In the blockchain industry there are disagreements about many things. For example, will prevailing blockchain projects be permissioned or permissionless, should we utilize Proof of Work (PoW), Proof of Stake (PoS), or other consensus algorithms, and will we see one blockchain take over or many co-exist. But, one thing we all agree about is that a requirement for the success (or not) of this technology is user adoption.
Currently, very few people utilize blockchain applications. Even what we consider to be more “mainstream” decentralized applications (dApps) only count their user bases in the hundreds. This is not enough for any system that wants to build a credible claim to eventually disrupt current incumbents, whether they are Facebook or Dropbox. Consider the chart below, which shows daily users for all dApps running on Ethereum for the last 30 days:
Importantly, the slight downward trend in the chart barely reaches 10,000 daily active users on any given day. The most used dApp on Ethereum, the exchange IDEX, has a paltry daily user count of about 1,200. Clearly, this is a far cry from any mainstream centralized application running on iOS or Android. And it is even further from platforms such as Facebook, which reported 1.8 billion daily active users in its most recent quarter.
The Risks and Limitations of Blockchain Simulation Modeling
As Managing Partner of Prysm Group, I have talked to hundreds of blockchain projects. And, most, if not all, are rightfully concerned that their project will ultimately join the wreckage ranks with a long list of unadopted platforms. But, while I see the merit in being concerned, I also see many using the wrong tools to solve their problems.
For starters, a blockchain platform needs to be properly structured. For me, this means it works so that potential users can derive value from adopting it, and that they will also want to actively contribute to its community. The approach that I frequently see project founders use to accomplish this is that they guess what those structures “should” look like, and then they construct simulations based on those assumptions to demonstrate that they have guessed correctly.
Through my time as an investment banker at Merrill Lynch, I became quite familiar with simulations. I believe they have both their benefits and their shortcomings. On the plus side, simulations are quite good at giving an idea about the range of outcomes from a particular structure and set of behavioral assumptions. However, we must always bear in mind that a simulation is only as useful as the quality of the inputs it is provided.
So, for example, in the case of blockchain, if the assumptions about user behavior do not accurately reflect what users will in fact do, the simulation will not provide any valuable information. The lesson is simple:
If we don’t know our users and just start assuming their choices, in most cases garbage will go in the simulation, and garbage will come out of it.
How Economics Affects Blockchain User-Adoption
On the contrary, the better we know our users, the better we can structure incentives to:
- Increase users’ willingness to participate in the system.
- Increase users’ willingness to contribute to the system, which in economics we call “value generating behavior.”
These are the two main drivers to increase the economic value generated by a platform, and whether it is tokenized or not, ultimately its underlying financial value.
Simulations can be valuable, but first, we need to have a clear understanding of the inputs of the system to be simulated. When it comes to user behavior, economics can help with this.
What is economics? Economics is the study of choice. We know that people don’t make choices at random. While this lack of randomness is true, still, we can never truly predict people’s choices because they exercise their own free will.
How Should Economics be Applied in Blockchain Platform Design
If we cannot assume people’s choices, and we still want to have an idea about what they might ultimately decide, what can we do? To figure this out, I suggest taking a microeconomic approach.
First, we will need to focus our resources so that we can:
- Identify potential users and user segments.
- Learn what their preferences are: what do they care about?
- Learn what the tradeoffs among those preferences are: what do they care about most?
Then, we need to incentivize them appropriately by structuring the user(s)’s target function, that is the constraint in the microeconomic model that will be built.
Through this process, we gain insight into user behavior that lets us do more than generate assumptions in the dark. Moreover, the output of this process is not a static statement about user behavior; it is the target function (or set of functions) that will provide insights into how users’ behavior will respond to changes in the platform’s design.
After these insights have been developed, various parameters of the platform design can be specified:
- One can optimize the variables in the platform mechanics (i.e., the numbers that will get coded in the software) to lead users towards generating an output by interacting with the system close to what the founding team envisions.
- Define the desired output, what economists specializing in the field of Contract Theory call the First Best of the system. First Best is the output the founding team would want the system to see if they had full control of its users, and thus not have to provide any incentives to them.
Once all of these steps are complete, we have a rigorous, micro-founded model of user behavior and a platform structure that can be checked for robustness using a simulation.
How to Know Blockchain Platform Users and their Preferences
You may be wondering how you can take those first steps to get to know your users and their preferences. The answer is simple; the same way they do in incumbent industries: by collecting market intelligence, doing user surveys, and sitting for interviews with potential users.
This is not a new approach by any means, but it is a proven one; it is well tested by not only private companies, but it also informs the way the federal government provides funding to startups. For example, the National Science Foundation, through its Innovation Corps program, provides $50,000 grants on the condition that the recipient (usually a scientist or an engineer working in a university laboratory that is looking to commercialize their discovery) will spend the entirety of the money to talk to potential customers. Blockchain investors should consider including a similar clause because it is imperative for project founders to talk to potential customers and gain valuable insights about their preferences.
It’s undeniable that this approach is definitely more challenging than simply brainstorming parameters and assumptions. It absolutely requires teams to spend a lot more time talking to people, conducting extensive research, and learning about their users. However, it is also more useful if you are looking to take things to the next level. It is far superior in providing the type of insight needed to have a system that will ultimately have engaged users over time.
Source: Guido Molinari