In-depth analysis of Kaito: How did Yap activities detonate the social flywheel?

Compilation|Wu Shuo Blockchain

This issue is the content of Alex's personal YouTube channel, which focuses on the recently 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 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 core advantages are explained. In addition, it compared the direction exploration of similar projects and pointed out how Kaito broke through the limitations of traditional data services through API call optimization, KOL graph construction and social binding mechanisms, successfully completed strategic transformation and established a unique market position. At the same time, he shared the entrepreneurial experience and insights of practitioners in related industries, pointing out the challenges and opportunities faced in the process of productization and commercialization of Web3.

Crypto traffic acquisition methods: the difference between delivery and

fission modes

Crypto is an area of ​​high volatility, high risk, and strong financial attributes. You may find opportunities, or you may need to be mentally prepared for your principal to return to zero. Next, let's talk about the first part: why Kaito and similar products choose Twitter as their main location.

First of all, from the perspective of the consumer goods industry, traffic structures are generally divided into two categories: public domain traffic and private domain traffic. In terms of how to obtain traffic, there are two main paths: placement and fission. Public domain traffic usually includes Twitter and YouTube. In the encryption industry, Telegram and Discord are private domain traffic. In contrast, private domain traffic is more difficult to track and its structure is relatively simple.

Although there are platforms such as Reddit or Instagram and TikTok that are gradually getting involved in the encryption industry, at present, Twitter and YouTube still have the highest traffic concentration. If placed in a domestic environment, it may need to be promoted with the help of Xiaohongshu, Douyin, and Kuaishou, as well as grass-growing platforms such as Bilibili, and finally in-site promotion through through trains or Vientiane. After that, the traffic is directed to private domains such as WeChat for conversion and repurchase.

In general, the traffic acquisition method in the Crypto industry is relatively simple, because the delivery logic cannot carry enough performance at the current industry stage. This results in a relatively single acquisition method for the entire traffic ecology, which mainly focuses 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 believe there are ultimately only two ways to obtain traffic: distribution and fission. Although these two 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 products to them for endorsement, and then they distribute them to retail investors or retail users.

Fission is to create a set of activities that attract users to actively participate by designing an efficient fission mechanism. For example, Kaito's Yap campaign is a typical case. Users share a piece of their Crypto Twitter (CT) account data, such as showing how many "smart followers" they have, forming a gameplay similar to NetEase Cloud's annual playlist or consumption bill. Essentially, 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 carry out effective weighted evaluation of content in the private domain. For example, if a community is all about Kaito, you can't accurately assess the true value and impact of the data. At the same time, the decentralized nature of private domain platforms also makes it very difficult to fully obtain relevant data. Because of this, this is not a priority.

Why Kaito chose Twitter as his primary platform

On public platforms such as YouTube, content is usually presented in the form of long videos. For example, it could be a stand-up video or interview like the one I'm recording now, or it could be more focused on tutorials and interactive content, 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 explanation and learning. Therefore, this content carrier is inherently unsuitable for scenarios driven by immediate events or hot topics.

These long video content are usually better suited to deal 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 ultimately found that the targets that can be effectively observed are usually projects like Kaspa and Helium, and for some short-term popular meme tokens, , the performance is completely bad.

Twitter, by contrast, is a natural fit as a data platform, especially in environments where the concentration of social data is very high. 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. For example, data such as your watch list, number of interactions (engagement), etc. are all presented in an explicit form. On platforms like YouTube, it is difficult to obtain clear details of fan relationships or interactions.

Ultimately, the reason for choosing Twitter as the primary platform was that it was the optimal solution. Its transparent social graph and centralized traffic structure provide us with clear advantages. In comparison, on platforms like YouTube, it is very difficult or even impossible to obtain similar relationship network data. Therefore, both we and Kaito prefer to choose Twitter as the main position.

Two major reasons for Kaito’s high pricing: API costs and regulatory

restrictions

We used some "weird tricks" at the time. Twitter had not yet been acquired by Musk, and there were some gray areas in the system. For example, using education accounts or other methods to obtain data, although not completely compliant, is common in the early stages. For early projects like Kaito, I suspect they initially adopted a similar strategy of obtaining data through these informal means. However, when the product began to be commercialized, it became clear that this approach could no longer be used.

When they completed financing and launched their products two years ago, they could only rely on commercial APIs. After Musk acquired Twitter, he also blocked many irregular channels. The cost of using commercial APIs is quite high, and as the number of calls increases, this cost increases linearly rather than decreases.

The second reason for high pricing is Twitter's regulatory restrictions. Even if a company uses a commercial API, there is an upper limit on the number of calls per month (can't remember the specific number). This means that if the product is particularly popular, the limit on the number of calls will make the ToC (consumer-oriented) model unsustainable. In the end, both Kaito and we chose the ToB (enterprise-oriented) model at a similar point in time, which is the best solution to maximize the economic value of a limited number of calls. For Kaito, this is a direction with almost no other options.

Specifically, since the call volume is fixed, the only way to achieve greater economic returns is by increasing the value of an individual user, which in layman's terms is a price increase. And this is precisely the necessary choice of products, otherwise the entire business model cannot be established.

I understand that their delay is about 15 minutes, which is about the same as ours. It should be understood that the shorter the delay, the higher the cost. This is because historical data needs to be scanned more frequently, and the cost increases exponentially. The setting of delay time also directly affects the efficiency and economic feasibility of API calls. All in all, Kaito's high pricing is justified given the cost of API calls and regulatory restrictions.

Evolution and selection of Kaito product direction

Next, let’s talk about Kaito’s product direction and why they have developed from “trending” type products to the current KOL type functions. First of all, 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 multiple directions and found that there are three directions that can be derived based on this set of logic.

The first direction is pure Alpha tools for personal use. Kaito's CEO mentioned in a podcast that they had also considered this direction. If the tool is only used for Alpha type purposes, then the more developed it is, the more likely it will 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 value it; if there is a charge, why not just use it yourself? Problems like these make Alpha tools generally more suitable for home use rather than production.

We have developed a set of tools ourselves using logic similar to Kaito. Using this set of tools allows us to often spot projects before they take off. We have considered using this logic to create a listing tool 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 at all in our “God’s Eye View” 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 studied 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. The test results show that this strategy is more effective for projects with smaller market capitalization and that are easily driven by emotions and events, while it has limited effect for projects with larger market capitalization. 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.

An exploration of Kaito’s community journalism tool and its 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 the current Alva (formerly Galxe) can obtain the corpus of all tweets by integrating Twitter's time-sharing data and process it with the ChatGPT interface. By adjusting prompts on the front end, these data can be output in a more intuitive form, thereby generating a lot of instant community news.

To give a simple example: For example, you may be confused when you see the dispute between upper and lower case "elisa". At this time, you can directly ask the tool: "What is the reason for the dispute between upper and lower case 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 expiration date and usually cannot provide content within the latest six months. You can only crawl the relevant corpus yourself and feed it to GPT, and then use prompts to summarize the logic. This type of tool has huge potential and is a direction worthy of further exploration.

From the current point of view, Kaito seems to be 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 encryption fields such as Rootdata, and connects users and 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 detail of cleaning. In contrast, Kaito’s advantage lies in the accuracy of its data, which is beyond doubt.

As a practical example, I recently got a quick answer to a question about the debate over the upper and lower case of "elisa" using this type of tool. 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 (you need to pay a fee every time you call the GPT interface), the gross profit margin of the product is low. Therefore, although such tools are of certain significance, their commercialization faces greater challenges. Many calling behaviors are more for the purpose of promoting activity, and the actual scenarios for generating income are limited. These have become problems that need to be overcome. In general, although this direction has great potential, it still needs more optimization and breakthroughs in actual implementation.

Data accuracy and the role of KOL map construction in marketing

When discussing these tools, there is a core question: How do they generate revenue? If the VIP model alone allows users to call the API unlimited times, it is difficult for this product to have a large profit margin, but its existence is meaningful. It can directly use Kaito's logic to read Twitter data and use it to generate and distribute self-media content, such as "Wu Shuo" 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 publish them on TikTok, or directly publish them on Twitter.

I think this kind of product direction is not something that only Kaito or Galxe can try. Projects like Mask are actually very suitable for this. Oddly, Mask doesn’t seem to be deeply involved in this direction yet. If any members of the Mask team hear these suggestions, I hope you can try and consider them.

For Kaito, its current product direction already indicates that they want to move towards a larger market cap, rather than continue down the path of Alpha tools. Alpha tools, while profitable, lack product potential. If you only focus on this, it will end up being limited to internal use and unable to form a product for the larger market. Kaito obviously aims to break through this bottleneck by turning to KOL graph construction.

The early users who were interested in Kaito products were almost the same group of users who were paying attention to our tools at that time. Our instruments were also recommended for sale to some trading firms or secondary funds in the early stages. Although these trading companies focus more on profitability, this direction will fall into a cycle of "whether to make profits or not." In contrast, the KOL graph provides precise support for marketing delivery, improving delivery effects through data accuracy, thereby increasing 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 are accurate on the open market. The core of data accuracy lies in "data washing", which is the most difficult and critical link. Collecting data is relatively simple, but weighting and cleaning the data requires a lot of repeated testing and logical adjustments, which often requires a combination of experience and intuition.

For example, Crypto Twitter (CT) in the Chinese community often has more noise and its weight needs to be reduced. This noise causes Chinese CT to typically lag behind English CT by 24 to 48 hours. How to effectively clean and adjust data is a "special skill" and also the company's core competitiveness.

Through accurate KOL maps, Kaito can help project parties optimize their delivery strategies and improve their delivery accuracy. This product can not only assist project parties to achieve more efficient marketing, but also obtain marketing expenses and form a sustainable business model. Choosing this direction is Kaito's smart strategy in market competition.

The strategic logic and flywheel effect behind Yap activities

Across the Crypto space, advertising has always been a relatively vague and inefficient activity. The current marketing agency is essentially a simple tool for maintaining an address book, and its means are relatively simple. In this context, the tools provided by Kaito can help project parties determine which KOLs are worth investing in and which ones are not, and provide evidence-based reference through data analysis. This precision greatly improves the effectiveness of advertising.

Kaito optimizes KOL placement through two key indicators: correctness and core circle. Correctness refers to whether the KOL’s judgment is accurate, for example, whether they have discussed an item before it goes up rather than getting involved after the item goes up. Every time a KOL is shared or promoted, whether the KOL’s judgment is correct or not will be recorded and weighted, affecting its weight score. This can all be verified repeatedly 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 the project team identify truly influential KOLs, not just accounts with a large number of fans.

Kaito's Yap campaign demonstrates the success of its strategic transformation. This campaign significantly reduces marketing costs by using free KOL leverage. Traditional marketing requires contacting KOLs one by one and paying high fees, while Kaito directly exposes a page and provides distribution rewards to KOLs through a weighting algorithm. This approach both simplifies the process and increases credibility through data transparency. This model allows many KOLs to voluntarily participate in promotion, helping the project spread quickly.

At the same time, the Yap campaign also addresses potential risk issues. Considering that if Twitter changes 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 approach enables Kaito to gradually break away from dependence on TwitterAPI and begin to take control of its own data assets. This not only gives Kaito greater independence, but also forms a positive cycle between supply and demand: as more CT users are bound, the project party's interest increases, forming a flywheel effect of data matching.

Ultimately, Kaito created a business imagination similar to Alimama or Juju Engine through this model, and became a successful marketing ecological platform in the encryption industry. So far, this strategy has been quite successful.

Reflections on Entrepreneurship: How practitioners with atypical elite

backgrounds can 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 party or CT users , this binding behavior can form data consensus and trends. This is the core logic behind the Yap campaign.

Before I end the Kaito topic, I want to share a little story about ourselves. Before we raised money from Kaito, we also developed similar products, or even at the same time. More than two years ago, we tried the direction of Alpha tools and GPT-like tools at the same time. At that time, the industry was at a trough, 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, only a few VC friends introduced us to it.

At that time, we contacted four VCs, and one of them was willing to invest, but we needed to find a lead investor. The other three simply ignored us. One of the reasons was that our background did not conform to the typical image of elite entrepreneurs. Instead of understanding the logic behind our product or even trying to imagine its potential value, they simply voted it down.

It was not until later that we gradually attracted the attention of more industry people through platforms such as YouTube. Most of these audiences are institutions and practitioners in the industry. Even so, I still didn't mention the past to the VCs who had contacted us because it was a bit embarrassing. Interestingly, I later saw on my timeline that the VC employees I had been in contact with were now full of praise for Kaito, which made me very moved.

We finally chose to take the Alpha tool route. This choice was related to our limited social circle at the time. We believe that it will be difficult to successfully commercialize ToB's products without outside help. We hope to find recognition from well-known VCs and use their resources to complete market expansion, instead of struggling to move forward on our own.

I have some advice for entrepreneurs who don’t come from typically elite backgrounds. VCs focus more on connections and relationships, not necessarily on your product itself. However, I always believe that a good product can speak for itself. If your product is really good, don’t be afraid to show it. Today, I also realize the importance of building a social presence. Through social networks, you can not only get to know more people, but also accumulate a certain degree of popularity and trust for future entrepreneurship.

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 come up with can be recognized by me, I will do my best to help you find resources.