IOSG Panoramic scan of on-chain data tools track.

IOSG's panoramic scan tracks on-chain data tools.

TL,DR;

Preface

Whether it is web2 or web3, data has always been a resource similar to oil in the information age, as well as a place where multiple participants compete for. 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, one can use the time lag of market lag to obtain excess returns (Alpha). The decentralized nature of blockchain makes on-chain data a public treasure. However, with the richness and improvement of multi-chain ecosystems, the diversification of on-chain ecosystems such as NFTs, Gamefi, and Socialfi, the difficulty of capturing Alpha from on-chain data has also increased. Ordinary users without technical background have difficulty in analyzing on-chain data, so there is a great demand for tools that can facilitate this analysis.

As for on-chain data, its unique characteristics make data tool products play an irreplaceable and important role:

  • Information is transparent and open, and anyone can access and verify on-chain data. For both project parties and investors, this is both an opportunity and a challenge, complementing each other and growing together. For project parties, products need to compete differentially; for investors, they need to continuously improve their ability to use tools and analyze data.

  • Information requires high timeliness and fast updates, 24/7. The timeliness of on-chain data is evident, as trading opportunities often come and go in an instant. Moreover, compared to traditional financial data disclosure, the time it takes for data to be recorded on the chain is almost negligible, and new on-chain behavioral records are generated around the clock.

  • Information has multiple dimensions, is diverse, and exhibits strong heterogeneity. On-chain data not only includes transaction operations but also various behaviors such as authorization and staking, as well as cross-chain flow of funds, and more.

  • There is a high technical barrier. Most users do not have knowledge reserves on setting gas fees, MEV, and other underlying principles of blockchain. Despite the availability of information in the on-chain dark forest, there is still a gap between obtaining information and converting it into actual operations and profits. Therefore, some automated tools empower ordinary players with the magic of being an on-chain “scientist”.

This article initially categorizes on-chain data analysis tools into two main categories: data-oriented and transaction-oriented (i.e., distinguishing whether they are ultimately focused on data or transaction behavior), but many tools actually have both data and transaction capabilities.

Data-Oriented

Market Overview Dashboard

Similar to financial terminals like Bloomberg in traditional finance, these tools aim to provide users with an overall perspective on observing and monitoring the market, generally focusing on data related to chains, protocols, and cryptocurrencies as a whole. In the early days of blockchain, data analysis indicators were relatively simple, such as token prices, the number of holding addresses, holding time, transaction records, and other basic indicators. Later, with the rise of DeFi protocols and the development of various sub-sectors such as NFT and gamefi, the dimensions of data have become greatly enriched. DeFi protocols commonly use indicators such as TVL, Marketcap, 24h volume, token holding distribution, as well as visualizations of token unlocking and release, NFT rarity rankings, and floor price distribution. Tokenterminal also provides indicators such as revenue and estimated market sales-to-earnings ratio, which are less related to short-term trading, so the data delay time is relatively long, while platforms like Nansen have a data delay in the range of minutes.

DeFiLlama User Interface

Data products are prone to internal competition, so most teams seek differentiation in competition:

  • Comprehensive research output: Nansen and Messari produce more research reports, and data product teams generally have analysts responsible for interpreting some data indicators. Research reports are also usually part of their products.

  • Focus on vertical sub-sectors: NFTSCAN focuses on market data for multi-chain NFTs, while L2Beat aggregates and visualizes data for various Layer2 solutions.

  • SQL query tools: Products like Dune Analytics and Bitquery provide users with the ability to customize SQL query statements, making the products more personalized but also requiring certain technical skills.

  • Enterprise solutions: Data products like Chainanalysis and amberdata mainly provide complete blockchain data solutions to B-end users, including exchanges and traditional financial institutions.

In addition, there are products that focus on visualization, such as Crypto Bubbles, as well as products that combine AI, such as DexCheck and KaitoAI. Overall, market overview dashboard products are the most common and widely used on-chain data analysis tools. Each product has different functional focuses, but the overall competition is fierce.

For analysis of projects like Nansen, please refer to the previous article by IOSG: 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 main analytical angle of on-chain data analysis tool products is from the perspective of addresses. The products that focus on address dimension analysis mainly fall into the following categories:

  • Blockchain browsers represented by Etherscan, as the underlying application, can view various interactive activities of individual addresses, as well as on-chain gas consumption.

  • Analytical platforms like Debank can view the holdings, profits and losses, and transaction records of individual addresses. Bubblemaps visualize the connections between addresses, allowing users to intuitively discover the relationships 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 trades.

Transaction-oriented

With the recent popularity of telegram bot tools like Unibot and Maestro, the token prices and TVL of many bot-like products have increased nearly tenfold in the past few 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 conveniently and quickly access mini-programs. Compared with data terminal products, transaction-oriented tools also cover the user’s operational process, which is extremely convenient for users, reducing the complexity and uncertainty from data analysis to trading, but also increasing security risks and financial costs (transaction fees and tool usage fees).

Changes in TVL of multiple Telegram projects

These automated trading tools will use wallet addresses created with representatives to trade or interact based on real-time data on the chain, or push real-time intelligence information to email, Discord, Telegram, etc. There is also a type of automated trading tool that is farming-oriented, which will randomly perform specified interactions in order to obtain airdrop rewards from project parties or carry out some programmatic arbitrage. Taking Unibot and Maestro as examples, common functions of on-chain automated trading tools include:

  • Limit order buying and selling: Similar to centralized exchanges, automated trading tools support limit orders for specific token prices and quantities.

  • Copy trading: Can replicate the transactions of a specified address, generally used to imitate the operations of “smart money” with a higher winning rate, providing a method for beginners and passive investors to profit from the cryptocurrency market with less effort.

  • Alerts: Can set up alerts for specific on-chain movements, such as transfers exceeding a specified transaction amount, and real-time scanning of newly deployed token contracts on the chain.

  • Simulated trading: Simulates the profit and loss of selling transactions before actual trading, such as whether the transaction may fail or lose money due to gas fee settings or slippage.

  • Private transactions: Avoid being frontrun and sandwich attacks, thereby reducing potential losses.

  • Farming: Randomly interact with projects, simulate users’ on-chain behaviors in new projects, and increase the possibility of obtaining token airdrops.

Unibot Sniper Feature List

The number of users of automated trading tools has grown rapidly recently, with the number of telegram bot users on the chain approaching 6,000 per day. Most of these users come from the long-running Maestro and the rising star Unibot, which together account for more than 80% of the user share of dex telegram bots.

Number of Telegram on-chain bot users

However, the real demand behind the attention brought by token price increases and market hotspots is worth careful consideration. The two most popular functions of Telegram bots – information push and copy trading – are not new demands, and in fact, there are already many centralized exchanges and relatively mature products (as shown in the figure below) that compete with Telegram bots. Therefore, the competitiveness of Telegram bots compared to these products is obviously weaker; therefore, the overall number of degenerate players in the crypto field is not large. Coupled with the option to choose a more secure and comprehensive automated trading platform, it is predicted that there are fewer experienced players among the user profiles of Telegram-based automated trading bots, and most people only use the information push function. However, from another optimistic perspective, the combination of Telegram, a social software with a huge user traffic and crypto-friendly, and bot that is easy to operate and user-friendly, may become one of the traffic entry points for Web3 onboard new users.

Copy trading platform products

Another product type that overlaps or is associated with automated trading tools is decentralized trading platforms similar to Dexscreener and Dextools. These products are mainly used to view the price changes of token trading pairs in real time and generally integrate dex swaps and basic contract security features on the front end, and perform basic honeypot transaction tax and other detections on the contracts deployed on-chain. The Unibot team recently launched a trading terminal called Unibot X, which integrates with the DEX tracking website GeckoTerminal. Users can directly log in to the UnibotX platform using the wallet address generated by their Telegram account. The platform features include limit orders, real-time K-line and transaction records, smart money trading, etc. It can be foreseen that the trading side DEX and bots may have closer connection and interaction in the future, thereby enhancing and enriching the user experience of decentralized trading. Although automated trading tools greatly enhance the technical capabilities of ordinary users, it is worth noting that such tools generally have significant centralization risks. The wallet addresses of most automated trading tools are generated by the tools themselves, and their private keys are completely exposed to the project team. As the saying goes in the crypto world, “Not your key, not your money”. If users want to use automated trading tools, they can only transfer funds to the address controlled by the project team, and at this time, they are completely in a weak position in the risk game.

The value logic of data tools track

The advantages and disadvantages of the business model of data tools

In the entire Web3 field, compared to some emerging niche products with difficult-to-prove practical needs, although the commercial logic of this tool product may not have the high ceiling and imaginative space of a new narrative, its market demand is more grounded and real. The business model of data tools is relatively mature, similar to that of web2 data companies, and has been successfully validated multiple times in the web2 field. Some tool projects, even if they do not issue their own tokens, have relatively stable cash flow income.

For projects that have not raised funds through token fundraising or taxation, the sources of project revenue include:

  • C-end tool user payments: similar to Web2’s SaaS, basic functions can be used for free, advanced functions require payment, or free services have certain limits or quantity restrictions, such as only being able to track 10 addresses. Charging C-end users can generally be divided into two types: one-time purchase and subscription: one-time purchase is similar to lifetime membership, and subscription refers to monthly/quarterly or annual payment;

  • B-end charges: packaging APIs or developing data systems, etc., charging developers and enterprises has been proven to be an effective monetization logic. For example, The Graph provides API services to multiple well-known defi/Gamefi projects, and Debank also has such business;

  • Advertising revenue: After the number of users reaches a certain level, project parties can use embedded advertising to monetize traffic.

From the characteristics of on-chain data and the current products, the on-chain data tool track is undoubtedly a track with deterministic opportunities, and it is destined to be a fiercely competitive track. Such products require certain infrastructure and equipment investment in the early stages, and 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 in market data dashboard products is already very fierce. The newly launched Arkham has already made some similar functions of Nansen free, which will inevitably have an impact on paid tools. However, due to the complexity of the data field, whether it is an all-in-one comprehensive platform, a small and refined product in a segmented field, or a possible leader in a vertical field, there is still a possibility. Tool products need faster product iteration and delivery capabilities, and the ability to dig out more valuable indicators in massive data, provide more comprehensive functions, and better help users increase the possibility of trading profits, in order to get rid of homogenized competition, 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 should build a token economy. The opposing voices mainly believe that the application scenarios of tokenized data tool products are limited, and it is difficult to maintain token prices after the heat of issuance decreases. Here, we take Arkham and Unibot, which have already issued tokens, as examples, representing the data side and the trading side mentioned above, to see the token economic model design of such products:

Not long ago, Arkham, as a data tool, issued its own token, which caused a lot of heat. Arkham is a comprehensive data analysis platform with multiple functions such as market dashboard, address analysis, market alerts, and intelligence rewards. The ARKM token is the native token of the Arkham Intel Exchange ecosystem, with a total circulation of 1 billion tokens, allocated as follows: treasury 50%, investors 20%, team 20%, liquidity 5%, rewards 5%.

ARKM token holders have governance rights and can vote on the strategic direction of Arkham. In addition, ARKM tokens can be used to reward users who contribute to the Arkham ecosystem. Users can earn ARKM rewards by submitting information intelligence, staking ARKM tokens, building ARKM ecosystem projects, and referring new users.

  • The intelligence bounty section provides a new application scenario for its economic model. The intelligence bounty is controlled by smart contracts, and a 2.5% fee is required when posting bounties, while a 5% fee is required when claiming bounties. Settlement using ARKM tokens enjoys a 20% discount, and locking ARKM tokens can enjoy a discount of up to 50% during settlement (but must hold the tokens for more than 30 days). Users with clue information can also initiate auctions or submit intelligence clues to the platform. Similar to bounties, auctions have a lock-up period of 15 days, and only the winning bidder can withdraw from the auction smart contract after the lock-up period. However, the auction initiator can withdraw early but must pay a 10% fee. The intelligence submitted to the platform will be rewarded with ARKM tokens based on different levels. The intelligence bought and sold on the platform will be exclusively held by the buyer for 90 days and then opened to all users, which promotes the intelligence and continuous development of the platform.

Arkham’s data-related functions are almost all freely accessible. 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. Anonymity is a highly regarded feature of cryptocurrencies, but Arkham’s intelligence platform goes against this principle by associating anonymous addresses on the blockchain with offline entities.

Compared to Arkham’s token model, which focuses on innovative businesses, Unibot’s token model is more traditional and simple. Unibot is an automated trading bot based on Telegram, currently deployed on Ethereum with a FDV of 176 million US dollars. It provides functions such as token exchange, limit orders, copy trading, privacy trading, and liquidity provision. Users can issue trading instructions through the Telegram chat box without any coding knowledge. Wallet addresses can be generated by Unibot or imported with private keys (higher risk).

As a leading project in the automated trading tool sector, Unibot has generated revenue of over 4000 ETH, mainly from tool fees and token transaction taxes. The token has profit-sharing functionality and requires holding 10 $UNIBOT tokens to be eligible. Rewards are proportional to the number of tokens held. Token holders will receive 40% of the tool platform’s trading fees and 1% of the UNIBOT token transaction taxes. Rewards are calculated every 2 hours and can be claimed every 24 hours. Transferring more than 200 tokens every 2 hours will result in confiscation of income share. The significant increase in token price has led to FOMO sentiment and increased attention in the market, bringing rapid growth of new users and a boost to the entire automated trading tool sector.

One major risk of the Arkham economic model is to focus on innovative businesses, while the risk of the Unibot token is mainly the unsustainability of the current price growth. By analyzing its revenue structure, it can be seen that 80% of its rapidly growing revenue comes from token transaction taxes, which largely depends on market popularity and the entry of new users. Once market popularity and trading volume start to decline, it is easy to suffer from a simultaneous decline in quantity and price.

It can be seen that the debate over the token model in the tool race is not groundless. How to enrich the ecosystem and expand the application scenarios of tokens is a key consideration when designing the economic model. Short-term and long-term interests should also be weighed, and although the short-term wealth effect has a significant driving force for user growth, in the long run, it is still necessary to seek a more sustainable development direction.

Possible Future Development Direction Combined with Socialfi

We know that the basic condition for social interaction is the participation of a sufficient number of users. Socialfi has always faced the problem of onboarding more users and user retention. Even Meta’s introduction of Threads, which is strongly tied to Instagram, has poor user stickiness. In the second week after the launch, the daily active users of Threads have decreased by 20%, and the average usage time has dropped from the initial 20 minutes to less than 5 minutes. Currently, the main social and UGC platforms in Web3 are web2 applications such as Twitter and Discord, lacking native Web3 social media. Data platforms have common interests and a high density of information, which has some potential as the basis for socialfi. The difficulty of data-oriented social interaction in Xueqiu Futu.

The Stream feature of Debank is a reflection of the development attempt towards socialfi, which can provide more verifiable information based on wallet addresses. The opinions of KOLs are more persuasive and conducive to the development of the field towards transparency and trustworthiness. Users can also reward valuable information, which is an ideal form of implementation for creator economy.

We know that the basic condition for social interaction is the participation of a sufficient number of users. Socialfi has always faced the problem of onboarding more users and user retention. Even Meta’s introduction of Threads, which is strongly tied to Instagram, has poor user stickiness. In the second week after the launch, the daily active users of Threads have decreased by 20%, and the average usage time has dropped from the initial 20 minutes to less than 5 minutes. Currently, the main social and UGC platforms in Web3 are web2 applications such as Twitter and Discord, lacking native Web3 social media. Data platforms have common interests and a high density of information, which has some potential as the basis for socialfi. The difficulty of data-oriented social interaction in Xueqiu Futu.

Personalized Recommendations

The transparency of on-chain data makes it natural to analyze individual behaviors and preferences. Currently, the personalized recommendation algorithms and engines of Web3 are still in their infancy. With the development of multi-chain ecosystems and applications, the dimensions of user profiles will also increase.

If we compare with the top products of Web2, recommendation algorithms are already a mature technology. Platforms like Taobao, Douyin, Meituan, and Bilibili push products or videos that you may like. However, whether it is data products like Dune or trading platforms like Opensea, personalized recommendations cannot be achieved. With the increase in data volume, the accuracy of recommendations will enter a positive feedback loop. The characteristics of blockchain data integration will give it an edge over Web2 in terms of accuracy. Moreover, with data sovereignty, it becomes possible to choose and fine-tune one’s personalized model. Similar to the recommendation algorithms in various fields of clothing, food, housing, and transportation in Web2, Web3’s social, trading, and gaming sectors also have their own application scenarios, and recommendation algorithms can be seamlessly integrated into different areas like Lego blocks.

Integration with AI

The transparency of on-chain data makes it natural to analyze individual behaviors and preferences. Currently, the personalized recommendation algorithms and engines of Web3 are still in their infancy. With the development of multi-chain ecosystems and applications, the dimensions of user profiles will also increase.

If we compare with the top products of Web2, recommendation algorithms are already a mature technology. Platforms like Taobao, Douyin, Meituan, and Bilibili push products or videos that you may like. However, whether it is data products like Dune or trading platforms like Opensea, personalized recommendations cannot be achieved. With the increase in data volume, the accuracy of recommendations will enter a positive feedback loop. The characteristics of blockchain data integration will give it an edge over Web2 in terms of accuracy. Moreover, with data sovereignty, it becomes possible to choose and fine-tune one’s personalized model. Similar to the recommendation algorithms in various fields of clothing, food, housing, and transportation in Web2, Web3’s social, trading, and gaming sectors also have their own application scenarios, and recommendation algorithms can be seamlessly integrated into different areas like Lego blocks.

Summary

This article analyzes and summarizes on-chain data tools from three aspects: product types, business models, and future development directions, hoping to provide more inspiration and thinking for practitioners, institutions, and individual investors in this field. The Web3 industry is still in its early exploratory stage, but the data track has already given birth to several well-known unicorns with valuations in the billions. From DeFi Summer to NFT Summer, and to the possible emergence of Layer2 Summer or Gamefi Summer in the future, from infrastructure to applications, all scenario judgments rely on the use and support of on-chain data analysis tools. Every address and every interaction construct a vast decentralized world, and this highly potential track will become one of the most important anchor points. In this data-native industry, we still have great 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 our next article.

We will continue to update Blocking; if you have any questions or suggestions, please contact us!

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