1. Overview

Every user’s deposit, withdrawal, trading and transfer behavior will form traceable records on the public chain. Massive scattered on-chain data contains core information such as user asset scale, trading preference, risk level and activity cycle. Most exchange platforms only view simple internal order data and ignore the huge operation value of on-chain address data, unable to carry out targeted refined user operation and market risk judgment.

SoonTech’s on-chain data analysis service integrates multi-chain address label library, user behavior portrait system and market capital flow tracking function, converting raw chain data into operable operation decision indicators to guide platforms to optimize activity strategies, liquidity layout and user retention plans.

2. Operation Blind Spots Caused by Ignoring On-Chain Data

2.1 Unable to accurately judge user asset strength

Only relying on platform internal balance cannot judge users’ total on-chain asset scale, missing high-value institutional and whale user identification opportunities.

2.2 Blind activity operation without user preference classification

Cannot distinguish spot traders, contract speculators and long-term holders, resulting in universal activity rewards with low conversion efficiency.

2.3 Lack of cross-platform capital flow monitoring

Unable to track large funds flowing into and out of the platform, missing early warning signals of large-scale user outflow and market capital shrinkage.

2.4 Difficult to identify black-industry batch associated accounts

Cannot correlate on-chain multiple address transfer relationships, unable to accurately label wash trading and money laundering high-risk user groups.

3. Core Functions of SoonTech On-Chain Data Analysis System

3.1 Multi-chain address intelligent label library

Accumulate massive address tags including whale addresses, institutional wallets, exchange hot wallets, black-industry money laundering addresses and project team wallets, automatically matching labels for all user incoming and outgoing chain addresses.

3.2 User multi-dimensional behavior portrait

Combine on-chain transfer records and platform internal trading data to generate user labels: high-frequency spot trader, high-leverage contract user, long-term asset holder, small retail investor and high-risk black-industry account, supporting differentiated targeted operation.

3.2 Real-time platform capital inflow and outflow tracking

Monitor large-value on-chain transfer records associated with platform wallet addresses, count daily net inflow and outflow of funds, send early warnings when continuous large capital outflow occurs to assist operation teams in timely retention intervention.

3.4 Associated account cluster identification

Trace multi-address mutual transfer relationships on the chain, identify batch controlled black-industry account groups and feed risk data to the platform’s internal blacklist risk control module for synchronous interception.

3.5 Visualized operation data dashboard

Convert chain data into intuitive charts of high-value user proportion, user trading preference distribution and capital flow trend, providing direct data basis for operation strategy adjustment.

4. Practical Operation Application Scenarios

1. Screen high-value whale and institutional users, launch exclusive high-value reward activities to lock core asset traffic.
2. Classify user trading preferences, push differentiated spot/contract/grid trading activity rewards to improve activity conversion rate.
3. Track cross-platform capital flow trends, adjust liquidity subsidies and market-making strategies in advance according to capital inflow and outflow changes.
4. Identify black-industry associated account groups, cooperate with internal risk control to purify the platform trading environment and reduce regulatory AML risks.

5. Conclusion

On-chain public data is an undervalued core operation asset of digital asset platforms. Single internal order data cannot support refined and data-driven operation decisions. SoonTech’s integrated on-chain data analysis service realizes address labeling, user portrait and capital flow full-track monitoring through multi-chain data mining, helping platforms transform massive raw chain data into actionable operation strategies and improve user operation efficiency and market risk early warning capability.