Opening Questions

As a newcomer to Web3 development, I faced a lot of confusion! I spent several sleepless nights creating an amazing DApp with a beautiful interface design and smooth functionality, but I just couldn't find users willing to try it out. It was truly frustrating. I've seen many fellow developers in various communities encountering similar issues: they create thoughtful products but struggle to find their "kindred spirits."

This situation is particularly common in the Web3 space. Look at blockchain projects today—they're as numerous as fish in the river, but how many actually survive? Many projects fail because they can't find users. Recently, I discovered an incredible tool that is a game-changer for us Web3 developers!

Understanding the Tool

Let me give you an analogy about this tool. Everyone has used Baidu Analytics, right? It's a tool that shows website traffic and user sources. This tool is like the Web3 version of "Baidu Analytics," but it does more than just provide statistics—it also integrates marketing capabilities similar to Alimama. It helps us find and attract target users by analyzing on-chain transaction data.

I have personal experience with this. Last year, I was involved in developing an NFT project. We spent a lot on social media advertising for promotion, but the results were disappointing. During our post-mortem, we realized we had no understanding of user needs and behavior patterns—we were essentially advertising blindly. If we had used this tool then, we wouldn't have been so reactive.

The blockchain world is incredibly competitive now. Having a good product isn't enough; you need to precisely identify your target users and understand their needs to maintain a competitive edge. This tool helps us tackle this challenge.

Core Features

This tool's most impressive aspects are its three core features. Let's discuss them one by one:

First, let's talk about on-chain behavior analysis. This feature is like installing a high-definition monitoring system on the blockchain. Want to know what operations a wallet address has been performing recently? You can find out instantly. For example, you can see how many tokens this address has swapped on Uniswap in the last 30 days, which NFT marketplaces they've traded on, which DeFi projects they've invested in, and how much they've invested—everything is crystal clear.

This level of transparency is hard to achieve in traditional internet domains. Traditional user behavior analysis often relies on cookies or user authorization, and the data might not be accurate. But on the blockchain, all transaction data is public, and this tool organizes this raw data into a format we can understand.

For instance, through this tool, we can see that a user has participated in 3 IDOs in the past month, traded on 5 different DEXes, and engaged in 2 liquidity mining projects. This data indicates that this user is interested in DeFi projects and has a certain risk tolerance.

The second important feature is user profiling. This feature is even more interesting—it's like a super detective that tags each user differently by analyzing various behavioral data. For example, tags like "DeFi player," "NFT collector," "conservative investor," or "high-risk enthusiast" are generated.

These tags aren't arbitrary; they're based on an analysis of large amounts of real data. The tool considers multiple dimensions of data including transaction frequency, single transaction amounts, types of projects participated in, holding time, etc. For instance, if a user frequently participates in early investments of new projects with large amounts, they might be tagged as an "aggressive investor."

This kind of user profiling is particularly helpful for product development and marketing. Knowing what type of user they are allows us to design product features and adjust marketing strategies accordingly. For example, for "conservative investors," we might need to provide more risk control features and detailed project analysis reports; for "aggressive investors," we might offer more high-yield but high-risk investment options.

The final core feature is marketing strategy optimization. With the previous data analysis and user profiling, the tool can also help us refine marketing strategies. It not only tells us which user groups to target but also when and through which channels to reach these users most effectively.

For example, if the data shows that your target user group is mainly from Asia and most active between 10 PM and 2 AM, this information is crucial—you'll know when to place your ads. Moreover, if the data indicates these users are primarily active on Discord and Telegram, then your community operations should focus on these platforms.

Practical Application

After all this theory, let's look at how this tool is used in actual projects.

I'm currently developing a DeFi lending platform, and through the tool's analysis, I discovered some very interesting data. First, 40% of our active users are regular Uniswap users who make at least 3 token swaps per week. Another 25% particularly enjoy using Aave, mainly for earning interest on deposits. The remaining 35% are active on multiple DeFi platforms, showing they're very familiar with the DeFi ecosystem.

This data provided many insights. First, our users are mostly DeFi veterans, meaning we don't need to include many basic tutorials in our product, but should focus on providing more advanced features. For example, we can add more detailed yield analysis tools or offer more complex lending strategy options.

Additionally, the data shows our users' average holding time exceeds 90 days, indicating they're not here for short-term trading but truly value long-term benefits. Based on this finding, we designed a special incentive mechanism: the longer the holding time, the more platform token rewards. After implementing this mechanism, user retention increased by nearly 30%.

The tool also helped us discover an interesting phenomenon: many users test with small amounts before making large loans. This led us to launch a "newcomer experience fund" campaign, allowing users to familiarize themselves with platform features using minimal amounts. This campaign achieved an impressive 45% conversion rate.

Advanced Usage

Speaking of advanced features, what impressed me most is the cross-chain data analysis capability. Today's Web3 world operates across multiple chains, and users often interact with different public chains. The tool can analyze user behavior across these different chains—this is incredibly powerful!

For example, through the analysis, we discovered an interesting user behavior pattern: many users primarily engage with NFTs on Ethereum due to its mature NFT ecosystem; however, on BSC, these same users are more involved in DeFi activities because of lower transaction fees. This discovery directly influenced our product layout strategy.

We decided to focus on NFT-collateralized lending features on Ethereum, allowing users to obtain liquidity using their NFTs; while on BSC, we focused on developing more diverse DeFi features. This differentiated layout strategy worked particularly well, with noticeable improvements in user activity on both chains.

The cross-chain analysis also helped us identify potential marketing opportunities. For instance, we found that a significant portion of users frequently transfer assets between different chains during major price fluctuations. Based on this discovery, we developed a cross-chain asset management tool to help users easily allocate funds between different chains. This feature quickly became one of users' favorite functions after launch.

Future Outlook

Honestly, I believe tools like this will become increasingly important. The Web3 world is developing rapidly, and user behavior patterns are becoming more complex. Without effective data analysis tools, developers simply can't keep up with changing user needs.

I predict that such tools will continue to evolve in several directions: first, data will become more real-time, allowing developers to discover changes in user behavior immediately; second, analysis dimensions will become richer, possibly incorporating off-chain data to provide more comprehensive user profiles; finally, AI capabilities will strengthen, automatically predicting user behavior trends and providing smarter marketing suggestions.

Moreover, as the Web3 ecosystem continues to expand, new public chains and application scenarios will constantly emerge, making user behavior more diverse. At this point, tools like this become even more essential. They not only help developers understand existing users but also uncover new market opportunities.

Friendly Reminder

However, after saying all this, I must remind everyone that no tool, no matter how powerful, is a substitute for understanding user needs. While it can tell you what users are doing on-chain, understanding why they're doing it and what their real needs are—these are aspects developers need to explore deeply themselves.

Also, suggestions from data analysis tools shouldn't be blindly followed. For example, if data shows users prefer high-yield products, this doesn't mean you should simply provide high-risk investment options. Fundamental issues like product safety and sustainability still need to be managed by developers themselves.

Overall, this tool is excellent for helping us better understand users and optimize products and marketing strategies. But it ultimately serves as an auxiliary tool—what's most crucial is whether the product itself truly addresses users' pain points and creates value for them. This is something all Web3 developers should always keep in mind.