Motley Fool 分析师蕾切尔·沃伦（Rachel Warren）邀请了连续创业者、前麦肯锡（McKinsey）顾问、入驻企业家、早期风险投资基金的风险合伙人以及哈佛创新实验室（Harvard Innovation Labs）研究员罗布·斯奈德（Rob Snyder），讨论他的新书《拉动之力》（The Power of Pull）。斯奈德的研究挑战了传统的经济需求模型，认为许多常见的增长信号可能会误导投资者。 斯奈德解释说，在他第一次创业公司陷入困境后，他对需求的理解发生了根本性转变。他最初认为，提供明确的价值和投资回报（ROI）自然会带来销售。然而，在两年销售不佳后，一位餐馆老板给他打电话，不是为了他的产品，而是特别寻求关于当前优先事项的帮助。这次互动表明，购买者并不总是按预期行事；他们不会仅仅被逻辑说服。当他与这些紧迫的客户需求保持一致时，他的创业公司才腾飞，两年内收入从零增长到四百万。 《拉动之力》的核心主题区分了“推式”（push）需求和“拉式”（pull）需求。“推式”需求发生在卖方必须说服、劝导并不断跟进买方的时候。斯奈德在尝试销售他*认为*市场需要的东西时，就经历了这一点。相反，“拉式”需求发生在买方主动发起购买，积极寻求甚至“拉走”卖家手中的产品时。这种真正的需求产生于买方有当前“项目或优先事项”，而现有选择无法充分解决之时。如果他们现有的选择“足够好”，他们就不会购买，无论新产品的价值主张多么引人注目。 斯奈德认为，传统的、通常被视为图表上可预测曲线的需求模型之所以失败，是因为它们没有考虑到由特定生活事件或紧急优先事项驱动的个体购买决策。投资者常常误解营收增长，以为其代表市场契合度，而实际上这可能只是通过激进的销售和营销“制造”出来的。对于真正的产品与市场契合度，斯奈德强调观察买家行为：他们是被说服的（推式），还是积极寻求解决方案的（拉式）？真正的拉式需求带来长期的留存和参与度，客户使用产品“仿佛他们不能不用它”。 他强调，单凭“引人注目的价值主张”是不够的。在数百万种产品可供选择的情况下，只有当价值主张与买家当前的紧迫优先事项相关时，它才重要。许多杰出的产品之所以失败，是因为客户虽然认可其价值，但根本没有把它们解决的问题列为优先事项。 回顾他作为连续创业者的经历，并观察哈佛创新实验室的数百家初创公司，斯奈德指出早期成功往往“与传统理论相悖”。它不总是关于完美的产品或激进的销售话术。他分享了一个例子，他的初创公司用一个简陋的产品（他自己就是电子表格）和一个简单的销售演示就创造了最初的十万到二十万美元的收入。同样，他认识一位欧洲创始人，其销售电话糟糕透顶，甚至在电话中读GDPR法规，但客户“无论如何都渴望购买”。根据斯奈德的说法，早期成功的标志是“尽管如此，而非因此而购买的人”。这些客户在做某事时受阻，并将该产品视为他们唯一可行的前进之路。之后才聘请有才华的团队来完善产品、定价和销售流程。 对于投资者，斯奈德建议不要只看由销售和营销大量投入支撑的营收增长，那表明是“推式”需求。相反，要寻找销售和营销工作致力于转化那些*已经*有需求的人的公司。结构性需求的一个关键财务信号，尤其是在B2B软件领域，是“净收入留存率”（net revenue retention）。这一指标表明客户不仅在购买，而且正在发现越来越多的价值，更多地使用产品，并支付更多费用，而不是流失。他将这些“揭示性偏好”（实际使用情况）置于“声明性偏好”（如NPS分数）之上。 关于人工智能（AI）热潮，人工智能云基础设施公司 restack.dev 的联合创始人斯奈德解释说，“拉式”框架高度相关。许多企业的新“选择”是“使用 Claude 或 Codex（大型语言模型）自行完成”。人工智能初创公司必须识别出这些通用人工智能选项“不够好”的地方，并且即使未来模型改进，它们也仍然不足。他对那些解决特定小众痛点的“简单而平凡”的人工智能解决方案感到兴奋，例如医药领域的专业市场研究、商业保险报价比较，或为财务顾问提供符合规范的会议记录工具（例如 Jump）。这些解决方案消除了客户已经在做但效率低下的任务。 在评估人工智能初创公司时，斯奈德高度关注注册后和购买后的指标，如留存率和使用率，以确保其在最初热潮过后的持久性。在快速发展的人工智能领域，与客户保持紧密联系，不断为他们排除障碍，并扩展产品套件，对于保持“持久的顺风”并领先于大型人工智能实验室和其他初创公司的竞争至关重要。

Motley Fool analyst Rachel Warren welcomed Rob Snyder, a serial startup founder, former McKinsey consultant, entrepreneur in residence, venture partner for early-stage venture capital funds, and a fellow at the Harvard Innovation Labs, to discuss his new book, "The Power of Pull." Snyder's work challenges traditional economic models of demand, arguing that many common growth signals can mislead investors. Snyder explained that his understanding of demand fundamentally shifted after his first startup struggled. He initially believed that providing clear value and ROI would naturally lead to sales. However, after two years of low sales, a restaurant owner called him, not asking for his product, but specifically for help with a current priority. This interaction revealed that buyers don't always behave as expected; they aren't convinced by logic alone. His startup took off when he aligned with these urgent customer needs, growing from zero to four million in revenue in two years. The core theme of "The Power of Pull" differentiates between "push" and "pull" demand. "Push" occurs when a seller has to convince, persuade, and relentlessly follow up with a buyer. Snyder experienced this when trying to sell what he *thought* the market needed. In contrast, "pull" happens when a buyer initiates the purchase, actively seeking out and even "pulling" the product out of the seller's hands. This genuine demand arises when buyers have a current "project or priority" that their existing options cannot adequately address. If their current options are "good enough," they won't buy, regardless of how compelling a new product's value proposition might be. Snyder contends that traditional demand models, often seen as a predictable line on a chart, fail because they don't account for individual purchasing decisions driven by specific life events or urgent priorities. Investors often misinterpret revenue growth, assuming it signifies market fit, when it could just be "manufactured" by aggressive sales and marketing. For true product-market fit, Snyder emphasizes observing buyer behavior: are they being convinced (push) or actively seeking solutions (pull)? Genuine pull leads to long-term retention and engagement, where customers use the product "as if they can't not use it." He stressed that a "compelling value proposition" alone is insufficient. With millions of products available, a value proposition only matters if it's relevant to a buyer's immediate priority. Many brilliant products fail because customers, while acknowledging their value, simply aren't prioritizing the problem they solve. Reflecting on his experience as a serial founder and observing hundreds of startups at Harvard Innovation Labs, Snyder noted that early success often "contradicts conventional theory." It's not always about a flawless product or an aggressive sales script. He shared an example where his own startup generated its first $100,000-$200,000 in revenue with a rudimentary product (him in a spreadsheet) and a simple sales deck. Similarly, he knows a European founder whose sales calls are notoriously bad, even reading GDPR regulations, yet customers are "desperate to buy regardless." The hallmark of early success, according to Snyder, is "people who are trying to buy despite, not because of." These customers are blocked in doing something and see the product as their only viable path forward. Talented teams are then hired later to refine the product, pricing, and sales process. For investors, Snyder advises looking beyond revenue growth that is heavily funded by sales and marketing, which indicates "push." Instead, look for companies where sales and marketing efforts are dedicated to converting people who *already* have demand. A key financial signal for structural demand, especially in B2B software, is "net revenue retention." This metric indicates that customers are not just buying, but are finding increasing value, using the product more, and paying more, rather than churning. He prioritizes these "revealed preferences" (actual usage) over "stated preferences" like NPS scores. Regarding the AI boom, Snyder, co-founder of AI cloud infrastructure company restack.dev, explained that the "pull" framework is highly relevant. The new "option" for many businesses is to "do it themselves with Claude or Codex" (large language models). AI startups must identify where these generic AI options are "not good enough" and will continue to be insufficient even with future model improvements. He's excited by "simple and mundane" AI solutions that unblock specific, niche pain points, like specialized market research for pharmaceuticals, commercial insurance quote comparisons, or compliant meeting note-takers for financial advisors (e.g., Jump). These solutions eliminate tasks customers were already doing, but inefficiently. When evaluating AI startups, Snyder focuses heavily on post-signup and post-purchase metrics, like retention and usage, to ensure durability beyond initial hype. In the rapidly evolving AI landscape, staying close to customers, continuously unblocking them, and extending product suites are crucial for maintaining "durable tailwind" and staying ahead of competition from both large AI labs and other startups.