Author: Wendy, IOSG Ventures
TL,DR;
The characteristics of on-chain data make on-chain data analysis tools a strong demand. This article divides the existing products in the market into data-led or transaction-led types according to different focuses;
Data Kanban products are heavily involved and require differentiated competition; automated trading tools are highly popular and risks need to be paid attention to. The two types of products have different needs, and their functions partially overlap but will not completely replace them;
The commercialization of data products is an issue worthy of careful discussion. This article briefly describes the advantages and disadvantages of commercialization with or without tokens. For more detailed further discussion, see the next article;
Possible future development directions for on-chain tools include the development of socialfi and communities, personalized recommendations based on user portraits, and integration with AI.
Preface
Whether it is web2 or web3, data has always been a resource similar to oil in the information age, and it is also a place where multiple participants dig for gold and must compete. On-chain Alpha refers to valuable and profitable information on the blockchain that has not been widely disseminated and discovered. By analyzing on-chain data, the time difference of market lag can be used to obtain excess returns Alpha. The decentralized nature of blockchain makes on-chain data a public treasure, but with the enrichment and improvement of multi-chain ecology, the diversification of on-chain ecology such as NFT, Gamefi and Socialfi, while the on-chain Alpha content increases, the difficulty of capturing Alpha from the chain also increases, and the technical capabilities of ordinary users are difficult to achieve. Therefore, ordinary users with non-technical backgrounds have a great demand for tools to analyze on-chain data.
As for on-chain data, its unique characteristics as follows make data tool products play an irreplaceable and important role:
The information is open and transparent, and all the data on the chain can be checked and verified. For both the project owners and investors, it is both an opportunity and a challenge, which complement each other and lift all boats. For the project owners, the products need to be differentiated for competition; and for investors, they need to continuously improve their ability to use tools and analysis.
The information has high timeliness requirements, fast update speed, and 24 hours non-stop. The timeliness of on-chain data is obvious, and trading opportunities are often fleeting; and compared with the data disclosure of traditional finance, the time for data to be on the chain is almost negligible, and new on-chain behavior records are generated non-stop around the clock.
Information has multiple dimensions, multiple sources, and strong heterogeneity. On-chain data not only includes transaction operations, but also authorization, pledge and other behaviors, as well as cross-chain flow of funds.
The technical threshold is high. Most users do not have the knowledge reserve for the setting of gas fees and the underlying principles of blocks such as MEV. On-chain dark forests still have a long way to go to convert information into actual operations and profits, so some automated tools give ordinary players the magic of "scientists" on the chain.
This article preliminarily divides on-chain data analysis tools into two categories: data-oriented and transaction-oriented (i.e., whether the final user-oriented tool is data or transaction behavior), but many tools are actually both data and transaction tools.
Data-oriented Overall market data dashboard
Similar to the role of financial terminals such as Bloomberg in traditional finance, this type of tool is intended to provide users with an overall perspective to observe and monitor the market, generally focusing on the overall data of chains, protocols and currencies. In the early days of blockchain, the indicators for data analysis were relatively simple, such as basic indicators such as token price, number of addresses holding coins, holding time, and transaction records. Later, with the rise of defi protocols and the development of various sub-sectors such as NFT and gamefi, the dimensions of data have been greatly enriched. Defi protocols often use TVL, Marketcap, 24h volume, token holding distribution, and visualization of token unlocking and release, NFT rarity ranking, floor price distribution, etc. Tokenterminal also provides indicators such as revenue and expenses and estimated price-to-sales ratio and price-to-earnings ratio. Because it is not closely related to short-term trading, the data delay time is relatively long, while the data delay of platforms such as Nansen is at the minute level.
DeFiLlama User Interface
Data products are in a state of internal competition, so most teams are also seeking breakthroughs in differentiated competition:
Comprehensive research report output: Nansen and Messari output a large number of research reports. The data product team usually has analysts responsible for interpreting some data indicators, and research reports are usually included as part of their products.
Focus on vertical segments: NFTSCAN focuses on the market data of multi-chain NFTs, and L2Beat aggregates and visualizes data from various Layer2s.
SQL query tools: Products such as Dune Analytics and Bitquery provide users with the ability to customize SQL query statements, making the products more personalized, but with relatively certain technical barriers.
Enterprise solutions: The main business model of data products such as Chainanalysis and Amberdata is to provide complete blockchain data solutions to B-side users. The main users include exchanges and traditional financial institutions.
In addition, there are products that focus on visualization, such as Crypto Bubbles, as well as DexCheck and KaitoAI that are combined with AI. In general, market data dashboard products are the most common and frequently used on-chain data analysis tools. The functional focus of each product is different, but the overall competition is fierce.
For analysis of Nansen and other projects, please see IOSG’s previous article: https://mp.weixin.qq.com/s/o1pO7unj3cUS9sWt4q_gBw.
Address dimension analysis
In addition to providing data support from the perspective of the overall market, another major analysis angle of on-chain data tool products is from the perspective of addresses. Products that focus on address dimension analysis mainly include the following categories:
Blockchain browsers such as Etherscan, as underlying applications, can view various interactive activities of a single address, as well as on-chain gas consumption, etc.
Analysis platforms such as Debank can view the holdings, profits and losses, and transaction records of a single address. Bubblemaps visualizes the connections between addresses, allowing users to more intuitively discover the connections between addresses and the flow of funds. Nansen is also famous for this type of analysis. Smart Money tracking can be used to track smart money and increase the possibility of profit by observing its trading behavior or following its transactions.
Transaction Oriented
With the recent popularity of telegram bot tools such as Unibot and Maestro, the token prices and TVL of many bot products have increased nearly tenfold in recent weeks, which is particularly prominent in the bear market. Telegram is a chat software with 700 million monthly active users, which can provide rich APIs for developers to easily and quickly access mini-programs. Compared with data terminal products, transaction-oriented tools also cover the user-side operation process, which is extremely convenient for users and reduces the complexity and uncertainty from data analysis to transactions, but also increases security risks and capital costs (the cost of the transaction itself and the cost of using the tool).
TVL changes of multiple Telegram projects
These automated trading tools will use the wallet addresses created by the chain to trade or interact based on the real-time data on the chain, or push the chain intelligence information to mailboxes, Discord or Telegram in real time; there is also a type of automated trading tool that is farming-oriented and will perform designated interactions in a random form in order to obtain airdrop rewards from the project party or conduct some programmatic arbitrage. Taking Unibot and Maestro as examples, the common functions of on-chain automated trading tools are:
Limit order buying and selling: Similar to centralized exchanges, automated trading tools support limit orders for tokens at specific prices and quantities.
Copy trading: You can copy the transactions of a specified address, which is generally used to imitate the operations of "smart money" with a higher winning rate. It provides a way for novices and passive investors to profit from the crypto market with less effort.
Alert: You can set up push notifications for on-chain movements of specific addresses, such as transfer transactions greater than a specified transaction amount, and real-time scanning of new token contract deployments on the chain.
Simulated trading: simulate the profit and loss of the selling transaction before the actual transaction, such as whether the transaction may fail or lose money due to the setting of gas fee or slippage.
Private transactions: Avoid front-running and sandwich attacks, thereby reducing potential losses.
Farming: Randomly interact with projects, simulate users' on-chain behavior in new projects, and increase the possibility of obtaining token airdrops.
Unibot Sniper Features List
The number of users of automated trading tools has increased rapidly recently. The number of Telegram bot users for on-chain transactions has recently reached nearly 6,000 per day. Most of these users come from Maestro, which has been in operation for a long time, and Unibot, a rising star. Together, they account for more than 80% of the user share of Dex Telegram bot.
Number of robot users on Telegram Chain
However, through the attention bubble brought by the rise in token prices and the rotation of market hot spots, the real demand behind it is worth careful consideration. The two most mainstream functions of Telegram bot - information push and copy trading are not new demands. In fact, there are already many centralized exchanges and relatively mature products (as shown below). Telegram bots are obviously less competitive than such products; therefore, the overall base of degen players in the encryption field is not large, and there are safer and more comprehensive automated trading platforms to choose from. Therefore, the author predicts that there are fewer senior players in the user portrait of automated trading robots based on Telegram, and most people only use the information push function; but from another optimistic perspective, Telegram, a social software with huge user traffic and encryption-friendly, combined with a simple and user-friendly bot, may become one of the traffic entrances for Web3 onboard newbies.
Copy trading platform products
Another type of product that overlaps or is more related to automated trading tools is decentralized trading platforms such as Dexscreener and Dextools. This type of product is mainly used to view the price changes of token trading pairs in real time. Generally, dex swaps and basic contract security functions are integrated on the front end, and basic honeypot transaction taxes and other tests are performed on the contracts deployed on the chain. The Unibot team recently launched the trading terminal Unibot X, which is integrated with the DEX tracking website GeckoTerminal. Users can directly use the wallet address generated by the Telegram account to log in to the UnibotX platform. The platform functions include limit orders and other buy and sell transactions, real-time K-line and transaction records, smart money transactions, etc. It can be foreseen that the trading side DEX and Bot may have closer connections and interactions in the future, thereby enhancing and enriching the user experience of decentralized transactions.
Although automated trading tools have greatly enhanced the technical capabilities of ordinary users, it is worth noting that such tools generally have great centralization risks. The wallet addresses of most automated trading tools are generated by the tools, and their private keys are completely exposed to the project parties. As the famous saying in the crypto world goes, "Not your key, not your money", if users want to use automated trading tools, they can only transfer funds to addresses that the project parties have control over, and at this point they are completely at a disadvantage in the risk game.
The value logic of the data tool track
Advantages and disadvantages of the business model of data tools
In the entire Web3 field, compared with some products in emerging niche areas that are difficult to prove actual demand, although the business logic of this tool product does not sound as high as the new narrative and has no imagination, its market demand is more practical and real. The business model of data tools is relatively mature, similar to the logic of web2 data companies, and has been successfully verified many times in the web2 field. Even if some tool projects do not issue their own tokens, they still have relatively stable cash flow income.
For projects that do not raise funds through tokens or collect taxes, the sources of income for the project include:
C-end tool user payment: Similar to Web2 SaaS, basic functions can be used for free, advanced functions need to be paid, or free services have certain quotas or quantity restrictions, such as only tracking 10 addresses. C-end charges can generally be divided into two types, buyout and subscription: buyout is similar to lifetime membership, and subscription refers to monthly/quarterly or annually;
B-side charging: Packaging APIs or developing data systems, etc. Charging developers and enterprises has also been proven to be an effective monetization logic. For example, The Graph provides API services to many well-known defi/Gamefi projects, and Debank also has such business;
Advertising revenue: When the number of users reaches a certain level, the project owner can monetize traffic by embedding advertisements.
Judging from the characteristics of on-chain data and current products, the on-chain data tool track is undoubtedly a track with certain opportunities, and it is also destined to be a track with fierce competition. This type of product requires a certain amount of infrastructure and equipment investment in the early stage. The openness and availability of data also make Web3 on-chain data analysis tools lose their moat in terms of data sources. For example, the competition for market data dashboard products is already very fierce. The newly launched Arkham has made some similar functions of Nansen free, which will inevitably have an impact on paid tools; but due to the complexity of the data field, whether it is an all-in-one comprehensive platform or a small and refined product in a niche field, it is still possible to become a leader in the vertical field. Tool products need to have faster product iteration and delivery capabilities, as well as the ability to mine more valuable indicators in massive data, provide more complete functions, and better help users increase the possibility of profitable transactions, so as to get rid of the competition of product homogeneity and establish their own advantages and barriers.
Analysis of the token economic model of data tool products
There is also some debate in the industry about whether tool products need to build a token economy. The main objection is that the application scenarios of data tool product tokens are limited, and it is difficult to maintain the price of the token after the issuance enthusiasm decreases. Here we take Arkham and Unibot, which have already issued tokens, as examples, representing the two types of products mentioned above, namely the data side and the transaction side, to look at the token economic model design of such products:
Arkham recently issued its own token as a data tool, which caused a lot of heat. Arkham is a comprehensive data analysis platform with multiple functions such as market dashboard, address analysis, market alerts, intelligence rewards, etc. ARKM tokens are the native tokens of the Arkham Intel Exchange ecosystem, with a total issuance of 1 billion, which are distributed as follows: 50% for the treasury, 20% for investors, 20% for the team, 5% for market making, and 5% for rewards.
ARKM token holders have governance rights and can vote on the strategic direction of Arkham. In addition, ARKM tokens can also be used to reward users who contribute to the Arkham ecosystem. Users can obtain ARKM rewards by submitting information intelligence, staking ARKM tokens, building ARKM ecological projects, and recommending new users.
The intelligence bounty section provides a new application scenario for its economic model. The intelligence bounty is controlled by a smart contract. A handling fee of 2.5% and 5% is required when issuing a bounty and claiming a bounty, respectively. A 20% discount can be enjoyed by using ARKM for settlement, and a discount of up to 50% can be enjoyed by locking ARKM at the time of settlement (but the currency must be locked for more than 30 days). Users with clue information can also initiate auctions or submit intelligence clues to the platform. Like bounties, auctions have a 15-day lock-up period, after which the winning bidder can withdraw from the auction smart contract, but the auction initiator can withdraw early, but must pay a 10% fee. Intelligence submitted to the platform will be rewarded with ARKM tokens according to different levels. Intelligence bought and sold on the platform will be exclusively held by buyers for 90 days before being opened to all users, which also promotes the intelligence and continuous development of the platform.
Almost all of Arkham's data-related functions are free and open. We can see that its ecosystem and token application focus on the intelligence bounty platform, which is also the most controversial feature of this product. The anonymity of cryptocurrency is a major feature that is highly praised by everyone, but Arkham's intelligence platform goes the other way and marks the anonymous addresses on the chain with the corresponding entities off the chain.
Compared with the Arkham token model's emphasis on innovative business, Unibot's token model is more traditional and simple. Unibot is an automated trading robot based on Telegram. It is currently only deployed on Ethereum, with a FDV of $176 million. It provides functions such as token exchange, limit orders, copy trading, privacy trading, and liquidity provision. Users do not need any code foundation and can issue trading instructions only through the Telegram chat box. The wallet address can be generated by Unibot, or import your own private key (higher risk).
As a leading project in the automated trading tool track, Unibot's revenue has exceeded 4000 ETH, mainly from tool fees and token transaction taxes. The token has a shared income function, and you must hold 10 $UNIBOT tokens to be eligible. The reward is proportional to the number of tokens held. Token holders will receive 40% of the tool platform transaction fees and transaction taxes on UNIBOT token transactions (1% of the total). The reward is calculated every 2 hours and can be claimed 24 hours a day. If more than 200 tokens are transferred every 2 hours, the income share will be confiscated. The huge increase in the coin price has caused FOMO sentiment and attention in the market, bringing about a rapid increase in new users, and the entire automated trading tool track has also ushered in a pull-up.
A major risk of the Arkham economic model is that it places its focus on innovative businesses, while the risk of Unibot tokens is mainly the unsustainable growth of the current price of the token. By analyzing its revenue structure, it can be seen that 80% of its rapidly growing revenue comes from transaction taxes on tokens, which largely depends on market heat and the entry of new users. Once market heat and transaction volume begin to decline, it is easy to suffer a Davis double kill of falling volume and price.
It can be seen that the debate about the token model of the tool track is not groundless. How to enrich the ecology and expand the application scenarios of tokens are the key issues that should be considered when designing economic models. There should also be a balance between short-term and long-term interests. The short-term wealth-making effect certainly has a greater driving effect on user growth, but in the long run, we should still find a more sustainable construction direction.
Possible future development directions
Integrate with Socialfi
We know that the basic condition for social interaction is to have enough users participating. The problem that Socialfi has always faced is how to onboard more users and retain users. Even threads launched by meta have poor user stickiness when strongly tied to Instagram. In the second week after the launch, the daily active users of Threads have decreased by 20%, and the user usage time has also dropped from an average of 20 minutes at the initial launch to less than 5 minutes. The main social and UGC platforms of Web3 are web2 applications such as Twitter and Discord, and there is a lack of native Web3 social media. Users of data platforms have common interests and a high density of information, which has certain potential as the basis of socialfi. xueqiu futu The difficulty of data-driven social interaction.
Debank's Stream function is a reflection of its attempt to develop into socialfi. Using wallet addresses as accounts can provide more verifiable information, and the opinions of kols are more convincing, which is conducive to promoting the field to develop in a more transparent and credible direction. Users can also reward valuable information, which is an ideal form of realization of the creator economy.
We know that the basic condition for social interaction is to have enough users participating. The problem that Socialfi has always faced is how to onboard more users and retain users. Even threads launched by meta have poor user stickiness when strongly tied to Instagram. In the second week after the launch, the daily active users of Threads have decreased by 20%, and the user usage time has also dropped from an average of 20 minutes at the initial launch to less than 5 minutes. The main social and UGC platforms of Web3 are web2 applications such as Twitter and Discord, and there is a lack of native Web3 social media. Users of data platforms have common interests and a high density of information, which has certain potential as the basis of socialfi. xueqiu futu The difficulty of data-driven social interaction.
Personalized recommendations
The openness and transparency of on-chain data makes it easier to analyze personal behavior and preferences. Currently, Web3's personalized recommendation algorithms and engines are still in their infancy. As multi-chain ecosystems and applications become more diverse, the dimensions of user portraits will also increase.
If we use the top products of web2 as a comparison, the recommendation algorithm is already a fairly mature technology. Taobao, Douyin, Meituan, and Bilibili will all push products or videos that you may like. However, today, neither data products such as dune nor trading markets such as opensea can provide personalized recommendations. As the amount of data increases, the accuracy of recommendations will also enter a positive feedback flywheel, and the data characteristics of blockchain will make the accuracy of recommendations better than web2. Moreover, with data sovereignty, it is possible to choose and fine-tune your own personalized model. Similar to the recommendations in multiple fields of food, clothing, housing, and transportation in web2, social networking, transactions, and games in web3 also have their own application scenarios. Recommendation algorithms can be completely spliced into different fields like Lego blocks.
Combining with AI
The openness and transparency of on-chain data makes it easier to analyze personal behavior and preferences. Currently, Web3's personalized recommendation algorithms and engines are still in their infancy. As multi-chain ecosystems and applications become more diverse, the dimensions of user portraits will also increase.
If we use the top products of web2 as a comparison, the recommendation algorithm is already a fairly mature technology. Taobao, Douyin, Meituan, and Bilibili will all push products or videos that you may like. However, today, neither data products such as dune nor trading markets such as opensea can provide personalized recommendations. As the amount of data increases, the accuracy of recommendations will also enter a positive feedback flywheel, and the data characteristics of blockchain will make the accuracy of recommendations better than web2. Moreover, with data sovereignty, it is possible to choose and fine-tune your own personalized model. Similar to the recommendations in multiple fields of food, clothing, housing, and transportation in web2, social networking, transactions, and games in web3 also have their own application scenarios. Recommendation algorithms can be completely spliced into different fields like Lego blocks.
Summarize
This article analyzes and summarizes the on-chain data tools from three aspects: product type, business model and future development direction, hoping to give more inspiration and thinking to practitioners, institutions and individual investors in this field. Today, the Web3 industry is still in the early exploration stage, but the data track has already produced several well-known unicorns with a valuation of one billion US dollars. From Defi Summer to NFT Summer, and then to the possible Layer2 Summer or Gamefi Summer in the future, from infra to application, all scenario judgments are inseparable from the use and support of on-chain data analysis tools. Every address and every interaction builds the stars and sea of the decentralized world, and this highly potential track will also become one of the most important anchor points. In this data-native industry, we are still full of expectations for the Alpha magic of on-chain data.
Due to space limitations, we will continue to discuss the specific practices of commercializing data products in the next article.
