以Web3经典项目为例,解读七大营销法则

Odaily星球日报Published on 2023-11-13Last updated on 2023-11-13

Abstract

如何在这个不断发展的行业里建立一个持久的品牌?

原文作者:phillip_xyz,  Marketing & Growth of Sei Labs

原文编译:Felix, PANews

导语:加密各赛道中的项目竞争日渐激烈,如何脱颖而出成为佼佼者,可以学习借鉴一些传统营销法则。Web3营销的 7 个不变法则源自传统营销领域最杰出的战略家 Al Ries 和 Jack Trout 的经典理论,这些法则是经过时间考验的,对如今Web3营销来说仍然非常有价值。

1、领导力法则:做第一比做的好更好

领导力法则重点关注品牌塑造消费者认知、定义消费者需求以及为新市场类别铺平道路的能力。不是要做最好,而是要做消费者心目中的第一。营销是一场认知之战,谁率先在消费者心中留下自己的形象,谁就会超越那些客观上可能更胜一筹的人。

不管后来者的产品多么独特或优秀,“第一”品牌通常都会在消费者的脑海中留下深刻印象。一旦一个品牌填补了一个品类的空缺,消费者就很难改变。

Web3示例:

以太坊:作为第一个智能合约平台,以太坊塑造了市场对区块链计算和 dApps 的看法。如今,尽管面临其他有能力的平台的激烈竞争,以太坊仍然在人们心目中占据主导地位。

Metamask:Metamask 可能不是最快或最流畅的,但却是第一个链上钱包。作为第一个推出的钱包,尽管竞争对手提供更低的交易费和更丝滑的用户体验,但 Metamask 仍保持了其作为首选加密钱包的地位。

2、类别法则:如果不能成为某个类别第一,那就创建一个新类别

不能成为现有类别中的第一?那就创建一个新类别。这条法则强调在更广阔的市场中建立一个新的、独特的领域。在这个领域中,你的品牌可以成为领导者,而不是在原有赛道上试图与领导者竞争。

作为一名Web3营销人员,你会发现自己所处的赛道中,已经被开拓性的项目所主导。这个原则鼓励你创新思考,创造一个新的利基市场。这种方法不仅可以让你避开激烈的竞争,而且还提供了一个领导和塑造一个新类别的机会。

Web3示例:

Axie InfinityAxie Infinity 在“Play to Earn”领域占据主导地位。然而,像《StepN》这样的新游戏能够开辟自己的利基市场,如“Move to Earn”。

Trader JoeTrader Joe 最初是 SushiSwap 的分叉,如今已成为 Avalanche 生态排名第一的 DEX。Trader Joe 战略性地开辟了一个新的品类,开发游戏式的用户界面和功能从而占据主导地位。

3、思维法则:在思想上领先比在市场上领先更重要

这条法则强调了品牌首先要打入消费者的思想,然后通过重复来巩固这一地位。市场营销的真正战场在于你的目标受众的内心,通过不断重复的信息、口号和核心品牌定位,你可以塑造消费者思想。

混乱的Web3世界中,在你的受众心中创造一个独特空间的能力是无价的。这一切都是为了创造持久的印象,加强与受众之间的联系。从本质上讲,你不仅仅是在销售产品或服务,而是一种感知,一种你的品牌所唤起的感觉。

Web3示例:

Solana尽管网络不稳定,但 Solana 通过不断宣传其速度和成本优势,使其作为快速廉价的以太坊替代品获得了广泛关注。

DOGE:尽管 DOGE 并非第一个 MEME 币,但凭借出色营销、品牌推广和一个类似“邪教”的社区,其成为了加密人士心中 Top 1 的 MEME 币。

4、专注法则:市场营销中最强大的概念是在潜在客户心中拥有一个词/概念

这个法则的关键在于简单和具体的力量,通过将焦点缩小到一个单词或概念,可以“深入”用户的脑海。营销就是操纵你的目标受众的观念,成功倾向于那些巩固品牌地位的人,而不是那些扩大品牌范围的人。

在潜在客户心中拥有一个词,可以让你占据一个竞争对手无法轻易渗透的独特空间。通过专注于一个概念,你可以成为你所在领域的专家,让你更容易被识别和记住。

Web3示例:

Phantom Wallet:Phantom Wallet 专注于“Solana 钱包”这一词,让用户将两者建立强烈联系。

1inch1inch一直高度专注 DEX 聚合,有意识地忽略其他 DeFi 利基市场,从而巩固其作为类别领导者的市场地位。

5、阶梯法则:使用的策略取决于市场地位

阶梯法则表明,你的营销策略应该由你的品牌在市场上的地位决定。如果你是市场领导者,你的重点应该放在整个类别上。如果你是一个挑战者或追随者,你应该专注于如何将自己与更高层次的品牌区分开来。

记住这条法则有助于品牌制定清晰有效的营销策略。这条法则强调了解自己的市场地位并以此采取行动的重要性。

Web3示例:

TensorTensor 在战略上将自己地位成 Magic Eden 的竞争对手,专注于专业交易员,以从 Magic Eden 在 Solana 的市场主导地位中分一杯羹。

LayerZero尽管 LayerZero 进入跨链基础设施领域较晚,但其还是发现了当前差距并调整自身定位。

6、品牌延伸法则:延伸品牌价值存在着不可抗拒的压力

品牌延伸法则是指品牌的过度扩张往往会导致稀释和混乱。当一个品牌代表一切时,最终什么也代表不了,因为试图迎合所有人需求的品牌往往会陷入困境。品牌应该专注于利用有效的方法,而不是理所应当认为拥有忠实的客户群就能实现品牌增长。

理解这一法则可以避免你的品牌陷入过度扩张的陷阱。如果品牌正考虑向新领域扩张,那么这将淡化自身品牌信息并让用户感到困惑。不要轻易毁掉花费大量时间努力打造的品牌。

Web3示例:

Magic Eden:Magic Eden 在扩展到 Solana 生态之外后,在短时间内就失去了市场主导地位。

UniswapUniswap 等 DeFi 协议在去中心化金融领域成功搭建成熟品牌后试图将业务扩展到 NFT,但都失败了。

7、加速定律:成功的项目不是建立在热点上,而是建立在趋势上

加速定律强调,成功的营销策略建立在长期趋势之上,而不是短暂的热点。虽然热点在短期内可能会带来利润,但不会带来持久的利益。

虽然热点追踪不容忽视,但重要的是热点热得快,凉得也快。品牌的营销策略应该是真实的和专注的,而不是跟随每个热点。

Web3示例:

Wonderland DAO:Wonderland DAO(TIME)是 Avalanche 上的 OlympusDAO(OHM)分叉项目,在经历最初热潮后并未能维持活跃的交易活动。

另外,许多虚拟元宇宙项目都犯有追逐逐热点的错误,而不是关注长期趋势或提供可持续的效用。

综上,坚持以上这七条Web3营销法则,就能在这个不断发展的行业里建立一个持久的品牌。

Related Reads

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

U.S. Government Bans Foreign Access to Fable 5, Anthropic Issues Rebuttal On June 12th, the U.S. government ordered AI company Anthropic to immediately suspend all foreign access—including foreign nationals within the U.S. and Anthropic's own foreign employees—to its newly released Fable 5 and Mythos 5 AI models, citing national security concerns. This forced Anthropic to temporarily disable access to both models for all users globally, as it cannot technically differentiate user nationality at scale. The models, released just three days prior, represent Anthropic's highest public capability tier. Fable 5 is the first publicly available model from the advanced "Mythos" family, while Mythos 5 is a less-restricted version for approved cybersecurity and critical infrastructure partners. The government's directive was reportedly triggered by claims from another company that it could "jailbreak" Mythos 5, raising alarm within the Trump administration. Anthropic, in a detailed public statement, strongly challenged this rationale. The company argues the demonstrated "jailbreak" is a narrow, non-generalized technique that merely involves identifying minor, known software vulnerabilities—a capability common to other publicly available models like OpenAI's GPT-5.5 and routinely used by cybersecurity defenders. Anthropic stated it has complied with the order but disagrees with the government's standard, warning that applying it industry-wide would halt all new frontier model deployments. The company criticized the lack of a transparent, fact-based legal process and expressed confidence the situation stems from a misunderstanding. It is working to restore access and will release more technical details within 24 hours. Other Anthropic models remain unaffected.

链捕手7m ago

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

链捕手7m ago

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

**Raydium Exploit Reveals DeFi's Hidden Risk: Forgotten "Zombie" Contracts** A recent attack on Raydium's deprecated V3 AMM pools resulted in a loss of approximately $1.34 million. The hacker exploited pools that were no longer supported by Raydium's current UI or SDK but remained fully functional and accessible on-chain. This incident highlights a critical, often overlooked category of risk in DeFi: inactive or legacy smart contracts that projects fail to properly decommission. Since March 2025, there have been at least 8 publicly reported attacks targeting such abandoned contracts, with total losses around $10.8 million. Including older pools and deprecated features, the count rises to 10 incidents with roughly $22.5 million in losses. These "zombie contracts" represent a lifecycle management failure rather than a code vulnerability, yet they are typically misclassified under general "code bug" categories in security reports, masking the true scale of the problem. The root cause is that projects often merely document a contract as "deprecated" without taking essential technical steps to secure it: withdrawing remaining assets, disabling external call functions, and implementing ongoing monitoring. These forgotten, under-monitored components become prime targets for attackers. To address this, the industry needs to recognize "zombie contracts" as a distinct risk category and establish standardized decommissioning protocols. Essential steps should include: 1) a formal retirement announcement, 2) removal of all front-end integrations, 3) withdrawal of locked assets, 4) disabling key contract functions, 5) ongoing security monitoring, 6) clear user communication, and 7) a post-mortem analysis. The value of a DeFi project lies not only in its current TVL but also in the security of its historical codebase, which has now become a new attack surface.

Foresight News1h ago

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

Foresight News1h ago

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbit4h ago

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbit4h ago

Trading

Spot
Futures
活动图片