropbox achieved network effect which helped them to achieve growth and reduce CAC.
How Network Effect Works
Network effect becomes significant after only when certain number of users are using the product. This certain number is called critical mass. The challenge lies in getting users to join in before the critical mass is achieved. In order to achieve critical mass, companies could offer services for free for a limited time and adopt various growth hacking tactics, such as referral bonus (as mentioned earlier in Dropbox’s case), request a friend to sign up or subsidizing fees for service.
Once this critical mass has been achieved, the value obtained from the product or service is greater than or equal to the cost for the product or service. As the value increases by the number of users of a product, more users would want to subscribe/purchase the product, and hence more users would be added to that product, which would further increase value of product and make more people to join it.
The above graph, created by Ray Stern depicts concept of network effects. It shows that once product achieves network effects, value Increases exponentially while cost increases linearly. The cost of maintaining the network does not grow as fast as the value of the network. The value increases as the size of the network increases. In the long run, it’ll be difficult for competitors to enter in the market as existing players would have more valuable network. So, there will tend to be fewer players, and they will continue to grow larger.
For example, one of the most valuable aspects of the Spotify platform is music discovery. Listeners want to explore different genres of music and listen music beyond top playlists, while artists/record labels want their music to be reached to more customers.
As more record labels/artists join Spotify, more users tend to join Spotify due to more music choices available on the platform, which in turn attracts more artists/record labels and collectively increases value of Spotify’s network. Also, Spotify users can see their friends’ playlist and recent activity. So, once you have more friends on Spotify, it creates stickiness of the product. The platform therefore ultimately becomes more valuable to users as more users join. Spotify curates and creates its own playlists, but much of the value comes from other users’ tastes. As more music listeners, bloggers, organizations get on Spotify, the more valuable it becomes to a listener as they have more curated music. Simultaneously, the more users on Spotify, the better it is for artists (increased streaming royalties and exposure).
To sum this up — the more playlists, the more users, the better the music discovery. Once Spotify has more users and artist on its network, in other words network effect is achieved, it’s really difficult for users and playlist to switch to other service.
Considering network effects as a foundation, I look forward to exploring different types of network effects and various business models enabled by them.
Thank you for taking the time to read this. Please let me know in the comments or on twitter (@anujshah1992) about your thoughts!
