Flow vs Other L1s Pt (II)

    Challenges

    How does Flow compare to other L1s in terms of user retention? Is a user who made a transaction previously likely to make another transaction a week or a month later? Compare and contrast this type of activity vs other L1s like Solana and Ethereum.

    Introduction

    A layer-1 network is another name for a base blockchain. Ethereum (ETH), and Flow are examples of layer-1 protocols. We refer to them as layer-1 because these are the main networks within their ecosystem. In contrast to layer-1, we have off-chains and other layer-2 solutions that are built on top of the main chains. In other words, a protocol is layer 1 when it processes and finalizes transactions on its own blockchain. They also have their own native token, used to pay for transaction fees. Now that we know what layer 1 is, let's look at some examples:

    • Flow is a blockchain that is designed for extensive scaling without the use of sharding techniques, providing fast and low-cost transactions that make sense for dapps such as NFT marketplaces and crypto-infused video games. As mentioned, Flow hails from Dapper Labs, which decided to solve its blockchain congestion problem head-on by building one primed for games and other interactive experiences. Dapper is now using Flow for all of its own projects, including NBA Top Shot, but it’s open to other developers as well.

    • Ethereum was the first smart contract platform. Being the first layer 1, it has established itself as the leader of the pack and has the largest market cap. One of the key things to note about Ethereum is the Ethereum Virtual Machine (EVM). The EVM is the decentralized computer that developers interact with when writing smart contracts on Ethereum. A number of other layer 1 blockchains are EVM compatible, including Binance Smart Chain and Avalanche. That means they use the same code as Ethereum and developers can easily deploy their projects across multiple blockchains and strengthen the ecosystem of EVM developers (they all use the programming language Solidity). Ethereum is the most decentralized layer 1 blockchain.

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    • The Near protocol is a layer-1 blockchain aiming to be the base of the emerging web stack. With a sharded proof-of-stake design, it’s highly-scalable and low-cost solution is set to compete with Ethereum and Polkadot.

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    • Algorand is focused on the future of finance, looking to grow the decentralized finance economy by replacing traditional financial models. Already, they’ve seen growth across a number of industries and have raised hundreds of millions of dollars to grow the ecosystem.

    • Harmony is an Effective Proof of Stake (EPoS), layer-1 network with sharding support. Harmony currently uses a "Cross-Chain Finance" strategy to attract developers and users. Trustless bridges to Ethereum (ETH) and Bitcoin play a key role, allowing users to exchange their tokens without the usual custodial risks seen with bridges. Harmony’s main vision for scaling Web3 relies on Decentralized Autonomous Organizations (DAOs) and zero-knowledge proofs.

    Methods

    • All the data for analysis is extracted from transaction tables of different blockchains from Flipside Crypto. Since “flow.core.fact_transactions“ table only includes transactions conducted since 2022-04-20, therefore transactions from other tables were filtered to fit in the same time interval.
    • Avalanche table has been added recently and only includes transactions since 2022-05-30, therefore Avalanche was excluded from the analysis. Additionally, BSC table has been added recently too and lacks a large amount of data, therefore BSC was excluded from the analysis too.
    • I wanted to include Solana in the analysis, but due to the amount of data in the Solana tables, I kept getting this error: “OperationFailedError: Statement reached its statement or warehouse timeout of 900 second(s) and was canceled.” First I tried to break my code and optimize it, but finally, in order to keep the results and charts integrated, I excluded Solana.
    • In this analysis, retained user is defined as a wallet address that conducted more than one transaction on a blockchain, with a timestamp difference greater than 1 hour between at least two consecutive transactions. Most of the transactions that a particular address conducts within an hour can be considered as one action (e.g., approving a token before swapping it), besides there are several addresses carrying out transactions continuously with a short timestamp difference between them (e.g., XFYAYSEGQIY2J3DCGGXCPXY5FGHSVKM3V4WCNYCLKDLHB7RYDBU233QB5M, an oracle funded by Algorand inc), Therefore, this filtering is used to avoid distorted results and wrong conclusions, and two consecutive transactions within an hour from a particular are considered as a single transaction.
    • Transactions made by the retained users were analyzed to calculate the time interval between two consecutive transactions made by a particular user, and transactions were categorized into six groups based on this parameter. Transactions with a timestamp difference greater than 4 weeks from their previous transaction are grouped as “After a month.”

    The first chart below presents the number of all transactions, the number of transactions made by retained users, and the number of transactions made by unretained users on the analyzed blockchain. The second chart shows the percentage of transactions made by retained users.

    Sources

    Contact Data

    The first chart below presents the number of all users, the number of retained users, and the number of unretained users. The second chart shows the percentage of retained users.

    Data Analysis

    The following charts, present the time interval between two consecutive transactions made by a particular retained user.

    As it can be seen:

    • Most of the transactions made by a retained user are conducted on the same day as the previous transaction made by that user, regardless of the blockchain.
    • Interestingly, the Maximum percentage of “within a week” group is on Flow blockchain, this group includes transactions that have been carried out within the same week as their previous transaction from a particular user.

    There is a 3.12% (5.8256% * 53.6%) probability for a user who made a transaction previously on Flow, to make the next transaction within the same day excluding the same hour.

    There is a 2.14% probability for a user who made a transaction previously on Flow, to make the next transaction within the same week excluding the same day.

    There is a 0.28% probability for a user who made a transaction previously on Flow, to make the next transaction after a week.

    There is a 0.22% probability for a user who made a transaction previously on Flow, to make the next transaction after two or three weeks.

    There is a 0.05% probability for a user who made a transaction previously on Flow, to make the next transaction after a month.

    Results for other blockchains can be seen in the table below.

    Conclusion

    Comparing data presented in the charts and table in this analysis provides a broad perspective on the number of retained users and transactions made by these users among analyzed blockchains. Some of the findings are listed below:

    • Ethereum has a large number of retained users who are responsible for a significant amount of transactions on it. There is a 20.63% probability for a user who made a transaction previously on Ethereum, to make the next transaction within the same day.
    • Algorand has almost 540K retained users (19.68% of all users), but they only make 2.8% of all transactions on Algorand (lowest among all analyzed blockchains); This means that there are a considerable amount of addresses on Algorand which are continuously making transactions with time intervals shorter than 1 hour between these transactions.
    • Harmony has almost 142k retained users (only 2.8% of all users), and these users make 18.8% of all transactions on Harmony; 93.7% of these transactions are carried out on the same day as their previous transaction. There is a 17.7% probability for a user who made a transaction previously on Harmony, to make the next transaction within the same day.
    • Flow has almost 1.34M retained users (28.73% of all users), but they only make 5.82% of all transactions on Flow. Flow has an average position based on retained users and their transactions among other analyzed L1 Blockchains.
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    Findings based on the four charts above :

    • The average number of transactions made by a retained user on Algorand is around 5.3. There are a considerable amount of addresses on Algorand which are continuously making transactions with time intervals shorter than 1 hour between these transactions.
    • The average number of transactions made by a retained user on Ethereum is around 7.7. Ethereum has the maximum percentage of retained users and transactions made by these users among analyzed blockchains. Ethereum has a considerable number of loyal users who are responsible for a significant amount of transactions on it.
    • The average number of transactions made by a retained user on Flow is around 2.6. This is the lowest number among analyzed blockchains.
    • The average number of transactions made by a retained user on Harmony is around 88.13. Harmony has a small amount of extremely loyal users! who are responsible for a significant amount of transactions on it.
    • The average number of transactions made by a retained user on Near is around 17.8. There are a considerable amount of addresses on Near which are continuously making transactions with time intervals shorter than 1 hour between these transactions.

    As it is calculated, Flow has the lowest average number of transactions made by a retained user among analyzed blockchains. Now let’s take a look at the time interval between these transactions to get a broader perspective.

    In this analysis, a retained user is defined as a wallet address that has conducted more than one transaction on a blockchain, with a timestamp difference greater than 1 hour between at least two consecutive transactions.

    It should be noted that the time interval and possibility of another transaction from the same user, has been calculated between two consecutive transactions, i.e., if a user makes his/her second transaction two weeks after the first transaction, and the third transaction two weeks after the second, the third transaction will be grouped in “after two weeks” group.

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