Author: Adam Cuculich

TL;DR

• Locker is building a tool that automates crypto savings and investments, using smart accounts.

• We wanted to gain insight into how frequently technical users use blockchains.

• It turns out that hackathon winners don’t use blockchains very frequently.

Purpose

To gain insight into how frequently technical users use blockchains.

Backstory—Why Though?

At Locker, we originally conceived of a product that could save users money each time they used the blockchain. Think of the company, Acorns—they round up users’ transactions to the nearest dollar and invest that money each time users transact with their debit cards. Could something similar be done with blockchain usage?

Enter Account Abstraction

The answer is yes. With the programmability that account abstraction provides, money can be be saved or invested each time users transact on the blockchain.

Okay, But How Much Money Would Be Saved?

In the case of Acorns, most users transact with their debit cards multiple times a day. Suppose the average amount saved per transaction is 50 cents. At a hypothetical average of 3 transactions per day, that’s $45 saved per month—not bad. Now, what about blockchains; how frequently do people use them? That’s precisely the question we wanted to answer, thereby prompting this investigation.

Methodology

A Representative Sample

We wanted to identify a concentrated group of addresses that are reasonably representative of folks that use blockchains regularly. With this in mind, we chose ETHGlobal hackathon winners, as they represent a technical group of crypto builders who are likely to use blockchains regularly. ETHGlobal is an organization that hosts EVM-based hackathons globally, multiple times a year. All team members at Locker have won multiple hackathons, for which bounties were received. We therefore decided to look at the on-chain transactions that distributed those bounty payments. There, we found the recipient addresses of other hackathon winners. Great—but one hackathon wasn’t enough, so we extended our sample to include winners of 7 hackathons, spanning multiple regions of the world over the last 2 years.

Gathering and Cleaning Data

With hackathon winners identified, we queried the most recent 1,000 blockchain transactions for each address on the Ethereum blockchain. We then filtered the data, removing addresses with no blockchain usage and addresses with outlier usage, clearly representative of trading bots. We then repeated this process for 7 other well-known blockchains.

Results

The results for 8 different blockchains are shown below.

Figure 1 – Distributions of Average Transactions Per Month Across 8 Chains

Figure 2 – Statistics On Average Usage Per Month Per User Per Chain

Conclusion

Hackathon winners don’t use the blockchain very frequently. Given this information, we’ve decided to explore a different go-to-market approach with Locker, focusing on users that frequently get paid in crypto.

Areas For Improvement

• Hackathon winners may not necessarily be representative of the whole space. Therefore, the study can be improved by increasing the quantity and quality of the sample set.

• While the current sample set did exclude all accounts with no usage, participants might primarily use different addresses on other chains than the one receiving bounty payments on Ethereum, potentially skewing the results. Obtaining a sample set of primary addresses for each chain would improve the analysis.

• It would be useful to gain insight into how much value is transferred in the gathered transactions across different chains—do those amounts change with transaction frequency? For example, it’s possible that less frequent users transfer more value on average, compared to those that use blockchains frequently.

• It would be useful to gain insight into what types of transactions are submitted. For example, swaps, peer-to-peer payments, or yield-bearing pool deposits.

References

Here’s the code used for the analysis above.

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