A cuboid is a box-shaped object. It has six flat sides and all angles are right angles. And all of its faces are rectangles. The volume of a cuboid can be expressed in the following formula:
volume = length x depth x height
To enlarge the cuboid, one would need to grow at least one if not all 3 dimensions. For instance, if the length is doubled while the other 2 remain the same, the volume will be doubled. If all 3 dimensions are doubled, the volume will be increased by 8x. On the other hand, the cuboid volume might not even increase if one dimension grows while the other dimensions shrink. For instance, if the length is doubled while the depth and height are both halved, then the volume will be halved. Ask any primary school student and he or she would understand this.
Here is a similar formula that we can use to determine the strength of the network effects of a network:
Network effect coefficient = # of users x attention x connections
The first dimension, “number of users”, is MAU or MUV. The second dimension, “attention”, is typically expressed as “total monthly time spent”. The third dimension, “connections”, is a measure of how much the users interact with each other. For example, it can be a combination of the number of messages, amount of content created, number of follower / following relationships or other network specific activities.
To create strong network effects, you need to make sure your ‘cuboid’ is large in all 3 dimensions, and ideally your cuboid should be very balanced i.e. it should be more like a cube than a rectangle.
For example, a couple of years ago LinkedIn was very strong in the first dimension (“number of users”) and the third dimension (“connections”) but not the second one (“attention”). To solve this problem, LinkedIn encouraged their users and influencers to share content. The total time spent from users has grown significantly since. Linkedin’s weakest link is now getting stronger and stronger.
SlideShare, on the other hand, is an example of a network that is very strong in the first dimension but weak in the other two. If I am searching for a slide deck, I can’t find a better place than SlideShare. But I don’t hang out there and interact with other users. Once I have found what I want, I will be gone.
There are networks that are strong in the first and second dimensions but weak in the third. Netflix and Pandora are both good examples. Both are large networks. Their users are spending many hours every week to consume the content. That said, for the most part their users / content providers don’t interact among each other. As such, their network effects might not be as strong as you might think.
Not suggesting the aforementioned networks are not good businesses. It is just that their barrier to entry and defensibility do not come from network effects, which are commonly mistaken for economies of scale (i.e. business size rather than interactions within the network). For instance, Netflix is a very capital intensive business and this is one of their barriers to entry. Netflix is also creating original, exclusive content to differentiate itself from its competitors.
According this formula, which networks have the strongest network effect? IMO, Facebook, Instagram, YouTube and WhatsApp. Interestingly Facebook owns 3 out of 4 …..
As for Wattpad, on average its users spend more than 4 hours every month. The most engaging ones spend many hours per day. The connections among its users are also strong. However, at 40 million monthly users, Wattpad’s first dimension is not yet as strong as it could be. It will take a few more years before Wattpad reaches its 1-billion-user goal.
Intuitively I always know that strong network effects need more than millions of highly engaged users. But I have never been able to put it together in such a simple formula until now. Network effects can be a very capital efficient way to create barrier to entry. Powerful network effects can make a company extremely difficult to compete with, even for competitors with next-to-infinite resources. With this simple formula you know where your product’s strength and weakness is, and consequently you know where to focus your effort on to improve the weakest link and where to make tradeoffs.
P.S. Networks come in many flavours. They can be networks of data, systems, people or a combination of all of the above. The above formula for network effect coefficient formula is mainly applicable to “people” networks.