Layer3 Launches Signal: on-chain Real User Engagement Metrics

The Web3 space has always been keen on chasing "hype". Whether it's the sudden rise of a new protocol or an entire track becoming the focus of capital's pursuit, "everyone is talking about it" often becomes the reason for people to enter the market. However, the problem lies in the fact that volume does not equate to actual traction; truly valuable attention should not only exist in community discussions but should also be observable on-chain.

The truly valuable hype is not just about who is discussing it, but more importantly, how many people take actual action afterward—opening wallets, completing tasks, and interacting with protocols. This is precisely the original intention behind Layer3 launching Signal: an on-chain attention metric specifically designed for Web3, aimed at helping users identify which protocols are gaining real user engagement and action, rather than just remaining topics in chat rooms.

Signal: Redefining on-chain attention

In mid-July, Layer3 officially launched its new product Signal. This is not just a tool, but a way to rethink the Web3 world — it does not focus on who shouts the loudest, but rather on who truly attracts users to take action on-chain.

Signal claims to be the "on-chain correlation index", which aims to capture whether a protocol or project truly attracts on-chain participants. It does not base its criteria on who has the loudest voice or the most interactions, but rather focuses on: Did anyone take actual action on-chain? What specific tasks were completed? Are they willing to pay Gas fees for these interactions or provide a real identity?

For an already highly gamified on-chain world, this shift in perspective may be a necessary step.

Achievements Based on Three Years of Data Accumulation

The launch of Signal is not a result of nothingness, but is based on the vast amount of data accumulated by Layer3 over the past three years. As one of the most active on-chain task platforms currently, Layer3 has:

• Real interaction records of 3 million active users
• Over 200 million task completion behaviors
• 60 million verifiable credentials
• The tracking scope across more than 40 public chain ecosystems

These data are not just cold hard numbers, but also filled with semantic participation traces. For example, Signal can identify that even if a task has 10,000 completion records, if only 100 participants each repeat the operation 100 times, then this "popularity" is actually questionable.

This in-depth judgment was difficult to achieve in the past relying solely on community data.

Signal's Scoring Logic: Focus on Behavioral Authenticity

Signal is not just a simple ranking system; it is backed by a set of metrics that focus on "who did what and why they did it." Its main scoring logic comprehensively considers the following factors:

• The proportion of independently participating users
• Task conversion rate and depth of initiation behavior
• The complexity of the task or interaction (is it just a single click? Does it include on-chain signatures or transfers?)

In other words, if a protocol can attract a large number of real addresses to engage in meaningful actions (such as staking, voting, minting, etc.), then its Signal value will relatively increase.

This allows Signal to serve as a proxy indicator for product appeal and genuine traction, rather than a false prosperity under marketing campaigns.

The Difference Between Signal and Other "Hype" Tracking Tools

In recent years, many products have emerged in the market aimed at helping users grasp the "focus points of the crypto world," from InfoFi projects that focus on narrative integration to traditional on-chain data analysis tools. Although these tools seem to be engaged in "trend tracking," their starting points and handling methods are actually quite different.

The products in the InfoFi track mainly focus on the topics being discussed by the community and changes in public opinion. Their value proposition is: in the face of information overload, users need a more efficient system to organize discussions, news, and sentiment trends. These tools largely rely on AI, keyword, and source aggregation, belonging to the "narrative perspective editorial layer".

On-chain data analysis tools are centered around open queries. They provide a complete set of on-chain databases and query languages, allowing users to set their own conditions and extract the data they care about. These tools emphasize flexibility and transparency, but also impose higher technical requirements on users.

The starting point of Signal is that there are actually a large number of real participation behavior signals hidden on-chain, but there is a lack of effective organization and presentation. Signal does not require users to input complex query conditions, but directly tells you: how many people are actually participating in this protocol now, what interactions have been completed, and what proportion of overall attention they occupy. It is a real-time scoring system based on behavior, not just a simple data query tool or an information aggregator.

We can understand the division of labor among the three as follows:

• InfoFi products help you understand "what everyone is talking about"
• The data analysis tool helps you query "the specific data you want to know"
• Signal actively tells you "what is really happening right now"

There is no distinction of superiority or inferiority among these three types of tools; rather, they correspond to different information needs and decision-making scenarios in Web3. Users can first understand the current mainstream narratives through information integration platforms, then use data tools to validate the details, and finally return to Signal to determine whether these narratives truly have real appeal, specific actions, and developmental momentum.

Signal hopes to supplement what has long been overlooked beyond the noise and data charts: real on-chain traction.

Future Development: Predictive Features, API Interfaces, and Commercialization Models

Layer3 also admitted that Signal will not always offer a free version. In the coming months, they plan to launch a Premium Plan, which is expected to include the following features:

• Real-time trend forecasting and potential hot protocol alerts
• on-chain task and activity efficiency analysis
• API interface functionality for data teams and funds to establish custom strategies
• Custom dashboard and notification module

For a product that already has 1.1 million active users, such expansion is natural and also means that Layer3 views Signal as one of its core engines for the next phase of growth and commercialization.

Conclusion: Redefining the Metrics of Attention

The emergence of Signal is a correction to the past methods of assessing on-chain popularity. It is not intended to replace the volume models of social media and community platforms, but rather to provide a measurement standard that is closer to the essence of behavior.

While most people are focused on who is the most eye-catching, Layer3 chooses to observe who is truly taking action. This perspective may very well be the starting point for the next wave of crypto product design and research.