In the Web3 data ecosystem, there exists a 'feedback blind spot': after personal users authorize data, they do not know 'whether their actions can maximize returns', and adjusting direction relies solely on guesswork; a small three-person team's Web3 data tool project, after launching, only sees 'user numbers increasing', but is unclear about 'which features users like and which need optimization', leaving iterations without direction; offline community clothing stores conducting Web3 activities only know 'customer flow did not meet standards', without being able to determine 'whether it's a problem with the activity rules or ineffective promotion', making targeted improvements impossible — 'lack of precise feedback and optimization without basis' leads small roles to 'blindly trial and error' in operations, wasting time and resources. As a decentralized data infrastructure, Chainbase is building a 'data effect feedback station' with 'data feedback + optimization suggestions', allowing individuals to receive operational guidance, small projects to gain iterative directions, and small stores to have improvement plans, addressing the pain point of 'feedback absence'.
Its core capability is to break down 'vague results' into 'optimizable details', rather than letting small roles fall into confusion over 'poor results'. On a technical level, Chainbase has built a 'feedback analysis hub': for individual users, the hub launched an 'operation effect dashboard' — displaying in real-time 'earning conditions after data authorization' (such as 'earn 50 C by authorizing Ethereum data, earn 30 C by authorizing Polygon data'), and also marking 'optimization suggestions' (such as 'you have low frequency of authorizing Polygon data, increasing authorization could earn an additional 20% C', 'you have high efficiency in completing certain tasks, suggested to prioritize those'). A Beijing user, Xiao Han, discovered through the dashboard that 'his earnings from Solana chain tasks were the highest', and after adjusting, he earned an additional 300 C monthly, saying, 'I used to do tasks blindly, now with feedback guidance, earning is much more systematic'; for small and medium projects, the hub developed 'user behavior analysis tools' — tracking 'every step users take in using tools' (such as '80% of users click on 'data filtering', but only 30% use 'export report''), generating 'function optimization reports' (such as 'export report operation is complicated, suggest simplifying button placement'), and also providing 'industry comparison data' (such as 'the usage rate of 'export function' for similar tools reaches 60%'). A four-person DeFi data tool team optimized the 'export report' function using the analysis tools, increasing its usage rate from 30% to 70%, with user retention growing by 40%; for offline small stores, the hub designed an 'activity effect diagnostic module' — breaking down issues from three dimensions: 'customer flow, redemption, member growth' (such as 'the failure to meet customer flow standards is due to 'low exposure from community promotion', and low redemption is due to 'unclear rule explanations'), and also providing 'improvement plan templates' (such as 'increase KOL forwarding in community promotion, post simplified process posters at redemption points'). A clothing store in a Shanghai community identified through diagnosis that 'the event promotion only sent to one community', and by following the plan, added promotions to three more community groups, resulting in a 55% increase in customer flow within three days.
When the ecosystem is implemented, Chainbase does not engage in 'feedback piling', but focuses on providing precise guidance for 'optimization difficulties'. For individual users, it launched 'feedback reminder push' — when the system detects 'there is room for optimization in user operations' (such as 'only doing low-yield tasks for a long time'), it will push suggestions in real-time (such as 'you can try Solana chain's high-yield tasks, expected to earn an additional 15 C per day'), and currently, 5.35 million users have received feedback guidance, with personal earnings increasing by an average of 65%; for small and medium projects, it introduced 'optimization companionship services' — consultants help projects formulate 'iterative plans' based on feedback reports (such as 'optimize the export function this week, test new data dimensions next week'), and also provide 'user research support' (assisting in collecting user feedback on optimizations), a three-person GameFi data project completed three iterations of functions through companionship, increasing its user count from 5,000 to 20,000; for offline small stores, it initiated 'feedback improvement follow-ups' — after small stores adjust according to the plan, consultants follow up within a week to check 'improvement effects' (such as 'whether customer flow has increased, whether redemption is smooth'), and also help solve 'newly emerged issues', recently helping a children's clothing store in Hangzhou improve an event, and during the follow-up discovered 'no increase in member repurchases', and then supplemented a 'repurchase discount coupon' plan, resulting in a 30% increase in repurchase rate within a week. More thoughtfully, Chainbase also developed a 'feedback value calculator', where inputting 'current operation/function/activity data' can calculate 'expected additional earnings in C, increased customer flow/usage rate after optimization as per suggestions', allowing small roles to visually see the value of feedback.
In the long run, its value lies in 'using feedback to help the 'small roles' in the Web3 data ecosystem take fewer detours', preventing blind trial and error from consuming potential. Currently, Chainbase has 5.35 million individual users and 255,000 small and medium cooperative projects/stores optimizing operations through the feedback station, with 80% of individual users increasing their earnings through feedback, and 75% of small and medium projects clarifying their iterative directions through feedback, forming a positive cycle of 'viewing feedback - making optimizations - seeing results - further improving'. Recently, the project has also reached a cooperation with the Web3 user research platform UserTesting Web3 to enhance the precision of user behavior analysis, providing deeper feedback for projects, and is expected to add 4.25 million individual users; cooperating with offline merchant operation platforms to incorporate 'small store feedback diagnosis' into daily operational services, helping more traditional merchants optimize Web3 activities through data feedback. The 'feedback attributes' of C tokens are also being strengthened: individuals optimizing operations based on feedback can enjoy a 15% bonus on C earnings; projects using C to procure feedback analysis tools can enjoy a 30% discount; users/projects/stores staking C can receive priority for in-depth feedback reports, with optimization efficiency improving by 50%, which design has stabilized the $C staking rate at 99%, with daily trading volume increasing by 250%.
From helping users earn more by adjusting tasks based on feedback, to guiding small projects in determining iteration directions through analysis, and then assisting clothing stores in diagnosing customer flow, Chainbase is equipping the 'small roles' in Web3 data with 'optimization navigation' through the 'Effect Feedback Station'. As more small roles rely on feedback to avoid detours, this 'iterative' data platform may enable a new rhythm in the Web3 ecosystem of 'feedback can optimize, small roles can grow quickly', and truly transform the data ecosystem into one where 'every attempt yields rewards, and every optimization brings progress'.
