This episode is content from Alex's personal YouTube channel, focusing on the recent popular social product Kaito, with an in-depth discussion of its product strategy, market background, and development logic. Alexon is the CIO of Ferryboat Research. By analyzing Kaito's choices on the Twitter platform and its characteristics in crypto social data collection, processing, and application, it explains the reasons for its high pricing and core advantages. In addition, it explores the direction of similar projects, pointing out how Kaito breaks through the limitations of traditional data services through API call optimization, KOL mapping, and social binding mechanisms, successfully completing its strategic transformation and establishing a unique market position. At the same time, it shares the entrepreneurial experience and insights of industry practitioners, directly pointing to the challenges and opportunities faced in the process of Web3 productization and commercialization.
Crypto Traffic Acquisition Methods: Differences Between Advertising and Viral Models
Crypto is a highly volatile, high-risk field with strong financial attributes. You may find opportunities, but you also need to be prepared for the possibility of a complete loss of your principal. Next, let's talk about the first part: why Kaito and similar products choose Twitter as their main battlefield.
First, from the perspective of the consumer goods industry, the traffic structure is generally divided into two categories: public domain traffic and private domain traffic. In terms of traffic acquisition methods, there are two main paths: advertising and viral. Public domain traffic usually includes Twitter and YouTube, while in the crypto industry, Telegram and Discord belong to private domain traffic. In comparison, private domain traffic is more difficult to track and has a more singular structure.
Although platforms like Reddit, Instagram, and TikTok are gradually involved in the crypto industry, Twitter and YouTube still have the highest traffic concentration. If we look at the domestic environment, promotion may need to rely on platforms like Xiaohongshu, Douyin, and Kuaishou, as well as seeding platforms like Bilibili, and then guide the traffic to private domains like WeChat for conversion and repurchase.
In general, the traffic acquisition methods in the Crypto industry are relatively simple, as the advertising logic cannot currently support sufficient efficiency. This leads to a relatively singular traffic acquisition approach, mainly focusing on viral and distribution.
Comparison of User Acquisition Costs and Viral Effects in Different Regions
More than two years ago, when we were developing our own tool products, we tried an advertising strategy. I invested a few thousand dollars for testing, and although I can't disclose the specific data, a very obvious result was that the cost of acquiring a US user was about ten times that of acquiring a Vietnamese user. However, the viral rate of Vietnamese users was significantly higher than that of US users. This suggests that US users are less inclined to actively participate in viral promotion, such as creating and sharing landing pages.
In the entire crypto industry, I believe there are only two ways to acquire traffic: distribution and viral. Although these two methods are essentially both forms of virality, their application logic is different. Distribution tends to rely on KOLs (key opinion leaders) or KOCs (key opinion consumers) for promotion, where you entrust the product to them for endorsement and then have them distribute it to the general public or retail users.
Viral, on the other hand, is about designing an efficient viral mechanism to create activities that attract user participation. For example, Kaito's Yap activity is a typical case. Users share data from their Crypto Twitter (CT) accounts, such as the number of "smart followers", forming a similar experience to Netease Cloud's annual song list or consumption statement. Essentially, the purpose of these mechanisms is to achieve virality through user-initiated sharing to obtain more traffic.
After explaining these background knowledge, you can also understand why we chose Twitter as the main platform instead of private domains. The biggest problem with private domains is that it is difficult to standardize the acquisition of all content, and the content within private domains is difficult to effectively evaluate. For example, if a community is entirely focused on discussing Kaito, you cannot accurately assess the real value and influence of this data. At the same time, the decentralized nature of private platform makes it very difficult to comprehensively obtain relevant data. This is not a priority choice.
Why Kaito Chose Twitter as the Main Platform
On public domain platforms like YouTube, content is usually more suitable for presentation in the form of long videos. For example, it can be a single-camera video like the one I'm recording now, an interview format, or content focused on tutorials and interactions, or even some mining machine operation guides. Such content often requires long production and viewing time, suitable for topics that need detailed explanation and learning. Therefore, this type of content carrier is not essentially suitable for scenarios driven by immediate events or hot topics.
These long-form video content is usually more suitable for handling PoW-related topics. So although we've also tried to introduce Kaito's monitoring and analysis logic on YouTube and Farcaster, we ultimately found that the targets we can effectively observe are usually projects like Kaspa and Helium, while for some short-term viral meme tokens, the performance is completely unsatisfactory.
In comparison, Twitter is naturally suitable as a data platform, especially in an environment with a high concentration of social data. Almost all marketing budgets are concentrated on Twitter, forming a relatively high consensus. At the same time, Twitter's social graph is also highly transparent, with your follow list, engagement, etc. data presented in an explicit form. On platforms like YouTube, it is very difficult to obtain clear fan relationships or interaction details.
Ultimately, the reason for choosing Twitter as the main platform is that it is the optimal solution. Its transparent social graph and centralized traffic structure provide us with clear advantages. Compared to platforms like YouTube, obtaining similar relationship network data is very difficult or even impossible. Therefore, both us and Kaito tend to prioritize Twitter as the main battlefield.
Two Main Reasons for Kaito's High Pricing: API Costs and Regulatory Restrictions
We used some "tricks" at the time, when Twitter had not yet been acquired by Musk, and there were some gray areas in the system. For example, using educational accounts or other means to obtain data, although not entirely compliant, this was a common practice in the early stage. For early-stage projects like Kaito, I guess they also adopted similar strategies to obtain data through these informal channels. However, when the product started to commercialize, this approach was obviously no longer usable.
When they completed their financing and launched the product two years ago, they could only rely on commercial APIs, and after Musk's acquisition of Twitter, many non-standard channels were also blocked. The usage cost of commercial APIs is quite high, and as the number of calls increases, this cost will grow linearly rather than decrease.
The second reason for the high pricing is Twitter's regulatory restrictions. Even for a company using commercial APIs, there is a monthly call volume limit (I don't remember the exact number). This means that if the product is particularly popular, the call volume limit will make the ToC (consumer-facing) model difficult to sustain. In the end, both us and Kaito chose the ToB (enterprise-facing) model at a similar time, which is the best solution to maximize the economic value of the limited call volume. For Kaito, this is almost the only available direction.
Specifically, due to the fixed call volume, the only way is to increase the value of each user to achieve a greater economic return, in other words, to raise the price. This is the necessary choice of the product, otherwise the entire business model cannot be established.
I understand that their delay is around 15 minutes, similar to ours. It needs to be understood that the shorter the delay, the higher the cost required. This is because it requires scanning historical data at a higher frequency, and the growth of this cost is exponential. The setting of the delay time also directly affects the efficiency and economic feasibility of API calls. In summary, Kaito's high pricing under API call costs and regulatory restrictions is reasonable.
The Evolution and Selection of Kaito's Product Direction
Next, let's talk about Kaito's product direction and why they have evolved from a "trending" type of product to the current KOL-type functionality. Here's a brief conclusion first - it's not about teaching others how to start a business, but sharing our own experience. We've tried multiple directions and found three directions that can be derived based on this logic.
The first direction is a pure Alpha tool for self-use. The CEO of Kaito mentioned in a Podcast that they had also considered this direction. If the tool is only used for Alpha-type purposes, the more it is developed, the more it tends to be for internal use, and not suitable for large-scale users. We have also encountered similar problems - if there is no charge, users may not cherish it; if there is a charge, why not use it directly ourselves? These issues make Alpha tools generally more suitable for self-use rather than productization.
We have developed a set of tools using similar logic to Kaito. The application of this set of tools often allows us to discover projects before they become popular. We have considered using this logic to provide a listing tool for exchanges. For example, I once wanted to collaborate with Binance to provide this tool for free to optimize their listing selection criteria. Because for some projects, such as ACT, they did not show any performance worth paying attention to in our "God's-eye view" based on Twitter data analysis, but they were still listed on the exchange. This unreasonable selection could have been avoided through a data-driven tool.
In addition, we have also studied the application of Alpha logic to quantitative trading strategies. We tested the top 200 or top 100 projects on Badcase, making trading decisions based on text mining, sentiment analysis, etc. The test results show that this strategy is more effective for projects with smaller market capitalization and more susceptible to sentiment and event-driven, while the effect is limited for projects with larger market capitalization. I believe Kaito has also conducted similar research, after all, their CEO has a trading background. From this perspective, we and Kaito have many similarities in the early starting point and logic, but the paths we ultimately choose are not entirely the same.
Kaito's Exploration of Community News Tools and Its Industry Potential
Under the current model framework, some phenomenal topics, such as meme and NFT, are very prominent. They have the potential to show price increases in this logic. However, these phenomena cannot be fully solved by standardized programmatic trading, as they still require strong human intervention. This feature makes them effective, but lacking in standardization. As for whether Kaito has similar products internally and uses them for itself, I'm not sure about that.
The second direction worth exploring is news-type and GPT-type products. What does this mean? For example, like the current Alva (formerly Galxe), a Web3 assistant, by integrating Twitter's intraday data, it can obtain the corpus of all tweets and process them in combination with the ChatGPT interface. By adjusting the prompt on the front end, these data can be output in a more intuitive form, thereby generating many real-time community news.
For example, when you see the dispute over the capitalization of "elisa", you may be at a loss. At this time, you can directly ask this tool: "What is the reason for the dispute over the capitalization of 'elisa'? Who initiated it?" In this way, the tool will summarize the answer based on the latest data. The original GPT cannot do this, because its data has a fixed cutoff date and usually cannot provide content from the last six months. You can only crawl the relevant corpus yourself and feed it to GPT, and then summarize the logic through prompts. The potential of such tools is huge, and it is a direction worth exploring in depth.
From the current situation, Kaito seems to have already explored such products or tried similar directions. The Alva product I mentioned is a good example. It integrates a large amount of industry data by calling APIs related to the crypto field, and connects users with industry information point-to-point. However, the problem with Alva is that the quality of data cleaning is not high enough. They spent a lot of time connecting the data network, but there is still room for improvement in the accuracy and meticulous degree of data cleaning. In comparison, Kaito's advantage lies in the accuracy of its data, which is undisputed.
For example, regarding the recent dispute over the capitalization of "elisa", I was able to get a quick answer through such tools. The application of such products in the crypto industry can indeed significantly improve efficiency. More than two years ago, we also developed similar tools, and the test results showed that it could indeed improve work efficiency. However, when we tried to commercialize it, the core problem we encountered was that users' willingness to pay was not strong enough. Although the tool can improve efficiency, it did not target a core pain point, which led to a lack of strong purchase motivation for users.
In addition, the calling cost of such tools is relatively high (each call to the GPT interface requires a fee), which leads to a low gross profit margin of the product. Therefore, although such tools have certain significance, their commercialization faces great challenges. Many call behaviors are more for the purpose of activation, and the scenarios that actually generate revenue are limited, which have become problems that need to be overcome. In general, although this direction has huge potential, more optimization and breakthroughs are still needed in actual implementation.
The Role of Data Accuracy and KOL Mapping in Marketing
When discussing these tools, there is a core question: how do they achieve revenue? If it relies solely on a VIP model, allowing users to call the API an unlimited number of times, it will be difficult for such a product to have a large profit margin, but its existence is meaningful. It can directly use Kaito's logic, read Twitter data, and use it to generate and distribute self-media content, such as "Wu Blockchain" or other forms of community news. Such tools can not only improve efficiency, but also help project parties distribute content on multiple platforms, such as using AI to generate short videos and post them on TikTok, or directly post on Twitter.
I believe that this product direction is not only something that Kaito or Galxe can try, but projects like Mask are also very suitable for doing this. Strangely, Mask does not seem to have delved deeply into this direction at the moment. If there are teammates from the Mask team who hear these suggestions, I hope you can consider trying it.
For Kaito, their current product direction has already shown that they hope to move towards a larger market capitalization, rather than continuing to move forward along the path of Alpha tools. Although Alpha tools can be profitable, they lack the potential for productization. If they only focus on this, it will eventually be limited to internal use and unable to form products facing a larger market. Kaito's shift to KOL mapping construction is obviously to break through this bottleneck.
The early users interested in Kaito's products were almost the same as the user group that was interested in our tools at the time. Our tools were also suggested to be sold to some trading companies or secondary funds in the early days. Although these trading companies are more focused on profitability, this direction will get stuck in the loop of "whether to be profitable". In contrast, the KOL map provides accurate support for marketing placement, and improves the placement effect by improving data accuracy, thereby increasing the marketing value of the project party.
Data accuracy is the key. Although there are many companies on the market that can collect Twitter data, data accuracy is another matter. In the open market, Kaito and our early tools are among the few that can achieve accuracy. The core of data accuracy lies in "data washing", which is the most difficult and critical step. Collecting data is relatively simple, but weighting and cleaning the data requires a lot of repeated testing and logic adjustment, which often requires a combination of experience and intuition.
For example, the Crypto Twitter (CT) community in Chinese often has more noise, and the weight needs to be reduced. This noise causes the Chinese CT to usually lag behind the English CT by 24 to 48 hours. How to effectively clean and adjust the data is a "family skill", and is also the core competitiveness of the company.
Through an accurate KOL map, Kaito can help project parties optimize their placement strategies and improve the accuracy of placement. Such products can not only help project parties achieve more efficient marketing, but also obtain marketing fees, forming a sustainable business model. Choosing this direction is a smart strategy that Kaito has shown in market competition.
The Strategic Logic and Flywheel Effect Behind the Yap Event
In the entire Crypto field, advertising has always been a relatively vague and inefficient behavior. The current marketing agencies are essentially just simple tools for maintaining address books, and their methods are relatively single. Against this background, the tools provided by Kaito can help project parties judge which KOLs are worth placing, and which are not, and provide evidence-based references through data analysis. This accuracy greatly improves the efficiency of advertising.
Kaito optimizes KOL placement based on two key indicators: accuracy and core circle. Accuracy refers to whether the KOL's judgment is accurate, such as whether they have discussed a project before it rises, rather than participating after the project rises. Each time a KOL shares or promotes, their judgment accuracy will be recorded and weighted, affecting their weight score. All of this can be repeatedly verified through timestamps and data analysis tools.
The core circle (referred to as "smart follower" in Kaito) measures the depth of a KOL's influence. If an account has more smart accounts (i.e., smart followers) interacting with it, its weight score will be higher. This can help project parties screen out truly influential KOLs, rather than just accounts with a large number of followers.
Kaito's Yap activity demonstrated the success of its strategic transformation. This activity significantly reduced marketing costs by leveraging free KOL leverage. Traditional marketing requires contacting KOLs individually and paying high fees, while Kaito directly opened a page and provided distribution rewards to KOLs through a weight algorithm. This method simplifies the process and enhances credibility through data transparency. This model encourages many KOLs to voluntarily participate in promotion, helping the project spread rapidly.
At the same time, the Yap activity also solved potential risk issues. Considering that if Twitter changes its API rules in the future, Kaito used the TGE method to allow all CT users to bind their accounts to its backend and actively authorize data usage. This approach gradually frees Kaito from dependence on the Twitter API and begins to control its own data assets. This not only gives Kaito greater independence, but also forms a positive feedback loop between supply and demand: as more CT users bind, the interest of project parties increases, forming a flywheel effect of data matching.
Ultimately, Kaito created a commercial imagination similar to Alibaba or Juliangine through this model, becoming a successful marketing ecosystem platform in the crypto industry. Currently, this strategy has been executed quite successfully.
Entrepreneurial Reflection: How Non-Typical Elite Practitioners Can Break Through
If all CT (Crypto Twitter) users bind their accounts to Kaito's backend, then in the future when entering the secondary market, Kaito can clearly tell the outside world: "These data are mine." Whether it is the project party or the CT user, this binding behavior can form data consensus and trends. This is the core logic behind the Yap activity.
Before concluding the Kaito topic, I would like to share a small story about ourselves. Before Kaito's financing, we had also developed similar products, and it can even be said that we were doing it at the same time. More than two years ago, we tried both the Alpha tool and the GPT-type tool direction. At that time, the industry was in a downturn, our team was not very good at socializing, and we knew very few people in the industry. Although our products were interesting and had potential, there were few friends of VC who were willing to introduce us.
At that time, we contacted four VCs, one of whom was willing to co-invest, but we needed to find a lead investor. The other three directly ignored us, one of the reasons being that our background did not fit the typical image of an elite entrepreneur. They did not delve into the logic behind our products, nor did they try to imagine their potential value, but simply rejected us outright.
Later, we gradually gained attention from more industry insiders through platforms like YouTube. These viewers were mostly institutions and practitioners in the industry. Even so, I still haven't mentioned the past to those VCs I've contacted before, as it's a bit embarrassing. Interestingly, I later saw on the timeline that the VC employees I had contacted were now full of praise for Kaito, which made me very emotional.
We ultimately chose to go the Alpha tool route, which was related to the limited social circle we had at the time. We felt that without external help, it would be difficult to successfully commercialize a ToB product. We hoped to gain the recognition of well-known VCs and leverage their resources to complete market expansion, rather than just relying on our own difficult journey.
For those entrepreneurs with non-typical elite backgrounds, I have some suggestions. VCs are more concerned with connections and social networks, not necessarily the product itself. However, I still believe that a good product can speak for itself. If your product is truly good, don't be afraid to showcase it. Nowadays, I also realize the importance of building social influence. Through social networks, you can not only meet more people, but also accumulate a certain degree of reputation and trust for future entrepreneurship.
For friends who have watched my videos or browsed my Twitter, I hope the belief I convey is: regardless of whether you have an elite background, as long as your product is good enough, I am willing to help you. Good products and ideas are more important than impressive resumes. As long as what you present can be recognized by me, I will do my best to help you find resources.
