Compiled by | Wu Talks Blockchain
This issue focuses on the content of Alex's personal YouTube channel, centered around the recently popular social product Kaito, delving into 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 compares the exploratory directions of similar projects, pointing out how Kaito breaks through traditional data service constraints through API call optimization, KOL mapping, and social binding mechanisms, successfully completing a strategic transformation and establishing a unique market position. It also shares the entrepreneurial experiences and insights of industry practitioners, directly addressing the challenges and opportunities faced in the productization and commercialization of Web3.
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The difference between traffic acquisition methods in Crypto: investment and fission models
Crypto is a high-volatility, high-risk field with strong financial attributes. You may discover opportunities from it, but you should also be prepared for your principal to completely zero out. Next, let's discuss the first part: why Kaito and similar products choose Twitter as their main battleground.
First, from the perspective of the consumer goods industry, traffic structure generally divides into two categories: public traffic and private traffic. In terms of traffic acquisition methods, there are also two main paths: investment and fission. Public traffic usually includes Twitter and YouTube, while in the crypto industry, Telegram and Discord belong to private traffic. In contrast, private traffic is more challenging to track, and its structure is relatively singular.
Although platforms like Reddit, Instagram, and TikTok are gradually entering the crypto industry, currently, Twitter and YouTube still have the highest traffic concentration. In the domestic environment, it may need to leverage platforms like Xiaohongshu, Douyin, and Kuaishou for promotion, and also require platforms like Bilibili for recommendations, finally promoting through internal channels like Direct Connect or Wanshangtai. Afterward, traffic can be directed to WeChat and other private domains for conversion and repurchase.
Overall, the traffic acquisition methods in the crypto industry are relatively simple because the logic of investment cannot sustain enough efficacy at the current industry stage. This leads to a relatively singular traffic acquisition method, mainly focused on distribution and fission.
Comparison of user acquisition costs and fission effects in different regions
Over two years ago, when we were developing our own tool product, we tried advertising strategies. I invested several tens of thousands of dollars for testing; although I cannot disclose specific data, a very clear result was that acquiring a US user costs about ten times that of acquiring a Vietnamese user. However, the fission rate of Vietnamese users is significantly higher than that of US users. This indicates that US users are less inclined to actively participate in fission promotion, such as creating and spreading a landing page.
In the entire crypto industry, I believe there are fundamentally only two ways to acquire traffic: distribution and fission. Although both methods essentially belong to a form of fission, their application logic is different. Distribution tends to rely on KOLs (Key Opinion Leaders) or KOCs (Key Opinion Consumers) for promotion, where you hand over the product for their endorsement, and they then distribute to retail users.
Fission involves designing an efficient mechanism to create activities that attract users to participate voluntarily. For example, Kaito's Yap event is a typical case. Users share data from their Crypto Twitter (CT) accounts, such as displaying how many 'smart followers' they have, creating a gameplay similar to NetEase Cloud's annual playlist or consumption bill. Essentially, these mechanisms aim to achieve fission through users' voluntary sharing to gain more traffic.
Explaining these background details, it’s easier to understand why we initially chose Twitter as the main platform instead of private domains. The biggest issue with private domains is that it is difficult to standardize the acquisition of all content, and content within private domains is hard to effectively weight and assess. For instance, if a community is solely discussing Kaito, you cannot accurately evaluate the true value and impact of that data. Additionally, the decentralization of private platforms makes it very challenging to comprehensively acquire related data. For this reason, it was not a priority choice.
Why Kaito chose Twitter as the main platform
On public platforms like YouTube, content is often better presented in long video formats. For example, it can be a monologue video like the one I'm recording now, an interview format, or content that focuses more on tutorials and interactions, even some mining machine operation guides. Such content often requires long production and viewing times, suitable for topics that need detailed explanations and learning. Therefore, this type of content is essentially not suitable for real-time events or hot topic-driven scenarios.
These long video contents are usually more suitable for handling PoW (Proof of Work) related topics. Therefore, although we have also tried to introduce Kaito's monitoring and analysis logic on YouTube and Farcaster, we ultimately found that the observable targets are typically projects like Kaspa and Helium, while the performance for certain short-term meme tokens is completely inadequate.
In contrast, Twitter is inherently suitable as a data platform, especially in environments where social data concentration 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 very transparent; for instance, your follower list, engagement counts, and other data are presented in an explicit format. However, on platforms like YouTube, it is challenging to obtain clear fan relationships or interaction details.
Ultimately, the reason for choosing Twitter as the main platform lies in that it is the optimal solution. Its transparent social graph and centralized traffic structure provide us with clear advantages. In comparison, it is very difficult, if not impossible, to obtain similar relational data on platforms like YouTube. Therefore, both we and Kaito are more inclined to prioritize Twitter as the main battleground.
Two main reasons for Kaito's high pricing: API costs and regulatory restrictions
At that time, we used some 'tricks'; 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, was common in the early stages. For early projects like Kaito, I suspect they initially adopted similar strategies to obtain data through these informal channels. However, once the product began to commercialize, this approach clearly could not continue.
Two years ago, when they completed financing and launched their product, they could only rely on commercial APIs. After Elon Musk acquired Twitter, many irregular paths were blocked. The use of commercial APIs is quite costly, and as the number of calls increases, this cost grows linearly rather than decreasing.
The second reason for the high pricing is Twitter's regulatory restrictions. Even if a company uses commercial APIs, there is a monthly limit on the number of calls (I can't recall the exact number). This means that if the product is particularly popular, the limitation on call volume will make the ToC (consumer-oriented) model difficult to sustain. Ultimately, both we and Kaito chose the ToB (business-oriented) model at similar points in time, which is the best solution for maximizing the economic value of limited call volume. For Kaito, this was almost the only available direction.
Specifically, due to the fixed call volume, the only way to achieve greater economic returns is to increase the value of a single user, simply put, to raise the price. This is precisely a necessary choice for the product; otherwise, the entire business model cannot be established.
I learned that their delay is about 15 minutes, which is roughly the same as ours. It needs to be understood that the shorter the delay time, the higher the cost required. This is because historical data must be fetched at a higher frequency, and this cost increase 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 justified.
The evolution and selection of Kaito's product direction
Next, let's talk about Kaito's product direction and why they evolved from 'trending' type products to their current KOL-based functions. Here, I'll give a small conclusion—it's not about teaching others how to start a business, but rather sharing our own experiences. We have tried multiple directions and found that there are three directions that can be derived based on this logic.
The first direction is a purely self-used Alpha tool. The CEO of Kaito mentioned in a podcast that they also considered this direction. If the tool is only for Alpha-type purposes, the more it develops, the more it tends to be used internally, which is not suitable for large-scale users. We have encountered similar problems—if it's free, users may not cherish it; if it charges, why not just use it ourselves? Such issues make Alpha tools generally more suitable for internal use rather than productization.
We once developed a set of tools using a logic similar to Kaito's. The application of this tool enabled us to often discover projects before they became popular. We considered using this logic to create a listing tool for exchanges. For example, I once wanted to cooperate with Binance to provide this tool for free to optimize their listing selection criteria. Because certain projects, like ACT, did not show any noteworthy performance in our 'God view' based on Twitter data analysis, yet they were still listed on exchanges. Such unreasonable choices could have been avoided through data-driven tools.
In addition, we 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, and so on. The test results showed that this strategy is significantly more effective for smaller market cap projects that are easily influenced by emotions and events, while it is less effective for larger market cap projects. I believe Kaito has also conducted similar research, as their CEO has a trading background. From this perspective, we and Kaito share many similarities in our early starting points and logic, but the paths we ultimately chose differ.
Exploration of Kaito's community news tool and its industry potential
In the current model framework, some phenomenal themes, such as memes and NFTs, are very significant. They can display price increase potential within this logic. However, such phenomena cannot be entirely resolved through standardized programmatic trading, as they still require significant human intervention. This characteristic means that while they are effective, they lack standardization. As for whether Kaito has similar product directions internally for its own use, I am not clear on that.
The second direction worth exploring is news-type and GPT-type products. What does this mean? For example, the current Alva (formerly Galxe) Web3 assistant can access all tweets' corpus by integrating Twitter's real-time data and process it with ChatGPT's interface. By adjusting prompts in the front end, these data can be output in a more intuitive form, generating many timely community news articles.
A simple example: if you see the dispute over the capitalization of 'elisa,' you might be confused. At this point, you can directly ask this tool: 'What is the reason for the dispute over the capitalization of elisa? Who initiated it?' Through this method, the tool can 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, then summarize the logic through prompts. Such tools have great potential and are worth exploring further.
As of now, Kaito seems to be exploring such products or trying similar directions. The Alva product I mentioned is a good example. It integrates a large amount of industry data through calling APIs related to Rootdata 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 data networks but still have room for improvement in data precision and the thoroughness of cleaning. In contrast, Kaito's advantage lies in its data accuracy, which is beyond doubt.
For instance, in a recent case regarding the capitalization dispute of 'elisa,' I obtained quick answers through such tools. The application of such products in the crypto industry can indeed significantly enhance efficiency. Over two years ago, we also developed similar tools, and testing showed that they could indeed improve work efficiency. However, when we attempted to commercialize, the core issue we faced was the lack of strong willingness to pay from users. Although the tool could enhance efficiency, it did not target a core pain point, which led to a lack of strong purchasing motivation among users.
Furthermore, due to the high calling costs of such tools (each call to the GPT interface incurs a fee), the product's gross profit margin is relatively low. Thus, although these tools have some significance, their commercialization faces significant challenges. Many calling behaviors are more for activation purposes, and there are limited scenarios that generate actual income, all of which become problems that need to be overcome. Overall, while this direction has great potential, more optimization and breakthroughs are still needed in practical 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 relying solely on a VIP model, allowing users to call the API an unlimited number of times, it is challenging for such products to have significant profit margins, but their existence is meaningful. They can directly utilize Kaito's logic to read Twitter data for generating and distributing self-media content, such as 'Wu Talks' or other forms of community news. Such tools can not only enhance efficiency but also help project parties distribute content across multiple platforms, for instance, generating short videos with AI for release on TikTok or directly posting on Twitter.
I believe that this product direction is not something only Kaito or Galxe can attempt; projects like Mask are also very suitable for this. Strangely, Mask doesn't seem to have deeply engaged in this direction so far. If anyone from the Mask team hears this suggestion, I hope you can consider it.
For Kaito, its current product direction indicates that they hope to aim for a larger market value, rather than continue along the Alpha tool path. While Alpha tools can be profitable, they lack the potential for productization. If focused solely on this, it will ultimately be limited to internal use, unable to form a product for a larger market. By shifting to KOL mapping, Kaito is clearly looking to break through this bottleneck.
The users who were interested in Kaito's product early on were almost the same as those who were looking at our tool at that time. Our tool was also suggested to be sold to some trading companies or secondary funds in the early days. Although these trading companies were more focused on profitability, this direction would fall into a loop of 'whether it is profitable.' In contrast, KOL mapping provides precise support for marketing investment, enhancing the effectiveness of investment through data accuracy, thereby increasing the marketing value for project parties.
Data accuracy is key. Although many companies can collect Twitter data, whether the data is accurate is another matter. In the public market, Kaito and our early tools are some of the few that can achieve accuracy. The core of data accuracy lies in 'data cleaning,' which is the most difficult and critical step. Collecting data is relatively simple, but weighting and cleaning the data requires extensive repeated testing and logical adjustments, often requiring a combination of experience and intuition.
For example, the Chinese community's Crypto Twitter (CT) often has more noise, necessitating a reduction in weight. This noise causes Chinese CT to typically lag 24 to 48 hours behind English CT. Effectively cleaning and adjusting data is a 'core skill' and represents the company's core competitiveness.
Through precise KOL mapping, Kaito can help project parties optimize their investment strategies and improve the accuracy of their investments. This product not only assists project parties in achieving more efficient marketing but can also generate marketing fees, forming a sustainable business model. Choosing this direction is a smart strategy exhibited by Kaito 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. Current marketing agencies are essentially more like simple tools for maintaining address books, with relatively singular methods. In this context, the tools provided by Kaito can help project parties determine which KOLs are worth investing in and which are not, providing evidence-based references through data analysis. This accuracy greatly enhances advertising efficiency.
Kaito optimizes KOL investment through two key indicators: accuracy and core circle. Accuracy refers to whether the KOL's judgment is correct, such as whether they discussed a project before its rise rather than after it has risen. Each share or promotion is recorded and weighted based on whether the KOL's judgment was correct, 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 followers interacting with it, its weight score will be higher. This helps projects filter out truly influential KOLs rather than just accounts with a large number of fans.
Kaito's Yap event showcases its successful strategic transformation. This event significantly reduced marketing costs by leveraging free KOLs. Traditional marketing requires contacting KOLs one by one and paying high fees, while Kaito directly opened a page and provided allocation rewards for KOLs through a weighting algorithm. This method simplified the process and enhanced credibility through data transparency. This model encouraged many KOLs to participate in promotion voluntarily, helping the project to spread rapidly.
At the same time, the Yap event also addresses potential risks. Considering the future if Twitter changes its API rules, Kaito allows all CT users to bind their accounts to its backend through TGE, actively authorizing data usage. This approach enables Kaito to gradually detach from its reliance on Twitter API and begin to master its data assets. This not only gives Kaito greater independence but also creates a positive cycle between supply and demand: as more CT users bind, project parties' interest increases, forming a flywheel effect of data matching.
Ultimately, Kaito created a business imagination similar to Alibaba Mama or Douyin Engine through this model, becoming a successful marketing ecological platform in the crypto industry. As it stands now, this strategy has been executed quite successfully.
Entrepreneurial Reflection: How non-typical elite background practitioners 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: 'This data is mine.' This binding behavior can create a consensus and trend among both project parties and CT users. This is exactly the core logic behind the Yap event.
Before concluding the Kaito topic, I would like to share a little story about ourselves. Before Kaito's financing, we also developed similar products, and one could even say we were doing this simultaneously. Over two years ago, we tried both the Alpha tool and GPT-like tools at the same time. At that time, the industry was in a low valley, and our team was not very good at social networking, and we knew very few people in the industry. Although our product was interesting and had potential, very few friends introduced us to VCs.
At that time, we contacted four VCs, and one was willing to co-invest but needed us to find a leading investor. The other three directly ignored us, one reason being that our background did not fit the typical elite entrepreneur image. They did not delve into understanding the logic behind our product, or even attempted to imagine its potential value, simply giving us a flat rejection.
Later, we gradually gained attention from more industry professionals through platforms like YouTube. Most of these viewers were institutions and practitioners in the industry. Even so, I still haven't mentioned the past to those VCs who once contacted us, as it felt somewhat awkward. Interestingly, I later saw that employees from the VCs we had contacted were now praising Kaito, which made me quite emotional.
We ultimately chose to follow the Alpha tool route, a choice related to our limited social circle at that time. We believed that without external help, it would be difficult to successfully commercialize a ToB product. We hoped to find recognition from well-known VCs to leverage their resources for market expansion, rather than relying solely on our own difficult path.
For those non-typical elite background entrepreneurs, I have some advice. VCs focus more on connections and relationship networks, rather than necessarily on your product itself. However, I firmly believe that good products can speak for themselves. If your product is genuinely good, do not hesitate to showcase it. Nowadays, I also realize the importance of building social influence. Through social networks, you can meet more people and accumulate a certain level of recognition and trust for future entrepreneurship.
For friends who watch my videos or browse my Twitter, I hope to convey the belief that no matter whether you have an elite background, as long as your product is excellent enough, I am willing to help you. A good product and idea are more important than a glamorous resume. As long as what you present can gain my recognition, I will do my best to help you find resources.