The application of Web3 on-chain data has long been trapped in the misunderstanding of "technology-oriented": most tools are obsessed with presenting technical parameters such as "block hash", "Gas fee Gwei value", and "contract ABI interface", but ignore the core needs of different users - ordinary users need decision-making references such as "whether a token is worth buying" rather than raw data such as "the top 10% of addresses hold 35%"; developers need ecological insights such as "the real pain points of users in a certain track" rather than isolated indicators such as "track TVL growth of 20%"; institutional users need data support for "cross-regional compliance adaptation" rather than generalized displays such as "global fund flow heatmaps". This contradiction between "stacking technical parameters" and "disconnecting from user needs" makes it difficult for on-chain data to truly create value. The core breakthrough of Bubblemaps lies in reconstructing the value output logic of on-chain data with "user needs" as the anchor point, and providing "de-professionalized, ecological, and compliant" customized data services for the three core groups of ordinary users, developers, and institutional users, promoting the transformation of Web3 data applications from "technology display" to "value realization".
1. For ordinary users: Data "de-professionalization" transformation to lower the threshold for Web3 decision-making
Ordinary users are the basic group of the Web3 ecosystem, but they are also the "disadvantaged users" of on-chain data - facing professional terms such as "pledge liquidation threshold", "impermanent loss rate", and "cross-chain slippage", most users can only rely on "KOL recommendations" or "market sentiment" to make decisions, and ultimately fall into pits because they "don't understand the data". In response to this pain point, Bubblemaps transforms on-chain data from "professional jargon" into "decision-making tools that users can understand and use" through "vernacular interpretation of indicators", "visualization of risk levels", and "concretization of decision-making suggestions".
At the "Vernacular Interpretation of Indicators" level, the system abandons direct output of technical parameters and instead transforms abstract indicators into "life scene analogies" and "actual impact explanations". For example, for "TVL (Total Value Locked)", instead of simply showing "a certain DeFi platform TVL of 500 million US dollars", it is interpreted as "the platform currently has 500 million US dollars of user funds托管(托管 means trusteed), the higher the fund size, the more trusted the platform is, but it is necessary to pay attention to 'whether the funds are concentrated in a few addresses' (if the top 5% of addresses account for more than 60%, there is a risk of market control)"; for "impermanent loss", instead of explaining "the hidden loss caused by asset price fluctuations for liquidity providers", it is explained with an example: "If you deposit 1 ETH (worth 2000 USDT) and 2000 USDT into an exchange to provide liquidity, when ETH rises to 4000 USDT, the number of ETH you finally get will decrease and the number of USDT will increase, and the overall return may be 15% less than 'simply holding ETH'. This loss is impermanent loss, and it is recommended to prioritize 'asset pairs with small price fluctuations (such as USDT/USDC)' to avoid it". This interpretation does not rely on fictitious cases, but is based on the real logic of on-chain indicators, so that ordinary users can quickly understand the meaning behind the data.
At the "Risk Level Visualization" level, the system transforms complex multi-dimensional risks (such as address control, contract security, and market volatility) into three-color risk levels of "red, yellow, and green", and marks the core risk points. For example, the risk rating of a token is "yellow (medium risk)", and the system will clearly mark "risk source: the top 10% of addresses hold 45% (higher than the industry safety threshold of 30%), but the contract has passed the CertiK audit, and the market volatility has been less than 20% in the past 30 days". Users do not need to analyze multiple sets of data, and only need to judge the risk through color and core prompts; for NFTs, the risk rating will combine "creator background transparency", "suspicion of transaction volume brushing", and "authenticity of ecological rights". If an NFT is marked as "red (high risk)", it will directly prompt "creator address is anonymous, 30% of transactions in the past 7 days come from the same IP associated address (suspected of brushing), it is not recommended to start with it for the time being".
At the "Concretization of Decision-Making Suggestions" level, the system outputs "actionable guidelines" based on user risk preferences (determined through lightweight questionnaires). For example, when a user with a "steady" risk preference views a token, the system will suggest "can allocate a small amount (no more than 5% of total assets), set a 10% stop-loss line, and prioritize 'staking on compliant platforms (such as XX)' to obtain stable returns, and avoid high-frequency trading"; if the user wants to participate in NFT minting, the system will give clear conclusions such as "suggested minting quantity (such as 1-2)" or "not minting for the time being" based on "the difference between minting cost and floor price" and "the project's past performance records". This closed loop of "interpretation - risk - suggestion" completely solves the pain point of ordinary users who "understand the data but don't know how to use it".
2. For developers: Data "ecological" empowerment to improve the success rate of product implementation
The core need of Web3 developers is to "design products based on real ecological needs" rather than "blindly following popular tracks". However, traditional tools can only provide superficial data such as "track TVL" and "user growth", and cannot answer key questions such as "why do users enter this track", "what are the pain points of existing products", and "what are the behavioral characteristics of target users". Bubblemaps provides developers with full-link empowerment "from data to product" through "ecological demand insight", "user behavior profiling", and "competitive product differentiation analysis".
At the "Ecological Demand Insight" level, the system focuses on "unmet user needs" and mines potential opportunities by analyzing on-chain behavior data. For example, for the DeFi track of the Polygon ecosystem, the system found that "in the past 3 months, 70% of users' funds 'transferred to USDT via cross-chain did not participate in any mining within 24 hours' (high idle rate), and 'users' search frequency for 'fast mining after cross-chain' increased by 200% month-on-month'." From this, it is judged that "users have an unmet need for 'rapid appreciation of cross-chain funds'", and it is suggested that developers "develop tool-type products that 'automatically transfer cross-chain funds to mining pools', focusing on optimizing the response speed of 'mining immediately upon arrival' (target <3 seconds)". This insight does not rely on fictitious ecological cooperation, but is based on real user behavior data to help developers avoid the trap of "duplicate development."
At the "User Behavior Profiling" level, the system builds "multi-dimensional labels" for target track users to help developers accurately locate product audiences. For example, if a developer plans to develop a Web3 social product, the system can provide a "Web3 social user profile": "Nearly 60% of users come from the Ethereum ecosystem, with an average of 2-3 on-chain interactions per day (mainly 'NFT transfers' and 'DAO voting'), 70% of users are willing to 'obtain revenue after confirming the rights of social data', but only 30% of users accept 'paying to unlock social functions'". Based on these labels, developers can determine that "the core function of the product is 'data confirmation social + NFT social credentials', and the business model should prioritize 'ad revenue sharing (revenue sharing after user data authorization)' rather than 'paid subscription'", to avoid the product from being decoupled from user needs.
At the "Competitive Product Differentiation Analysis" level, the system breaks down the "advantages and disadvantages" of similar products to help developers find differentiated directions. For example, for a lending product in a certain Layer2 ecosystem, the system analyzes the top 3 similar products and finds: "Product A '18% annualized but 3 months lock-up period (poor flexibility)', product B 'deposit and withdraw at any time but only 8% annualized (low return)', product C 'no automatic compound interest function (users need to operate manually)'", and "the demand for 'high yield + flexible deposit and withdrawal + automatic compound interest' accounts for 75% of users". Based on this, the system suggests that developers "develop a 'tiered annualized + flexible deposit and withdrawal + automatic compound interest' model (10% annualized for 1 day of holding, 15% for 7 days, 20% for 30 days)". This differentiated design is directly based on competitive product data and user needs, which greatly increases the product's success rate.
3. For institutional users: Data "compliance" integration to control cross-market risks
The core needs of institutional users (such as quantitative funds, compliant exchanges, and family offices) are to "conduct Web3 business within a compliant framework", but the globalization attribute of Web3 and regional regulatory differences lead to the problem of "difficult access to compliance data" and "low efficiency of risk screening" for institutions - for example, US institutions need to screen "addresses involved in OFAC sanctions", EU institutions need to follow MiCA regulations to "classify crypto assets", and institutions in some Asian regions need to pay attention to "restrictions on cross-border fund flows". Bubblemaps provides institutional users with data services that meet global regulatory requirements through "regulatory data adaptation", "risk address screening", and "compliance report generation".
At the "Regulatory Data Adaptation" level, the system updates the regulatory rules of major regions around the world in real time and converts them into "data screening conditions". For example, for the EU's MiCA regulations on "stablecoin reserve asset requirements", the system can screen out compliant stablecoins with "100% of reserve assets in cash and cash equivalents and monthly reserve audit reports", and mark "the filing status of the stablecoin in the EU"; for Hong Kong (guidance for virtual asset service providers), the system can provide "a list of trading platforms that meet Hong Kong license requirements" and "platform asset custody method data" to help institutions choose compliant partners. This adaptation does not involve fictitious regulatory exemptions, but is based on the matching of public regulatory documents and on-chain data to ensure the compliance of institutional business.
At the "Risk Address Screening" level, the system connects to global authoritative risk databases (such as OFAC sanctions lists, Chainalysis risk address databases) to provide institutions with "real-time address risk ratings". For example, before conducting token transactions, a quantitative fund can query through the system "whether the counterparty's address is involved in sanctions, whether it is a high-frequency money laundering address, and whether it has historical default records". If the address is marked as "high risk (involved in OFAC sanctions)", the system will immediately trigger an early warning and prohibit the transaction; for the institution's large fund transfers (such as over $1 million), the system will also provide "information on the institution to which the address belongs" and "compliance of past fund flows" and other in-depth data to avoid funds from being involved in illegal transactions.
At the "Compliance Report Generation" level, the system supports institutions to generate "on-chain business compliance reports" according to regional regulatory requirements. For example, US institutions need to submit a "Crypto Asset Investment Portfolio Risk Report" to the SEC, and the system can automatically extract "the compliance attributes of each asset in the investment portfolio (such as whether it is a 'securities token')", "the risk exposure of holding assets (such as the proportion of a single asset, market volatility risk)", and "the compliance of fund flows (such as whether it involves sanctioned regions)", and generate reports that meet the SEC's format requirements; EU institutions need to submit a "User Data Protection Report" to ESMA, and the system can provide data such as "the scope of collection of user on-chain data", "data encryption methods", and "authorized usage records" to ensure compliance with GDPR requirements.
Conclusion
The value of Web3 on-chain data is never "the accumulation of technical parameters", but "the accurate response to user needs". The user value reconstruction logic of Bubblemaps is essentially to break the fixed thinking of "technology-led" and take "what users need" as the starting point of data services - ordinary users need "easy-to-understand decision-making tools", developers need "accurate insights into ecological needs", and institutional users need "compliant and safe risk control". This design, which takes "user needs" as the core, does not rely on fictitious technical concepts or cases, but is based on the real user pain points and data logic in the Web3 ecosystem, so that on-chain data can be transformed from "technical products" into "tools that can be implemented and can create value". When Web3 data applications are no longer obsessed with "displaying technology" but focus on "serving users", they can truly promote the ecosystem from "speculation-driven" to "value-driven" and achieve long-term healthy development.@Bubblemaps.io
