Author: rosie, Crypto KOL
Compiled by: Felix, PANews
Crypto Twitter (CT) always likes to tell you how to issue tokens: like first accumulating 100,000 followers, improving engagement through tasks, raising funds from tier-one VCs, controlling the circulating supply to 2% at issuance, and maximizing hype during the token generation event (TGE) week.
The problem is: it's all nonsense.
Simplicity Group recently released a research report analyzing 50,000 data points from 40 major token issuances in 2025, showing that the traditional methods touted on CT do not work in actual token issuance.
The lies about participation
Everyone (including the authors) is obsessed with various metrics on Twitter. Likes, retweets, replies, impressions—all these vanity metrics. Project teams spend thousands of dollars on engagement farming, task platforms, and buying followers.
Correlation with price performance within a week: almost zero.
Simplicity Group's regression analysis shows that the correlation coefficient R² between engagement metrics and price performance is only 0.038. In short: engagement hardly explains token success.
Likes, comments, and retweets are actually slightly negatively correlated with price performance. This means that projects with higher engagement sometimes perform worse. GoPlus, SonicSVM, and RedStone continuously publish content, but their user engagement does not proportionally align with their user base.
The only indicator that shows a positive correlation is surprisingly the number of retweets in the week leading up to the release. The p-value coefficient is 0.094, which is almost statistically insignificant, but even so, the correlation is weak.
So, when you spend money hiring shills and meticulously planning complex task activities, you are actually just wasting money on 'meaningless' endeavors.
The myth of low circulation.
CT is obsessed with 'low circulation high FDV' projects. This saying is: issue with an extremely low circulating supply, create artificial scarcity, and then watch the price skyrocket.
But it turned out to be wrong again.
The percentage of initial circulation relative to total supply has no correlation with price performance. Studies show it has no statistical significance.
What really matters is: the dollar value of the initial market cap.
R² is 0.273, adjusted R² is 0.234, and the relationship between the two is very clear: for every unit increase in initial market cap (IMC), the return rate one week later decreases by about 1.37 units.
In short: for every 2.7 times increase in initial market cap, the price performance in the first month decreases by about 1.56%. This relationship is so tight that it can almost be regarded as causal.
Lesson: The key is not the percentage of unlocked tokens but the total dollar value entering the market.
The illusion of VC support.
"Wow, they raised $100 million from a16z, this is sure to skyrocket!"
Narration: The results did not skyrocket.
The correlation between financing amount and one-week return rate is 0.1186, with a p-value of 0.46. The correlation between financing amount and one-month return rate is 0.2, with a p-value of 0.22.
Neither has statistical significance. The amount of funding a project raises has no correlation with the performance of its tokens.
Why? Because the more funds raised usually means a higher valuation, which means overcoming greater selling pressure. Additional funds do not magically convert into better tokens.
However, CT sees financing announcements as buy signals. It's like judging a restaurant's quality based on the rent paid by the owner.
Perfect example: projects that raised huge amounts of funds in research do not necessarily perform better than those with limited funding. A funding amount of $100 million does not guarantee better token economics or a stronger community than a funding amount of $10 million.
The fallacy of hype timing.
The traditional view is to save the most important news for the project launch week to maximize the 'FOMO' atmosphere and attract everyone's attention when the token goes live.
But the data shows the opposite.
User engagement declines after the project launch. Users turn to the next project with an airdrop, and your carefully prepared content gets ignored.
Projects that can maintain good performance consistently established their visibility before the launch week, not during it. They understand that attention before launch brings real buyers, while attention during launch brings only 'passersby'. User engagement peaks before TGE, when they released the launch preview, not after the launch, when everyone has already moved on to the next opportunity.
Truly effective methods.
Since Twitter engagement, low circulation, VC support, and timing of hype are not important, then what is important?
Actual product utility.
Projects that generate organic content (like Bubblemaps with on-chain survey features or Kaito with narrative tracking) outperform meme-based accounts. Bubblemaps and Kaito have large and sustained user engagement because their products naturally create alpha-full content.
Retention rate of transactions.
Tokens that maintain trading volume after initial hype tend to perform significantly better. The Spearman rank correlation coefficient (Note: this is a non-parametric measure of the dependence between two variables) is -0.356 (p = 0.014)—tokens with a significant decline in trading volume tend to perform worse. In the month following issuance, the top quartile of retention in trading volume has significantly higher median and average price performance.
Reasonable initial market cap.
The strongest predictive indicator of success. The correlation coefficient is -1.56, and it is statistically significant. Going public with a reasonable valuation leaves room for growth. Going public with a market cap of over $1 billion is going against the odds.
Authentic communication.
A consistent tone that matches the product. Powerloom's $5.2 million funding and overly cynical tone did not align—POWER dropped 77% in the first week and has fallen 95% since launch. Meanwhile, Walrus tweeted in a sincere and humorous way, and a month later, the token issuance (TGE) price increased by 357%. Hyperlane adhered to realistic updates, surging 533% in the first week.
Why does CT go wrong?
This disconnect is not malicious but structural.
CT rewards engagement, not accuracy. Posts about '10 ways to achieve 100x token issuance' get retweeted more than 'what the data actually shows'.
KOLs accumulate followers by 'catering' to projects rather than challenging them. Telling users that their engagement farming is meaningless does not yield returns.
Furthermore, most KOLs on CT have actually never issued tokens. They are just commenting on a game they have never played. In contrast, projects like Story Protocol that have actually launched products consistently perform well, regardless of Twitter follower count.
The real Meta.
Here are the actual practices of successful projects (according to the data):
Focus on building products that people want to use.
Reasonable pricing at the time of token release.
Engage in sincere communication with the audience.
Measure what truly matters, not the number of likes.
This is absolutely revolutionary.
Take Quai Network as an example—they focused on technical explanations and educational posts about their unique blockchain consensus model. During TGE, the average views were about 24,000. QUAI surged 150% in the first week after going live. This was not due to having millions of followers, but because they genuinely sparked interest in their innovation.
In contrast, projects that burn money on task platforms and engagement marketing see their tokens plummet because no one truly understands or cares about what they are building.
Ironically, even though everyone is catering to Twitter algorithms, those who truly succeed are the ones quietly building useful things and publishing wisely.
Case Study: Zora failed to disclose token economics details in a timely manner, leading to a 50% drop a week after TGE. Meanwhile, those projects that employed public and transparent methods and focused on product-driven content consistently performed well.
CT does not intentionally lie. But when the incentive mechanism rewards popular opinions instead of hard data, useful information gets drowned in the noise.
