Author: rosie, crypto KOL
Compiled by: Felix, PANews
Crypto Twitter (CT) always loves to tell you how to issue tokens: like first accumulating 100,000 followers, improving engagement through tasks, raising funds from top VCs, controlling 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, and the findings show that the traditional methods promoted on CT do not work in actual token issuance.
The lie about engagement
Everyone (including the author) 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 over one 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 release content, but their user engagement does not proportionately match their user base.
The only indicator that showed a positive correlation is surprisingly the retweet count from the week prior to the launch. The p-value coefficient is 0.094, which is almost statistically insignificant, but even so, the correlation is weak.
So when you spend money buying bots and meticulously planning complex task activities, you're actually just burning money on 'meaningless' endeavors.
The myth of low circulation
CT is obsessed with projects that have 'low circulation and high FDV'. The claim is: issue with extremely low circulating supply, create artificial scarcity, then watch the prices soar.
But it turns out that was wrong again.
The percentage of initial circulation relative to total supply has no correlation with price performance. Research shows it has no statistical significance at all.
What really matters is: the dollar value of initial market capitalization.
R² is 0.273, and the adjusted R² is 0.234, with a clear relationship between the two: for every unit increase in initial market capitalization (IMC), the return rate one week later decreases by about 1.37 units.
In short: for every 2.7 times increase in initial market capitalization, the price performance in the first month decreases by about 1.56%. This relationship is so close that it can almost be considered causal.
Lesson: The key is not the ratio 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 definitely going to skyrocket!"
Narration: The results did not skyrocket.
The correlation between the amount of funds raised and the one-week return rate is 0.1186, with a p-value of 0.46. The correlation between the amount of funds raised and the one-month return rate is 0.2, with a p-value of 0.22.
Neither has statistical significance. The amount of funds a project raises has no actual relation to the performance of its tokens.
Why? Because the more funds raised typically means a higher valuation, which in turn means greater selling pressure to overcome. Additional funds do not magically convert to better tokens.
However, CT treats fundraising announcements as buy signals. It's like judging a restaurant's quality based on the rent the owner pays.
Perfect example: projects that raise massive funds in research do not necessarily perform better than those with limited funding. A $100 million fundraise does not guarantee a better token economy or a stronger community than a $10 million fundraise.
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 capture everyone's attention when the token goes live.
But data shows the opposite is true.
After the project launch, user engagement declines. Users turn to the next project with airdrops, and your carefully crafted content gets ignored.
Projects that can sustain good performance built their reputation before the launch week, not during it. They understand that pre-launch attention brings real buyers, while attention during launch week only attracts 'bystanders'. User engagement peaks before TGE when they release launch teasers, not after launch when everyone has moved on to the next opportunity.
The truly effective method
If Twitter engagement, low circulation, VC support, and hype timing aren't important, then what is?
Actual product utility
Projects that generate organic content (like Bubblemaps with on-chain polling features or Kaito with narrative tracking features) outperform meme-centric accounts. Bubblemaps and Kaito have significant and sustained user engagement because their products naturally create alpha-full content.
Trading retention rate
Tokens that maintain trading volume after initial hype tend to perform significantly better in price. The Spearman rank correlation coefficient (PANews note: a non-parametric measure of the dependence between two variables) is -0.356 (p = 0.014) — tokens with a substantial drop in trading volume often perform worse in price. In the highest quartile of trading volume retention one month after issuance (PANews note: a type of quantile in statistics that divides all values into four equal parts), both the median and mean price performance are significantly higher.
Reasonable initial market capitalization
The strongest predictor of success. The correlation coefficient is -1.56, and it is statistically significant. Listing at a reasonable valuation gives you room for growth. Listing at over $1 billion market cap is going against the tide.
Real communication
A consistent tone that matches the product. Powerloom's $5.2 million fundraising and overly cynical tone did not align — POWER plummeted 77% in the first week and has since dropped 95%. Meanwhile, Walrus tweeted with sincere humor, and the token price rose 357% a month later during the token generation event (TGE). Hyperlane maintained a realistic update and soared 533% in the first week.
What went wrong with CT?
This disconnect is not malicious but structural.
CT rewards engagement, not accuracy. Posts about '10 ways to achieve 100x token issuance' get more retweets than 'what the data actually shows'.
KOLs accumulate followers by 'catering' to projects, not challenging them. Telling users that their engagement farming is meaningless yields no returns.
Moreover, most KOLs on CT have actually never issued tokens. They are just commenting on a game they have never played. Projects like Story Protocol that genuinely launch products continue to perform well, independent of Twitter follower counts.
The real Meta
Here are the actual practices of successful projects (based on data):
Focus on building products that people want to use
Reasonable pricing at token release
Engage in genuine communication with the audience
Measure what really matters, not just the number of likes
This is definitely revolutionary.
Take Quai Network as an example — they focused on technical explanations and educational posts about their unique blockchain consensus model. During the TGE period, the average view count was around 24,000. QUAI rose by 150% in the first week after launch. This wasn't because they had 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, while everyone caters to the Twitter algorithm, those who truly succeed are the ones quietly building useful things and publishing wisely.
Case Study: Zora failed to disclose tokenomics details in a timely manner, resulting in a 50% drop one week after TGE. Meanwhile, projects that adopted transparent methods and focused on product-driven content consistently performed well.
CT does not intentionally lie. But when the incentive structure rewards popular opinions over hard data, useful information gets drowned in the noise.
