In Part 1, I laid out nfx’s 16 network effects. Which is a pretty useable framework. And I agree with about half of them (which is pretty high for me).
In Part 2, I want to lay out how I take apart network effects. And it starts with rejecting the graphic below on the left. This is how network effects (i.e., demand-side economies of scale) are usually visualized.
People get excited about the exponential curve. And NFX’s curve has somewhat of the same character. Its “typical marketplace” has an exponential shape.
And you can see this in the early stages of companies, which is where venture capitalists live.
But think about the tree analogy I used in Part 1. Do trees just keep growing forever? What powerful mechanism goes on for very long? What exponential phenomenon continues very long?
The graphic I use for network effects is this:
Note the carefully chosen words in the headline. For network effects, I always start with this question:
How does the marginal user or activity increase the value and/or utility to current and potential users?
That language is very specific.
We are looking at what happens when there is additional users or activity.
- If there are ten users, what happens when the 11th joins?
- If there are 10M users what happens when the is another million?
And which users are we talking about? Is it users of WhatsApp? Is it more merchants on Shopee? Is it more consumers on Shopee?
Network effects are going to be different for each user group on a platform. And they will change in impact based on scale, time, and other factors.
I argue that one of two things can change. The value of the service can go up. And/or the utility of the service can go up.
- Utility is easier to understand. If more people can be called by your phone line, it has greater value to the user. I can send money to more people.
- Value can be real (I have more merchants I can chose from on Shopee). It can also just be perceived (I just like browsing). And we are talking about value to the user, not the economic value.
You also need to think about current versus potential users. Adding lots of Indonesians on Facebook may not be valuable to Americans already on Facebook with most of their friends. But could be very valuable if Indonesia is earlier in adoption.
Next take a look at the shape of the curve.
I think this is a much better for visualizing network effects. It’s not sexy. There’s no exponential. Network effects as a phenomenon have a starting point and an ending point. Just like with trees. And most of the emphasis in the graphic is on the starting point and the point at which it flatlines (additional users or activity add no more utility or value). The range where the utility and/or value service increases with more users or activity is actually not huge on the graphic – which is what usually happens in practice.
I try to draw the shape of the curve for each user group on a platform. I like to know how additional value happens as a network gets bigger or more active.
- Some companies like Airbnb have a high threshold for viability. You need a lot of merchants before it is a viable service. And then the platform increases in value for consumers pretty linearly as the platform adds more and more accommodations around the world.
- Other companies like Uber have a very low threshold for viability. If you get 50 cars in a neighborhood, it is probably viable. And then the value increase flatlines
- Other companies go exponential at the beginning and then flatline.
- Some go up in a step function (especially on the supplier side).
Basically, it can be different for each company when you are dealing with platform business models (not just direct network effects).
So, you need to take it apart. For each user group on a platform, you need to know:
- The shape of the curve
- The minimum viable scale
- The asymptotic scale
- The scale differential with rivals.
- A detailed breakdown of its competitive strength.
And really, there are other important questions for network effects, which I have listed in detail in my Moats and Marathons books.
There Are (At Least) 4 Important Ideas in Network Effects
Keep in mind that a network effect can have multiple effects. Which can be confusing.
- Network effects can cause rapid increases in the real or perceived value and/or utility to customers. This is usually thought of as consumers. Or business customers. But it can also be for other user groups, like content creators and developers. A service that increases in value TO THE USER is great. And this is usually what we are talking about with network effects. But this doesn’t necessarily mean growth. You can have a great and improving service in a small or flat market.
- Network effects can increase economic value. This is not the same thing as customer value (real or perceived). If a platform business model has attractive unit economics and growth potential, then you can see increasing economic value and shareholder returns with network effects. But not always. You can have a fantastic service with increasing customer but not economic value (by network effects).
- Note nfx says it did a “three-year study, which we released recently, shows that nfx (network effects) are responsible for 70% of the value created by tech companiessince the Internet became a thing in 1994. Even though they are only a minority of companies, companies with nfx (network effects) end up creating the lion’s share of the value.”
- They are talking about economic value increases. That has to do with network effects but also with the growth and unit economics of those particular businesses.
- Network effects can create a competitive advantage. This is demand side economies of scale as a moat. This is what collapses the market to a monopoly or oligopoly. However, this doesn’t necessarily mean the creation of economic value. You can dominate an unattractive business. And it doesn’t necessarily mean growth. You can dominate a stagnant business.
- Network effects can create a barrier to entry. In digital, this is mostly by indirect network effects, which have a chicken and egg problem. That is hard for new entrants to overcome. There is less of a barrier to entry with direct network effects. We also see barriers to entry in physical networks which require lots of tangible assets. Replicating a railroad is almost impossible in a developed country.
All of these can be happening with a network effect.
Ok. That is most of how I view network effects. Let me finish up by talking about two network effects cited by nfx that I think I don’t pay enough attention to.
“Expertise” Network Effects Are Important
Here is how NFX describes an expertise network effect.
“Products that can develop “expertise” network effects are typically tools used by professionals to do their job — the instruments with which they ply their craft. As professionals become more skilled in their jobs, they also level up their expertise in tools required to do their jobs. If the tools are sophisticated enough, the tools require particular expertise of their own.”
“Here are some examples of industries and products where you see strong expertise nfx:
- Accounting Software (QuickBooks)
- CRMs (Salesforce, Hubspot)
- Analytics (Google Analytics, MixPanel)
- Computer Languages (Python, React)
- Spreadsheets (Microsoft Excel)
- Architecture (Revit, Autocad)
- CMS platforms (WordPress)
- Design software (Adobe, Figma, Invision)
- Video editing (Adobe, Final Cut, Avid)
- Mechanical Engineering (SolidWorks, CAD, Avid)”
That is pretty great.
I put this under standardization and interoperability network effects, but I think I underestimated how big this can be. If you standardize a profession and its skills, that plays out in lots of ways.
- Everyone uses the same tools (Adobe, QuickBooks) because that saves costs and lets everyone interoperate. You can share files. You can talk a common language.
- But you also create a standard skill list and career path so employers and contractors can more easily hire who they need. Employers can hire people based on specific skills. Employees can do training in certain skills in a progression.
Expertise as a network effect plays out in the tools, the expertise, in employment and in workflows. It really is pretty sweeping. We can see this in lots of companies that create tools like Microsoft Word and Adobe. But we can also see it in LinkedIn and Upwork for hiring. And within training withing schools and companies.
I’m Not Sure What I Think About “Social Network Effects”
Ok. Last point.
Nfx talks about “social network effects”. These are very low on their list but interesting to think about. I mostly consider these about consumer psychology and not linkages and nodes. But I’m mulling it over.
Here’s what nfx says”
“To date, we’ve identified three main types of social network effects: language, belief, and bandwagon effects. That number could easily expand, since human psychology is complex and there are many kinds of social interactions that work very differently, and we continue to look for new types.”
For Language, I think this can be definitely a network effect. But I don’t agree with their argument that everyone specific words does this. That’s just branding. Here’s their summary.
For Belief, here is what they say:
“The 13th network effect on our current Map is belief. The belief network effect is something you can best see with gold, Bitcoin and religion. It’s a direct nfx.
Homo Sapiens is a pack animal. We want to be in the “in group” and be accepted by others. Sharing common beliefs is a critical part of that. If people believe in something, others are more likely to stick with it and believe in it, too. As a result, there are big social consequences for not believing the things your friends believe, and perhaps worse consequences for ceasing to believe in what they believe. This is one factor that makes people stick with group thoughts, making them very resilient to contradictory information.
Most importantly, beliefs become more valuable to believers the more people believe.
Look at gold. Why is it valuable? You can’t eat it or sleep on it. It’s pretty, but lots of things are pretty. It has some industrial uses, but not that many. It’s valuable because — after we were done believing salt was valuable — people decided to believe gold was valuable instead. And for 5,000+ years, it has always stayed valuable. The past gives us confidence that everyone will continue to hold this belief in the future. That belief strengthens over time.
Ipso facto, gold is valuable because we believe it’s valuable…
…The same is true of Bitcoin. The more people believe it’s valuable, the more valuable it gets for everyone. And we’re seeing that same “sand layering” with Bitcoin now. The more times its price crashes and then bounces back, the more people will believe it has value. And then when you layer some Ethereum “sand” on top of it, and the “sand” of the thousands of other cryptocurrencies in existence — all denominated in Bitcoin on the exchanges — the Bitcoin sand gets progressively more stable as a result of growing Belief nfx. What was once fluid and intangible transforms to something closer to rock.”
I don’t really buy this as a network effect. It’s an important phenomenon. But I think it’s something else. Share of the consumer mind of connected consumers. Trust and community building?
That’s it for this topic. I’ve put in some more nfx stuff on “tribal” network effects below. That’s another related topic that I’m still mulling over.
Here’s some stuff on tribal network effects from nfx.
“Tribal network effects most often develop in alumni networks of schools, military units, fraternities and sororities, accelerators, languages, regions, and religions.
We suspect this was the very first network effect historically, as Homo sapiens evolved as a pack animal, trying to survive. The ones that built the best tribes survived to procreate, so we are all descendants of the best tribe builders. Those who weren’t good at building or joining tribes died off. Thus, our brains are wired to join tribes.
- The tribe is presented as an ingredient of a person’s identity, part of how that person is perceived by others. One might think: “It’s who I am.” This forms a self-concept.
- Network members within the tribe are taught to be intentional about building the value of the tribe by:
a. adding value to other tribe members,
b. defending the tribe’s reputation,
c. receiving value from the tribe members, and
d. growing the tribe.
This intentional value creation and defense of a network is distinct from other types of network effects, where nodes largely contribute value and drive network effects unintentionally.
- In contrast to the in-group of the tribe, there is an out-group that the tribe is actively NOT. A different group, a rival, an enemy, a force to be fought.
- A perception of higher-status attributes of members of the tribe, creating prestige and pride. Evidence or reasoning that members of the tribe are more committed, more “right”, more justified, smarter, stronger, etc.
- Members of the tribe endure shared hardship or adversity, such as training for the marines, studying for tests in college, founding a company, or going through a boot camp of some kind.
- Tribe network members overcome a barrier to get into the tribe. There must be a believable reason for your inclusion, and some demonstration of your worth or “fitness” for inclusion. There is often a period of worrying you won’t “get in.” This creates exclusivity and belonging in the minds of the tribe members, reinforcing the other five attributes.
Not all tribes share all six of these characteristics, but the more they do, the more powerful the tribal self-identification becomes in the minds of the tribe members, and thus the stronger the Tribal network effect.
As with other network effects, network size and network density also matter in the formation and strength of Tribal network effects. The larger the tribe, up to a point, the more valuable it becomes because you are more likely to encounter and form relationships with other nodes. College alumni networks, for example, often have clusters in many different cities and companies where alumni seek each other out. Tribal networks also have a higher density of relationships between nodes because self-identification between tribe members causes them to look for shared affinities and motivates them to altruistic behavior towards other nodes in the network.
That, in turn, leads to a higher proportion of shared connection between tribe members than in other types of networks, which incentivizes further relationship-formation and sets off a virtuous cycle. In a tribal network, people (often unconsciously) recognize that potential connections are more likely to materialize into actual connections, causing a self-fulfilling propensity to try harder to build in-tribe network connections. This creates a denser lattice of links between the nodes, driving network effects, and network value.”
- 3 Types of Network Effects (Asia Tech Strategy – Daily Lesson / Update)
- Questions for Huawei’s CEO, JD & Jingxi, Metcalfe’s Law Is Dumb (Asia Tech Strategy)
From the Concept Library, concepts for this article are:
- Network Effects
From the Company Library, companies for this article are:
- NFX / James Courrier
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