In the Web3 ecosystem, on-chain data often exists in the form of 'abstract indicators' — users can see 'TVL values, holding ratios, transaction frequencies,' but it's difficult to link these data to actual values like 'asset appreciation, risk avoidance, ecosystem participation,' leading data to become ineffective information that is 'seen but not used.' Bubblemaps breaks away from the shallow positioning of 'data display' with 'value visualization' as the core, through 'data-scenario value binding, abstract indicators-practical rights transformation, hidden connections-intuitive profit linkage,' allowing on-chain data to transform from 'cold numbers' into 'practical assets that are tangible, perceptible, and monetizable,' helping users convert data into actual profits, enabling project parties to activate ecological value through data, and allowing institutions to explore business opportunities through data. This model of 'data as assets, insights as profits' not only activates the practical value of on-chain data but also upgrades the transparency of Web3 from 'information tools' to 'value realization infrastructure.'
1. Data-scenario value binding: Linking abstract indicators to 'specific profit scenarios'
The core demand of users for on-chain data is essentially 'what value can data help me obtain in specific scenarios' — checking token data is for 'judging whether it can make money,' examining NFT data is for 'determining whether it is worth collecting,' but traditional tools only display indicators without linking them to scenario value. Bubblemaps' 'scenario value mapping' function binds every abstract indicator to specific profit scenarios, allowing users to easily understand 'what actual benefits data can bring.'
In the token investment scenario, the mapping logic is intuitively applied:
• Observing 'the number of small and medium wallets of a certain token increased by 200% weekly,' concurrently marking 'corresponding scenario value: community activity rising, short-term could have premium space, suitable for small-scale operations';
• Discovering that 'the TVL of a certain DeFi token pool has stabilized and increased for three consecutive weeks,' then prompting 'corresponding scenario value: ample liquidity in the pool, stable mining returns, suitable for long-term staking.'
A user discovered through this feature that '60% of the holdings of a certain Solana token are held by old players and there are no outflows,' corresponding to 'scenario value: consensus is solid, long-term holding is expected to accompany ecosystem appreciation,' so they chose to hold long-term. Three months later, the token price tripled, successfully gaining returns.
In the NFT collection scenario, value mapping is closer to user needs:
• Viewing that '80% of transactions in a certain NFT series in the last 30 days came from ecosystem users,' indicating 'corresponding scenario value: ecosystem user recognition, the collection may unlock more ecological rights in the future (such as game props, DAO voting rights)';
• Discovering 'the floor price of a certain NFT is lower than the mint price but old players are increasing their holdings,' prompting 'corresponding scenario value: signal of value bottom, old players buying at the bottom may push prices up, suitable for low buy-in.'
A certain NFT collector acquired a certain niche series based on this, and subsequently, this series launched rights for 'holding the NFT to participate in ecological game dividends,' resulting in both the collection value and dividend returns being realized, achieving a closed loop of 'data insight → collection decision → profit realization.'
2. Indicator-rights transformation: Allowing data performance to exchange for 'ecological practical rights'
On-chain data can not only guide decision-making but also be directly transformed into 'usable ecological rights' — users' on-chain behavior data (like participating in community analysis, contributing data insights), project’s on-chain performance data (like meeting decentralized standards, high community activity) can all be exchanged for actual rights. Bubblemaps' 'data rights exchange system' turns abstract data into 'consumable, usable' practical assets.
For users, the incentive data for rights exchange participation:
• User-submitted 'on-chain anomaly insights' adopted by the platform can be exchanged for 'DeFi mining interest coupons for cooperative projects' (e.g., '10% extra annualized, valid for 7 days');
• Accumulating viewing and analyzing 'data of 10 different track tokens' can unlock 'priority qualifications for NFT minting' or 'cross-chain transfer fee reduction vouchers.'
A user obtained a whitelist qualification for a certain popular NFT project by submitting 'insight on the control risk of a certain meme coin,' and after minting, the floor price of the collection increased fivefold, achieving a transformation of 'data contribution → rights acquisition → asset appreciation.'
For project parties, rights transformation promotes data optimization:
• The project's 'degree of decentralization of the token meets the standard (top holding percentage below 30%)' can be exchanged for 'homepage recommendation on the platform' or 'user precision reach service';
• The 'community interaction data (like the number of user analysis report submissions) of an NFT project ranks high,' can receive 'resources for online promotional activities hosted by the platform.'
A certain startup DeFi project optimized the 'pool user distribution data,' and after meeting the standard, exchanged it for a homepage recommendation on the platform, gaining over 2000 new users within three days, and a TVL increase of 150%, achieving a positive cycle of 'data optimization → rights acquisition → ecosystem growth.' This model of 'data as a rights certificate' gives abstract data direct practical value, significantly enhancing the motivation for user and project participation.
3. Association-profit linkage: Allowing hidden data associations to bring 'excess profit opportunities'
The deep value of on-chain data lies in 'the seemingly unrelated indicator associations' — the 'interaction data between holders of a certain NFT and a certain DeFi protocol,' the 'fund flow of a certain token and the ecological progress of a certain public chain,' these hidden associations often indicate excess returns, but traditional tools find it difficult to uncover. Bubblemaps' 'association profit mining engine' uses AI to identify hidden relationships between data and link them to specific excess profit opportunities, allowing users to grasp 'values that others cannot see.'
In cross-industry profit scenarios, the engine's capability stands out:
• Identifying that 'the core holder of a certain NFT series has recently frequently interacted with a new DeFi protocol,' immediately prompting 'hidden profit opportunity: this NFT may be linked to activities launched by the DeFi protocol (like holding the NFT may yield mining bonuses), recommended to pay attention to protocol announcements and prepare to invest in the NFT or protocol token.' A user purchased the DeFi protocol token in advance based on this, and a week later the protocol indeed launched an NFT-linked activity, with the token price rising by 80%;
• Discovering 'the flow of ecosystem subsidy funds from a certain public chain to a niche DApp, and the user retention rate of that DApp exceeds 60%,' marking 'hidden profit opportunity: strong support from the public chain + high user stickiness, the DApp token may have room for valuation correction, suitable for small allocations.' An investor followed up, and the DApp token rose from $0.1 to $0.8 within two months, yielding sevenfold returns.
In risk hedging profit scenarios, association mining is equally effective:
• The engine identifies 'the large outflows from the top addresses of a certain DeFi token' are time-related to 'the cross-chain inflows of a certain stablecoin,' prompting 'hidden hedging opportunity: there may be a token sell-off leading to price drops, it is advisable to buy the corresponding stablecoin or short the token to hedge risks.' A quantitative trader acted on this, gaining a 25% hedging profit amidst a 30% drop in the token price. This ability to 'uncover hidden connections → link to excess returns' upgraded on-chain data from 'decision reference' to 'source of profit,' significantly enhancing the practical value of the data.
Summary
From 'data-scenario value binding' linking indicators to specific profits, to 'indicator-rights transformation' allowing data to exchange for practical rights, and then to 'association-profit linkage' bringing excess opportunities, Bubblemaps' core value lies in 'transforming abstract on-chain data in Web3 into tangible, perceptible, and monetizable practical assets.' It is no longer a 'tool for displaying data' but a 'value realization partner' for users and project parties — allowing users to earn profits through data insights, enabling project parties to achieve growth through data optimization, and allowing institutions to seize opportunities through data exploration. With the enrichment of value transformation scenarios and the upgrade of association mining algorithms, Bubblemaps is expected to become the core platform for 'data value realization' in Web3, promoting the industry from 'data transparency' to 'data appreciation,' letting every piece of on-chain data unleash practical forces driving ecosystem development in the visualization of value.
