Compiled by Wu Talks about Blockchain
This issue is the content of Alex's personal YouTube channel. It revolves around the recent popular social product Kaito, and deeply explores its product strategy, market background and development logic. Alexon is the CIO of Ferryboat Research. By analyzing Kaito's choice of the Twitter platform and its characteristics in the collection, processing and application of encrypted social data, the reasons for its high pricing and its core advantages are explained. In addition, by comparing the direction of exploration of similar projects, it points out how Kaito breaks through the limitations of traditional data services through API call optimization, KOL map construction and social binding mechanism, successfully completes strategic transformation and establishes a unique market position. At the same time, the entrepreneurial experience and insights of practitioners in related industries are shared, pointing directly to the challenges and opportunities faced in the process of Web3 productization and commercialization.
Crypto traffic acquisition methods: the difference between delivery and fission mode
Crypto is a highly volatile, high-risk field with strong financial attributes. You may find opportunities in it, or you may need to be mentally prepared for your capital to be completely zeroed out. Next, let's talk about the first part: why Kaito and similar products choose Twitter as their main platform.
First, from the perspective of the consumer goods industry, traffic structure is generally divided into two categories: public domain traffic and private domain traffic. In terms of the way to obtain traffic, there are two main paths: delivery and fission. Public domain traffic usually includes Twitter and YouTube, and in the crypto industry, Telegram and Discord belong to private domain traffic. In contrast, private domain traffic is more difficult to track and has a simpler structure.
Although there are also platforms such as Reddit, Instagram, and TikTok that are gradually involved in the crypto industry, at present, Twitter and YouTube still have the highest traffic concentration. If it is placed in the domestic environment, it may be necessary to promote with the help of Xiaohongshu, Douyin, and Kuaishou, as well as Bilibili and other grass-planting platforms, and finally promote on the site through the through train or Wanxiang TV. After that, the traffic will be directed to private domains such as WeChat for conversion and repurchase.
In general, the way to acquire traffic in the Crypto industry is relatively simple, because the delivery logic cannot carry sufficient efficiency at the current stage of the industry. This leads to a relatively simple way to acquire traffic in the entire ecosystem, mainly focusing on fission and distribution.
Comparison of user acquisition costs and fission effects in different regions
More than two years ago, when we were developing our own tool products, we tried a delivery strategy. I invested tens of thousands of dollars in testing. Although the specific data is not convenient to disclose, an obvious result is that the cost of acquiring an American user is about ten times that of acquiring a Vietnamese user. However, the fission rate of Vietnamese users is significantly higher than that of American users. This shows that American users are less inclined to actively participate in fission promotion. For example, there are relatively few actions to create and spread a landing page.
In the entire crypto industry, I think there are only two ways to get traffic: distribution and fission. Although both methods are essentially a form of fission, their application logic is different. Distribution tends to rely on KOL (key opinion leaders) or KOC (key opinion consumers) for promotion. You give your product to them for endorsement, and then they distribute it to retail investors or retail users.
Fission is to design an efficient fission mechanism to create a set of activities that attract users to actively participate. For example, Kaito's Yap event is a typical case. Users share a piece of their Crypto Twitter (CT) account data, such as showing how many "smart followers" they have, to form a gameplay similar to NetEase Cloud's annual playlist or consumption bill. In essence, the purpose of these mechanisms is to achieve fission through spontaneous sharing by users, thereby obtaining more traffic.
After explaining this background knowledge, you can understand why we chose Twitter as the main platform instead of the private domain. The biggest problem with the private domain is that it is difficult to obtain all content in a standardized manner, and it is difficult to effectively weight the content in the private domain. For example, if a community is all discussing Kaito, you cannot accurately evaluate the true value and influence of this data. At the same time, the decentralization of private domain platforms also makes it very difficult to fully obtain relevant data. For this reason, this is not a priority choice.
Why Kaito chose Twitter as its main platform
On public platforms like YouTube, content is usually best presented in the form of long videos. For example, it can be a stand-up video like the one I’m recording now, an interview format, or content that focuses more on tutorials and interactions, or even some mining machine operation guides. Such content often requires a long time to produce and watch, and is suitable for topics that require detailed explanations and learning. Therefore, this type of content carrier is inherently not suitable for scenarios driven by immediate events or hot topics.
These long video contents are usually more suitable for dealing with PoW (proof of work) related topics. So although we also tried to introduce Kaito's monitoring and analysis logic on YouTube and Farcaster, we finally found that the targets that can be effectively observed are usually projects like Kaspa and Helium, while for some short-term popular meme tokens, the performance is completely unsatisfactory.
In contrast, Twitter is naturally suited to be used as a data platform, especially in an environment where social data is highly concentrated. Almost everyone's marketing budget is concentrated on Twitter, forming a high consensus. At the same time, Twitter's social graph is also very transparent, such as your follow list, number of interactions (engagement) and other data are presented in an explicit form. On platforms like YouTube, it is difficult to obtain clear fan relationships or interaction details.
Ultimately, the reason for choosing Twitter as the primary platform is that it is the optimal solution. Its transparent social graph and centralized traffic structure provide us with clear advantages. In contrast, it is very difficult or even impossible to obtain similar network data on platforms like YouTube. Therefore, both we and Kaito prefer to prioritize Twitter as the primary position.
Two reasons for Kaito's high pricing: API costs and regulatory restrictions
We used some "tricks" at the time. At that time, Twitter had not yet been acquired by Musk, and there were some gray areas in the system. For example, using educational accounts or other methods to obtain data, although not completely compliant, but in the early stages, this method was common. For early projects like Kaito, I guess they initially adopted a similar strategy to obtain data through these informal channels. However, when the product began to be commercialized, this method was obviously no longer usable.
Two years ago, when they completed financing and launched their products, they could only rely on commercial APIs, and after Musk acquired Twitter, he also blocked many non-standard channels. The cost of using commercial APIs is quite high, and as the number of calls increases, this cost will increase linearly, rather than decrease.
The second reason for the high pricing is Twitter's regulatory restrictions. Even if a company uses the commercial API, there is a cap on the number of calls per month (I can't remember the exact number). This means that if the product is particularly popular, the limit on the number of calls will make the ToC (towards consumer) model unsustainable. In the end, we and Kaito both chose the ToB (towards business) model at a similar time, which is the best option to maximize the economic value of limited calls. For Kaito, this is almost no other choice.
Specifically, since the call volume is fixed, the only way is to achieve greater economic returns by increasing the value of individual users, which is generally speaking to increase prices. This is precisely the necessary choice for the product, otherwise the entire business model cannot be established.
I learned that their latency is about 15 minutes, which is similar to ours. It is important to understand that the shorter the latency, the higher the cost. This is because historical data needs to be scanned at a higher frequency, and this cost increases exponentially. The setting of latency also directly affects the efficiency and economic feasibility of API calls. In short, Kaito's high pricing is reasonable under the API call costs and regulatory restrictions.
Kaito's product direction evolution and selection
Next, let's talk about Kaito's product direction and why they have evolved from "trending" type products to the current KOL type functions. Here is a small conclusion - it is not to teach others how to start a business, but to share our own experience. We have tried many directions and found that there are three directions that can be derived based on this set of logic.
The first direction is a pure Alpha tool for personal use. Kaito's CEO mentioned in a podcast that they had considered this direction. If the tool is only used for Alpha-type purposes, the more it is developed, the more it tends to be used internally, and it is not suitable for large-scale users. We have also encountered similar problems - if it is free, users may not value it; if it is charged, why not just use it yourself? Such problems make Alpha tools usually more suitable for personal use rather than productization.
We have developed a set of tools ourselves using a similar logic to Kaito. The application of this set of tools often enables us to discover projects before they become popular. We have considered using this logic to make listing tools for exchanges. For example, I once wanted to work with Binance to provide this set of tools for free to optimize their listing selection criteria. Because some projects, such as ACT, do not show any noteworthy performance in our "God's perspective" based on Twitter data analysis, but are still listed on the exchange. This unreasonable choice could have been avoided with data-driven tools.
In addition, we have also looked at applying Alpha logic to quantitative trading strategies. We test the top 200 or top 100 projects on Badcase and make trading decisions based on text mining, sentiment analysis, etc. Test results show that this strategy is more significantly effective for projects with smaller market capitalizations that are easily driven by emotions and events, while it has limited effect for projects with larger market capitalizations. I believe Kaito has done similar research, after all their CEO has a trading background. From this point of view, we and Kaito have many similarities in our early starting points and logic, but the paths we ultimately choose are different.
Kaito's exploration of community journalism tools and their industry potential
Under the current model framework, some phenomenal topics, such as memes and NFTs, are very significant. They can show the potential of price improvement in this set of logic. However, such phenomena cannot be completely solved by standardized programmatic transactions, because they still require strong manual intervention. This characteristic makes them effective but lacks standardization. As for whether Kaito has products in a similar direction and uses them for itself, I don't know.
The second direction worth exploring is news and GPT products. What does this mean? For example, a Web3 assistant like Alva (formerly Galxe) can obtain the corpus of all tweets by integrating Twitter's time-sharing data, and process them in combination with the ChatGPT interface. By adjusting the prompt on the front end, this data can be output in a more intuitive form, thereby generating a lot of instant community news.
Let's take a simple example: For example, you may be confused when you see the dispute over the uppercase and lowercase letters of "elisa". At this time, you can directly ask the tool: "What is the reason for the dispute over the uppercase and lowercase letters 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 deadline and usually cannot provide the latest content within the past six months. You can only crawl the relevant corpus yourself and feed it to GPT, and then summarize the logic through prompts. This type of tool has great potential and is a direction worth exploring in depth.
From the current point of view, Kaito seems to have been exploring this type of product or trying a similar direction. The Alva product I mentioned is a good example. It integrates a large amount of industry data by calling APIs related to the encryption field such as Rootdata, connecting 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 to the data network, but there is still room for improvement in data accuracy and the meticulousness of cleaning. In contrast, Kaito's advantage lies in the accuracy of its data, which is beyond doubt.
As a practical example, I used this type of tool to get a quick answer to a recent question about the debate over the capitalization of “elisa.” The application of such products in the encryption industry can indeed significantly improve efficiency. We developed a similar tool more than two years ago, and test results show that it can indeed improve work efficiency. However, when we tried to commercialize, the core problem we encountered was that users’ willingness to pay was not strong enough. Although the tool can improve efficiency, it does not address a core pain point, which makes users lack a strong motivation to purchase.
In addition, due to the high cost of calling such tools (each time you call the GPT interface, you need to pay a fee), the product gross profit margin is relatively low. Therefore, although such tools have certain significance, their commercialization faces great challenges. Many calling behaviors are more for the purpose of activation, and the actual scenarios for generating revenue are limited. These have become difficulties that need to be overcome. In general, although this direction has great potential, it still needs more optimization and breakthroughs 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 the VIP model alone allows users to call the API unlimited times, it is difficult for such a product to have a large profit margin, but its existence is meaningful. It can directly use Kaito's logic to read Twitter data to generate and distribute self-media content, such as "Wu Says" 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 generating short videos through AI and posting them on TikTok, or directly on Twitter.
I think Kaito and Galxe are not the only companies that can try this product direction. Projects like Mask are also very suitable for this. The strange thing is that Mask does not seem to be deeply involved in this direction at present. If any friends on the Mask team hear these suggestions, I hope you can try to consider them.
For Kaito, its current product direction has shown that they want to move towards a larger market value rather than continue along the route of Alpha tools. Although Alpha tools can be profitable, they lack the potential for productization. If they only focus on this, they will eventually be limited to internal use and cannot form products for the larger market. Kaito is obviously trying to break through this bottleneck by turning to KOL graph construction.
The users who were interested in Kaito products in the early days were almost the same as the user groups who were interested in our tools at the time. Our tools were also recommended to be sold to some trading companies or secondary funds in the early days. Although these trading companies are more concerned about profitability, this direction will fall into the cycle of "whether it is profitable or not". In contrast, the KOL map provides precise support for marketing delivery, improves the delivery effect through data accuracy, and thus increases the marketing value of the project party.
Data accuracy is key. Although there are many companies on the market that can collect Twitter data, whether the data is accurate is another matter. Kaito and our early tools are among the few that can be accurate in the open market. The core of data accuracy lies in "cleaning data", which is the most difficult and critical link. Collecting data is relatively simple, but weighting and cleaning data requires a lot of repeated testing and logical adjustments, which often requires a combination of experience and intuition.
For example, the Chinese community’s Crypto Twitter (CT) often has a lot of 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 data is a "housekeeping skill" and the core competitiveness of the company.
Through accurate KOL maps, Kaito can help project owners optimize their delivery strategies and improve the accuracy of delivery. This product can not only help project owners achieve more efficient marketing, but also earn marketing fees from it and form a sustainable business model. Choosing this direction is exactly the smart strategy Kaito has demonstrated in the market competition.
The strategic logic and flywheel effect behind Yap’s activities
In the entire Crypto field, advertising has always been a relatively vague and inefficient behavior. The current marketing agencies are essentially more like simple tools for maintaining address books, and their means are relatively simple. In this context, the tools provided by Kaito can help project parties determine which KOLs are worth advertising and which are not, and provide a reliable reference through data analysis. This accuracy greatly improves the efficiency of advertising.
Kaito optimizes KOL placement through two key indicators: correctness and core circle. Correctness refers to whether the KOL's judgment is accurate, such as whether they have discussed a project before it rises, rather than participating in it after it rises. Every time a KOL shares or promotes, whether his judgment is correct will be recorded and weighted, affecting his weight score. All of this can be repeatedly verified through timestamps and data analysis tools.
The core circle (called "smart followers" 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 campaign demonstrated the success of its strategic transformation. This campaign significantly reduced marketing costs by using free KOL leverage. Traditional marketing requires contacting KOLs one by one and paying high fees, while Kaito directly publishes a page to provide KOLs with distribution rewards through a weighted algorithm. This approach not only simplifies the process, but also improves credibility through data transparency. This model allows many KOLs to voluntarily participate in the promotion, helping the project to spread rapidly.
At the same time, the Yap event also solved potential risk issues. Considering that if Twitter changes its API rules in the future, Kaito uses TGE to allow all CT users to bind their accounts to its backend and actively authorize data use. This method allows Kaito to gradually break away from its dependence on Twitter API and begin to master its own data assets. This not only makes Kaito more independent, but also forms a positive cycle between supply and demand: as more CT users bind, the interest of the project party increases, forming a flywheel effect of data matching.
Ultimately, Kaito created a business imagination similar to that of Alimama or ByteDance through this model, becoming a successful marketing ecosystem platform in the crypto industry. At present, this strategy has been executed quite successfully.
Reflection on entrepreneurship: How can practitioners with atypical elite backgrounds break through?
If all CT (Crypto Twitter) users bind their accounts to Kaito’s backend, then when entering the secondary market in the future, Kaito can clearly tell the outside world: “These data are mine.” Whether it is the project owner or the CT user, this binding behavior can form data consensus and trends. This is the core logic behind the Yap activity.
Before I finish talking about Kaito, I want to share a little story about ourselves. We developed similar products before Kaito raised funds, and we even developed them at the same time. More than two years ago, we tried both Alpha tools and GPT-like tools. At that time, the industry was at a low point, our team was not very good at socializing, and we knew very few people in the industry. Although our product was interesting and had potential, there were only a few friends who introduced us to VCs.
At that time, we approached four VCs, one of which was willing to follow the investment, but required us to find a lead investor. The other three simply ignored us, one of the reasons being that our background did not fit the typical image of an elite entrepreneur. They did not deeply understand the logic behind our product, nor did they even try to imagine its potential value, but simply voted down.
Later, we gradually attracted more attention from industry professionals through platforms such as YouTube. Most of these viewers were institutions and practitioners in the industry. Even so, I still did not mention the past to the VCs who had contacted us before, because it was a bit embarrassing. Interestingly, I later saw on my timeline that the VC employees I had contacted before were now full of praise for Kaito, which made me feel very emotional.
We finally chose to go the Alpha tool route, a choice that was related to our limited social circle at the time. We thought it would be difficult to successfully commercialize a ToB product without outside help. We hoped to find recognition from well-known VCs and use their resources to expand the market, rather than just struggling on our own.
I have some advice for entrepreneurs who don't come from a typical elite background. VCs are more concerned about connections and networks, not necessarily your product itself. However, I always believe that good products can speak for themselves. If your product is really good, don't be afraid to show it to the outside world. Today, 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 fame and trust for future startups.
For those who watch my videos or browse my Twitter, I hope to convey the belief that no matter whether you have an elite background or not, as long as your product is good enough, I am willing to help you. Good products and ideas are more important than a gorgeous resume. As long as what you bring out can make me recognize, I will do my best to help you find resources.