2026 年的世界级 GTM 是什么样的 | Jeanne DeWitt Grosser（Vercel、Stripe、Google）
2026 年的世界级 GTM 是什么样的 | Jeanne DeWitt Grosser（Vercel、Stripe、Google）
文字记录
Lenny Rachitsky： 我一直收到很多关于市场进入策略方面的求助。
Jeanne DeWitt Grosser： 在 AI 时代，这种情况只会更加激烈，因为可能有十家公司在追逐同一个市场机会，所以你将产品推向市场、与竞争对手差异化的能力，在战略上变得比以往更加重要。
Lenny Rachitsky： 我最近在播客上邀请了 Jenna Abel，她的一个建议是：不要把重点放在”我们解决的痛点和问题”上，而要聚焦于”你将如何比竞争对手做得更好”。
Jeanne DeWitt Grosser： 80% 的客户购买是为了避免痛苦或降低风险，而不是为了增加收益，这是创业创始人需要理解的很重要的一点。我们都喜欢谈论各种可能性，谈论未来我们将赋能实现的一切，但这种说法往往只对其他创始人有吸引力。对于其他人，尤其是企业客户来说，你在帮他们避免的是下个季度完不成收入目标的风险。
市场进入策略即产品
Lenny Rachitsky： 我听很多人谈起过你如何将市场进入策略当作产品来思考。
Jeanne DeWitt Grosser： 我们购买很多东西是因为我们对它们的感受。如果产品之间的差异仅在商家端，那么被推销的体验将日益成为区分公司的关键，并驱动购买决策。因此你真正需要做的是，创造一个让客户感觉非常独特的购买旅程。
Lenny Rachitsky： 我从很多与你共事过的人那里听到，你的超能力是建立一个对工程师来说不像销售组织的销售组织。
Jeanne DeWitt Grosser： 我一直给销售团队设定的试金石是：如果你是我团队中的客户经理（account executive），我把你放在我们公司十位工程师面前，他们需要花十分钟才能发现你不是产品经理。
嘉宾介绍
Lenny Rachitsky： 今天我的嘉宾是 Jeanne Grosser。Jeanne 曾是 Stripe 的首席产品官，在那里她从零开始组建了他们最早的销售团队。她目前是 Vercel 的首席运营官，负责市场营销、销售、客户成功、收入运营和现场工程。Jeanne 在多家独角兽公司构建了世界级的市场进入策略团队，并指导了数十家公司做同样的事。在我们的对话中，我们深入探讨了世界级的市场进入策略团队是什么样的，包括市场进入策略到底是什么、市场进入策略工程师（go-to-market engineer）的兴起以及这个角色如何让她的团队已经以十倍速运转，还有很多非常具体的提升你市场进入策略技能的战术，关于客户细分的基础入门，如何像做产品一样思考你的市场进入策略流程，她最喜欢的市场进入策略工具，她对 PLG、销售薪酬和销售招聘的热门观点，以及更多内容。如果你想了解最新最好的市场进入策略思维，这期节目就是为你准备的。
非常感谢 Claire Hughes-Johnson、Kate Jensen 和 James Ditt 为这次对话建议了话题，感谢 Kelly Schafer 牵线搭桥。如果你喜欢这个播客，别忘了在你最喜欢的播客应用或 YouTube 上订阅和关注，这对我帮助很大。
什么是市场进入策略？
Lenny Rachitsky： Jean，非常感谢你来参加节目，欢迎来到播客。
Jeanne DeWitt Grosser： 谢谢你的邀请，Lenny。
Lenny Rachitsky： 我希望这次对话结束时能达到的目标是——基本上让这期节目成为当人们说”我想提升市场进入策略能力，我正在想办法做好市场进入策略”时我们发给他们的东西。我们直接发这个给他们，而不是花大价钱请人，而且通常他们也找不到优秀的人才，因为那些人都被抢光了。那就从最基础的开始吧。当人们听到”市场进入策略”这个术语时，它意味着什么？它包含哪些内容？
Jeanne DeWitt Grosser： 我认为这个问题有两个答案。人们通常想到的是驱动收入的矛头，也就是市场营销和销售。对我来说，我认为它是任何会接触客户或创造收入的职能。事实上我在 Vercel 的职责范围就是如此——这包括市场营销、销售、所有技术销售角色比如销售工程师，或者售后平台架构师——我们在 Vercel 是这么叫的。还包括客户成功、技术支持、合作伙伴关系。我这么说的原因是，在我整个职业生涯中的经验是，这些职能往往采取维恩图式的策略——市场营销在追求一个方向，它与销售追求的方向有重叠但并不完全一致，又与支持团队的方向有重叠但也不完全一致。这方面的例子包括各团队之间略有不同的客户细分框架等等。
市场进入策略的生命周期整合
Jeanne DeWitt Grosser： 因此我认为在当下这个阶段，你会越来越希望看到它成为一个真正整合的生命周期。特别是，我认为我们将看到市场进入策略中的许多职能被重新定义——我们经历了一个市场进入策略极度细分的时期，取决于你怎么算，现在大概有17种不同的市场进入策略角色，我推测其中很多将会开始合并。所以如果你更整体地看待市场进入策略，我认为你可以回到那些”待完成的任务”——从让潜在客户知道你的产品，一直到高LTV（客户生命周期价值），在平台上使用了五年，全面覆盖。你会想要把这个过程梳理出来，像编排自己产品内部流程那样去编排它。
Lenny Rachitsky： 太好了。我们后面会走完整个市场进入策略的周期，但对于大多数公司，尤其是刚起步的公司来说，当它们说”市场进入策略”的时候，是不是可以认为主要就是销售，然后市场营销可能占一个较小的部分，随着你变得更成熟、规模更大，客户成功、技术销售之类的才会加入进来？
Jeanne DeWitt Grosser： 对，大多数公司确实从销售开始，或者坦率地说，因为很多公司也从 PLG（产品驱动增长）起步，你可能实际上从市场营销开始，然后在需要做销售辅助、最终进入销售主导的阶段时，再叠加销售。所以我认为取决于你的产品和初始目标市场，它可以是市场营销，也可以是销售，或者两者的结合。
Lenny Rachitsky： 明白。所以本质上”市场进入策略”这个词本身就在告诉你我们在讨论什么——你如何把产品推向市场，让人们了解它、使用它、持续使用它？
Jeanne DeWitt Grosser： 没错，完全正确。
市场进入策略近年来的最大变化
Lenny Rachitsky： 过去几年里市场进入策略领域最大的变化是什么？你在 Google、Stripe 都做了很长时间，为 sales team 搭建了相关体系，现在又在 Vercel 做这件事。在市场进入策略的技能和艺术方面，变化最大的是什么？
Jeanne DeWitt Grosser： 有好几方面。当基于消费的商业模式兴起时，我认为市场进入策略明显变得更加咨询化，因为那首次着陆往往只是整个旅程的起点，只代表你最终与那个客户合作的很小一部分。所以你必须从交易型转向更深入的模式——你必须更深入地理解客户想做什么，才能最终与你的产品对齐。我觉得这个趋势在 AI 时代被进一步放大了，因为现在每个人都知道自己需要改变，但不一定清楚该变成什么样——不管是面向客户的产品，还是内部的生产力和工作流。因此我认为你会看到越来越多的市场进入策略组织倾向于展示”可能性”和最佳实践，像一个顾问一样帮你真正把事情想清楚。
前线部署工程与市场进入策略工程师
Jeanne DeWitt Grosser： 你现在会看到更多前线部署工程的做法，这在某种程度上像是专业服务的重新包装，但又不完全是。它的核心是：我如何真正进入你的环境，与你并肩工作，更好地理解你在做什么，然后帮你把技术真正落地，并在这个过程中学到很多东西。通常你不仅是在让那个客户成功，还会把所有这些经验带回你的产品和工程团队，去判断——什么是可泛化的、应该内置到我们的产品中的，什么在长期来看更适合作为专业服务来做。所以我认为这是一个重大变化——真正深度嵌入客户之中。然后不出意料地，将 AI 应用于销售流程本身是另一个重大变化。在过去18到24个月里，你看到了市场进入策略工程师的兴起，不同的人对其定义略有不同，但核心是两方面：一是把技术能力引入市场进入策略的各个方面，从而获得更好的工具、数据利用等；二是越来越多地用 AI 来重新设计工作流，同时让大规模地为客户提供个性化体验变得更容易。
Lenny Rachitsky： 太棒了。好，我们沿着市场进入策略工程师这条线继续——以前是什么情况，这些工程师在公司里具体在做什么？
从 Stripe 的项目 Rosalind 到 AI 驱动的拓客
Jeanne DeWitt Grosser： 我觉得可能讲一个有趣的故事。我在 Stripe 的时候，我们准备启动一个外呼 SDR（销售拓展代表）职能，就是外呼拓客。Stripe 一直保持精简运营。当时公司有一条运营原则叫”效率就是杠杆”。如果你看我当时负责的销售团队，大多数公司可能配30个 SDR，而我只能拿到4个。所以我根本不可能用典型的 SDR 方法来取得成功。于是我们想，好吧，我们能做什么？我们要做到数据驱动。所以我们开始构建项目 Rosalind。Rosalind 是最初绘制 A-DNA 结构图谱的科学家。这个项目本质上是一个公司全景数据库——你可以把它想象成一个巨型数据库，每一行是地球上的一家不同公司，每一列是关于那家公司的一个属性，帮助你更有针对性地向他们销售。在 Stripe，一个例子是知道他们的商业模式是 marketplace 会非常有帮助，因为那就意味着你应该卖 Stripe Connect 而不是基础的支付产品。所以目标基本上是：我们能否创建一个类似 Mad Libs（填词游戏）的系统——我来设计一个预定义的邮件模板，但其中80%的内容根据客户的不同属性填空。如果他们是这个行业或这种商业模式，就引用这个客户案例、强调这个价值主张，发给这个角色而不是那个。我们在2017年就尝试做这件事，但非常困难，实际上没有完全跑通——误报率太高，我们与 DSI 深度合作，但始终没有真正达到理想效果。而现在我们正在 Vercel 重新做这件事，而且它真的能用了，你可以用 AI 来驱动它。
Jeanne DeWitt Grosser： 不同之处在于，现在我和2017年一样有一位数据科学家，但我还有一位市场进入策略工程师——而以前我只有一个帮我在 Outreach 或 SalesLoft 里做配置的系统人员。我的市场进入策略工程师在帮我构建一个智能体——我们先确定人类原本会执行的工作流，然后用 Vercel workflows 之类的工具将其编码为实际代码，既包含确定性逻辑，也包含非确定性部分——由智能体出去尝试复制人类可能完成的操作，来生成那种填空式的内容。
Lenny Rachitsky： 我太喜欢那个项目的野心了。那是什么，大概八年前？
Jeanne DeWitt Grosser： 对。
Lenny Rachitsky： 我喜欢那种大格局的思维——“我们要绘制整个公司宇宙的图谱，然后这样向他们销售。“然后我在脑海中想象没有 AI 来做这件事的画面——简直难以想象没有 AI 怎么尝试做这个，而现在用 AI 来想象却简单太多了。
从 Stripe 到 AI 原生的市场进入策略公司
Jeanne DeWitt Grosser： 说来很有意思，稍微展开聊聊——当时我在 Stripe 带领团队和一批人一起做这件事，其中有一位叫 Ben Salzman 的同事，后来去了 ZoomInfo，最近又创办了一家市场进入策略的创业公司，核心思路就是把”公司宇宙”这个概念产品化，然后在上面叠加 AI。他的终极判断是，AI 最终会发展到你根本不需要做外呼拓客的地步，因为它会自动完成公司与产品的匹配。所以回头看很有趣——2017 年一起做这件事的人，有的现在在 OpenAI，有的在 Anthropic，也有人在做市场进入策略工程。Ben 创办了一家完全 AI 原生的市场进入策略公司，而我则在 Vercel 做着类似的事情。
市场进入策略工程师的完整职责
Lenny Rachitsky： 好，我觉得很酷的一点是，这是一个正在涌现的角色、一种正在涌现的技能，我认为很多人还没有意识到这件事正在发生。我听到的一个例子是，这个角色做的事情本质上是自动化外发邮件和外呼触达——他们设计工作流和智能体，确定应该瞄准哪家公司、如何传递信息。最终产出的会不会就是一封针对该潜在客户量身定制的邮件？
Jeanne DeWitt Grosser： 那是其中一种形式，但实际范围比那更广。市场进入策略工程的完整职责是逐一审视市场进入策略中的各个职能，拆解它们所做的所有工作流，然后把那些 AI 比人类更适合完成的任务转化为智能体。我们实际上是从 inbound 开始的，现在正在向 outbound 推进，因为 inbound 的工作流最清晰可读——所谓清晰可读，就是你能把它基本写下来，相对可复制，大部分是确定性的，所以 AI 更可能做好。我们搭建了智能体，并且保持人类在环。从那之后我们开始看外呼，先从市场低端切入，那里的定制化程度通常较低，因为公司只有一个决策者。
不过我认为要真正在大型企业中做到这一点还需要一段时间。在大型企业场景中，我们可能会用智能体做调研，但不一定会一路走到实际发送消息。而这还只是在拓客职能内部。我们正在探索的其他方向还包括基于已安装产品的销售——在这方面情况也相对确定，因为你拥有很好的内部数据，知道客户在使用什么、没在使用什么，下一步最佳行动是什么，他们从哪里能获得最大价值。所以我们开始梳理理想的流程是什么样的。
让销售人员真正花时间与客户对话
根本上你想要达到这样一种状态——在我从事销售的这些年里，行业每年都会发布年度报告，帮助大家互相做对标。其中一个统计指标是：你的销售人员实际花多少时间面对客户？在我从事销售的二十年里，这个数字一直在 30% 到 40% 之间，也就是说实际与人交流的时间是少数。我认为我们正在到达一个转折点——通过引入智能体，理想情况下我们终于能让销售人员把 70% 的时间花在与人的互动上，把调研、跟进以及那些比较机械化、不需要发挥人全部能力的工作交给智能体，从而解放销售人员去与客户做更深层次的交流。
如何避免 AI 邮件沦为垃圾
Lenny Rachitsky： 我很喜欢这个例子，它很好地展示了 AI 在高投资回报率方向上所做的贡献——承担了那些人们……比如你说的，以前需要招 50 个 SDR 才能完成的工作，现在用更少的人就能做到更多。这是一个 AI 赋予你杠杆效应的绝佳案例。不过我知道很多人听到这些时会想：好吧，我又要收到更多那种糟糕的推销邮件了，这种做法不会奏效的，我一眼就能看出这是 AI 写的。关于如何做到让人们收到的邮件真正有效、能够转化，你有什么心得？
Jeanne DeWitt Grosser： 我们的流程始终都有人类在环。具体做法是，我们让一位市场进入策略工程师去跟班观察该职能中表现最好的人。比如你去跟班一位 SDR，你会看到：哦，他们开了七个标签页，在 LinkedIn 上查这个人，在了解这家公司，同时在用 ChatGPT，又在查某个数据库获取这些属性。你就是这样来初步构建工作流的。然后我们让智能体来做判断。具体到 inbound 的例子，你需要判断这个线索是否可能合格，然后确定对它说什么。我们让智能体做这两个判断。
接下来它会做一些深度调研，从我们的数据库中拉取大量信息并撰写回复，但我们会让一个人审核所有这些内容，并由那个人实际点击发送。对我们来说，之前有 10 个 SDR 做这个 inbound 工作流，现在只需要一个，实际上就是在给智能体做质量审核。另外九个人我们调去做外呼了，相当于把他们沿着价值链往上移。到某个阶段，我想我们会到达这样一个状态：人类审核员说”可以”的频率足够高，我们有信心这些回复符合品牌调性、定位精准等等。但现在我们仍在训练智能体，它会根据我们选择拒绝或修改的内容来吸收反馈。
实战成果：六周内从 10 名 SDR 减至 1 名
Lenny Rachitsky： 而且你分享过，它已经产生了很大的效果——就像你说的，之前有 10 个 SDR，现在一个人就能做 10 个人的活。
Jeanne DeWitt Grosser： 在做这个调整之前，还有一件事令人惊叹——搭建线索智能体的只是一位市场进入策略工程师，他大概花了 25% 到 30% 的时间在这上面。大约六周后，我们就确信可以从 10 人减到 1 人。这不是一个跨越多个季度的大项目，推进速度非常快。然后就像我说的，我们现在就让那个智能体管理员持续和智能体协作，把它调教到我们觉得”好了，可以正式推出”的状态。实际上在整个过程中，我们也追踪了所有通常用来考核 SDR 的 KPI——线索到商机的转化率、所需的触达次数、转化所需的时间。最终我们做到了把线索到商机的转化率保持在不下降的水平，也就是说智能体的表现和人类一样好，而且它实际上压缩了转化所需的触达次数，因为它响应速度快得多——而人工处理时线索难免会在队列中等待，或者在夜间进来没人能及时处理，诸如此类。所以那就是我们判断可以撤出九个人、把他们转到外呼的时间点。
Lenny Rachitsky： 太不可思议了。好的，很有意思。所以你把这些 SDR 转到了外呼方向。我很喜欢这个做法的地方在于，就像你说的，现在留在这个岗位上的人在做他们更喜欢的事情，更多地和客户交谈，而不是做那些漏斗顶部的重复性工作。我不想扯到”AI 取代所有工作”那个大话题里去，但一直都有人在说 AI SDR 会基本上取代人类 SDR。这件事感觉是那种所有人都觉得”未来百分百会由 AI 来做”的领域。但我现在听到的是，这给了一个人——一个 Aster 的员工——大得多的杠杆，而且显然你还是需要有人来掌舵。你怎么看？你觉得 AI 会在某个时刻完全替代这些工作吗？然后就不再需要销售了？
Jeanne DeWitt Grosser： 我认为在拓客方面，AI 确实能替代相当大一部分工作，因为普通的 SDR 本来也没在做多么复杂的研究。所以我认为最后被替代的环节，正如我之前提到的，会是深度的企业级拓客——你需要同时触达组织架构中的多个层级，要在不同的业务线之间做出选择，需要进行三角定位。但我确实认为，对于那些更重复性的、通常不需要太多时间学习和上手的工作，AI 会表现很好。而且在我看来，没有人大学毕业的时候会说：“太好了，我上了四年大学就是为了当 SDR。“更多的是，“好吧，这是你不得不从这儿开始。“但我认为普通的 SDR 其实完全可以直接去做外呼，或者直接进入面向中小企业的成单角色。所以我们基本上做的事情就是，让人员从一开始就能发挥更多潜力，而不是被迫按照传统的论资排辈一步步往上爬。
Lenny Rachitsky： 太好了。因为这期播客的很多听众不是销售人员，对销售没有太多背景知识，我们用了 SDR 这个词，还有 AE 这个词。你能不能帮大家理解一下什么是 SDR，他们做什么，什么是 AE，以及再往上的角色是什么？
Jeanne DeWitt Grosser： 当然。SDR 通常负责生成 pipeline。他们的工作是和潜在客户交谈，把他们推进到值得投入时间启动正式销售流程的程度。SDR 通常分两种：一种是 inbound 的，就是人们来到你的网站，填写了”联系销售”的表单，SDR 会打第一个电话，确认这个线索是否值得让成本更高的客户经理去跑销售流程；另一种是 outbound 的，就是当你想比 inbound 需求增长得更快时，他们会主动出击，到这个时候你大概已经对产品市场契合点在哪里有了自己的判断，所以他们就会去针对那部分市场，试图激发那些原本没有主动举手说”我想跟你们聊聊”的人的兴趣。
所以销售拓展本质上就是——pipeline 生成。客户经理则是 closer，即成单者。他们的工作是把一个人从”好的，我有兴趣了解你们的方案，我确实有问题需要解决，我可能会做出购买决策”，推进到”我现在相信你们的产品对我来说是市场上最好的选择，而且我愿意为此付费”。然后客户经理会根据公司销售的客户细分——比如中小企业、中端市场、企业级等等——沿着这条链向上发展。从卖给小公司如中小企业或初创公司开始，这种通常更偏交易型销售，往往只有一个决策者；然后进入中端市场或商业级角色，这时你可能要面对一个经济买家——比如财务部门的人——和一个技术买家——比如工程部门的人；再到企业级销售，就要面对采购部门、委员会，十个人都要发表意见，你还得帮他们想清楚如何降低风险，因为他们很可能正在从某个现有系统迁移过来——协调的复杂度大大增加，销售难度也高得多。
Lenny Rachitsky： 这段解释太有帮助了。SDR 负责 pipeline 生成，客户经理负责成单。这么理解真是太简洁了。好，回到市场进入策略工程师这个话题，对于想在自家公司尝试的人，我有几个问题——你认为公司发展到什么规模时，开始招聘这个角色是合理的？让一个人来在市场进入策略流程中做自动化？
何时引入 GTM 工程师
Jeanne DeWitt Grosser： 这件事有趣的地方在于，它会迫使公司在早期就对销售流程更加严谨。通常初创公司从创始人主导销售过渡到——比如”我要招第一个销售人员”的时候，不管是有实际销售经验的真正的客户经理，还是那种全能型聪明人自己去摸索——创始人往往只会说：“好吧，销售不就是露面跟人聊天嘛，我过去几年不就是这么干的吗？“但实际上销售远不止于此。它是一项技能，就像写代码是一项技能、搭建财务模型是一项技能一样，它关乎发现——提出所有正确的问题来帮助你识别客户的痛点和挑战、付费意愿等等，然后通过一个流程来处理这些异议，展示你的价值是否足够让一个人最终愿意掏钱。
所以很多初创公司，特别是产品市场契合度很强的那些，往往能发展到相当大的规模却仍然没有一个可复制的流程。而你如果没有对”最佳实践应该是什么样的”有自己的判断，就很难应用市场进入策略工程。所以我认为基本上这会迫使人们更早地建立一套方法论——什么有效、什么无效？我能不能把它文档化？我有没有覆盖销售流程各个阶段的内容？一旦你做到了这些——大概 10 个人的规模可能是一个不错的节点——理论上一个市场进入策略工程师就可以进来，把这些流程变成智能体。你也可以说，如果你是一个创始人，想招一个全能型选手，而这个人又具备技术思维，那你可以有一个客户经理兼市场进入策略工程师的混合角色——他先摸索出最佳实践，然后尝试把它转化为一个和他并肩作战的智能体，让他自己也更高效。
所以我不确定我对最佳规模和时机是否已经有明确观点，但我一直给创始人的建议是——你通常应该比你以为的更早引入 revenue operations，它基本上是销售的分析支撑，因为拥有数据、拥有流程，实际上才是作为创始人洞察哪些有效、哪些无效的关键。所以我会说，就像更早引入 revenue operations 是个好主意一样，越来越早地拥有市场进入策略工程师、从一开始就让智能体参与你的流程，大概也会成为一个好主意。
何时招聘第一个销售人员
Lenny Rachitsky： 趁着聊这个话题，快速岔开一下——关于招聘第一个销售人员，我通常听到的建议是等到 ARR 达到 100 万美元左右再招。等到你有了可复制的流程，可以教给别人。这听起来对吗？你会怎么建议？
Jeanne DeWitt Grosser： 我觉得这个说法大致是对的。我确实认为，作为创始人，你需要与客户保持深度连接，把业务做到一定规模，达到你所说的那种——有了一定的可复制的程度。我认为并不是所有创始人都能做到这一点，创始人其实是非常出色的销售人员。他们说服了 VC、天使投资人掏出一大笔钱，显然他们也能打动别人购买产品。但如果你做到了 100 万 ARR，而你的客户群体彼此之间毫无相似之处，那你的销售仍然很大程度上是一种布道式销售、非常依赖创始人主导的销售。反过来，如果你能说：“我现在有了 ICP（理想客户画像），一种可以写下来的东西——我们的产品适合员工人数在 100 人以下、通常在构建 SaaS 应用的初创公司”，类似这样的表述，那你就差不多可以把接力棒交出去了。而创始人需要记住的是——要真正把接力棒交出去。你需要赋能接手的人：你哪些做法是有效的，你的内容是什么，你在 discovery 中问了哪些问题，你是如何处理异议的——这样你才能把这些知识传递下去。但也不要完全交接，你要继续与客户保持连接，因为你还有相当多的研发工作要做——弄清楚产品下一步会在哪里产生共鸣，在扩张过程中你会在哪里遇到瓶颈，等等。
市场进入策略工程师的理想画像
Lenny Rachitsky： 把市场进入策略工程师这个话题收个尾——理想的市场进入策略工程师是什么画像？也许是你招的第一个。
Jeanne DeWitt Grosser： 我们发现效果很好的是，有一定市场进入策略经验的人。在 Vercel，我们前三个市场进入策略工程师其实都是 sales engineer（销售工程师）。Vercel 招的销售工程师技术背景非常强，他们在决定进入销售领域之前都是前端开发者。所以我们直接跟他们说：“恭喜你们三位，你们现在是 GTM Eng 团队的创始成员了。” 这种安排的好处在于，你确实理解什么是好的市场进入策略、流程应该长什么样。说起来其实很有意思——我负责 GTM Eng 的那位同事，我们当时在审查 lead agent（线索智能体）并做质量检查。我在看智能体最终发出的回复时意识到：“哦，我不会发这条消息。“那是因为我有 20 年的销售经验，而我们建模的 lead agent 是以我们最优秀的员工为模板的，但那位最优秀的员工只有两年的销售经验。所以理解销售中的艺术与科学、把最佳实践运用进来，确实非常重要。要么你自己做过，所以知道一些最佳实践；要么你就对销售疯狂钻研，读一堆书，学一些东西，然后尝试把这些融入到你的智能体开发中。
Lenny Rachitsky： 这真的很有意思。所以应该从销售侧出发，而不是从工程侧出发。我觉得这对销售人员来说也是一个非常酷的机会——做一些完全不同的事情，离工程更近一步。
Jeanne DeWitt Grosser： 是的，我们确实玩得很开心。特别是在 Vercel，我们基本上就是自己的第一个客户。我们用智能体构建的所有东西，都跑在 Vercel 的 AI 云上。这些智能体现在有多个执行步骤，我们使用的是 Vercel 的 workflow SDK 和 workflow 产品。我们用 AI gateway 来调用不同的模型，做深度研究或其他数据增强。所以对我们来说特别好，因为我们基本上是在工程团队构建的所有东西上一顿猛踩，在真正交付给外部客户之前先当一个挑剔的内部客户。
Lenny Rachitsky： 活在这个时代真有趣。从你描述的方式就能感受到你们玩得多开心。
市场进入策略工具栈
Lenny Rachitsky： 稍微拉远一点——你提到了工具，你使用的工具。我很好奇，在市场进入策略工具栈中，你觉得有哪些业界领先的工具是你喜欢并愿意推荐的？
Jeanne DeWitt Grosser： 我会给你一个有点不一样的回答。先说一个，它本身不算是”最前沿”，这么说没有任何贬义——只是它已经存在了一段时间，很多人在用——但我认为 Gong 在过去一年里变得明显更有意思了。然后我回答的后半段会聊到，我认为 build 与 buy 的权衡正在发生变化。先说 Gong。Gong 非常厉害，因为你现在可以在它上面运行智能体了。我们把所有的 Gong 转录文本导入一个叫 deal-bott 的智能体，这个 deal-bott 能做很多事情。我们首先让它做的是 lost opportunity review（失利机会复盘）。我们刚结束 Q2，有一份按 deal 大小排序的季度最大失利清单，我们用它跑了分析，结果非常有意思。
那个季度最大的一笔失利，客户经理（account executive）给出的原因是价格。但当你让智能体跑过每一条 Slack 互动、每一封邮件、每一通 Gong 通话后，它说你实际上输掉的原因是你从来就没有接触到经济买家（economic buyer）。而当你跟对方谈 ROI 和总拥有成本时，从他们的反应可以清楚看出，他们并没有真正买单。所以我们真正输掉的原因是无法展示价值——回想起来，我确实还有工作要做，要把 Vercel 的价值量化体系建起来。实际上这个价值是非常容易量化的，这也是我非常喜欢卖这个产品的原因之一，但我们需要把它为整个市场进入策略团队形成一套体系。这个发现极其有价值。现在我们对所有失利的商机都运行这个分析，在归因”我们到底为什么输掉”方面做得好多了。
然后把结果反馈给工程团队，或者反馈给市场营销和销售管理层，指出我们在销售流程中哪里存在短板。这个功能非常棒。但后来我们想，输了毕竟不开心，那为什么不把这个机制前移呢？于是我们从 lost bot 升级到了 deal-bott，现在 deal-bott 是实时运行的，基本上把洞察推送到 Slack 里。Vercel 是 Slack 的重度用户，我们为每一个客户——无论是潜在商机还是现有客户——都有一个专门的 channel。现在我们往那个 Slack channel 里推送的洞察是这样的：“嘿，你的销售流程已经走到这一步了，但还没有跟经济买家谈过，你可能要考虑一下这件事。“或者：“嘿，你刚跟经济买家通了电话，听起来进展不太顺利。以下是一些值得考虑的事项以及后续跟进的建议。“
用智能体诊断销售流程中的”Bug”
Jeanne DeWitt Grosser： 在我停下来之前，最后还有一点——我们现在正处于一个我职业生涯中从未见过的产品迭代速度的时代。我二十多年的职业生涯全部在科技行业。对于市场进入策略团队来说，这其实非常困难。如果你每隔一天就发布一个新功能，要让团队及时了解并掌握这些更新，确实很有挑战性。所以这个智能体现在也在帮我们做一件事，我们目前的做法是：发布产品后，尽最大努力对团队进行培训赋能，然后让智能体跑过所有通话和互动记录，诊断我们在异议处理（objection handling）上哪里做得不好、哪里卡住了。到了周末，我们可以开一个碰头会，说：好的，智能体指出我们在哪些地方销售效率不高？
然后像工程团队一样，我们开始跑 sprint——那些问题其实就是 bug，是你市场进入策略流程中的 bug，不应该存在。到下一周，我们就会往异议处理指南里补充内容，往 discovery 指南里补充内容，找出演示中需要调整的地方，诸如此类。这还处于早期阶段，算是一个小小的预览，但这就是我们目前市场进入策略团队正在推进的方向。
Lenny Rachitsky： Jeanne，你在这么多方面都让我大开眼界，听起来真的太有意思了，听你讲这些我就觉得你们一定会赢。太厉害了。我最喜欢的是，这个 AI 工具、你们构建的这个智能体，看到了人类没有看到的东西。你被一个完全不同的结论所 surprises——这件事本身意义重大。这就是 AI 的全部承诺所在——它会做到我们甚至没有想到或力所不及的事情。
AI 看到人类看不到的洞察
Jeanne DeWitt Grosser： 确实如此。我们在 Vercel 有一个很有意思的案例。我们有 AI Cloud，人们用它把 AI 原生功能嵌入面向客户的应用中，但也用它构建内部应用来提升生产力或业务成果。我们正在和一家非常大的航空公司交流，这家航空公司显然会收到海量的客服咨询。所以他们当然想用 AI 来回答这些问题，从而降低客服成本——这是显而易见的方向。但更有意思的对话实际上来自他们的一位 C-level 高管，他说：我们把每一通客服电话都做了转录。所以我真正想知道的是，他们为什么打电话来，以及我怎么做才能让下周打电话来的人更少？现在有了 AI，你可以快速遍历所有这些内容，比让一个人在 CRM 里手动挑选状态来归纳”这周人们为什么给航空公司打电话”要快得多，而且能更快地找到可行的改进措施，让下周的来电减少。
Build 与 Buy 的权衡
Lenny Rachitsky： 我想很多听众现在都在想：“我也需要一个 deal-bott 和 lost bot。“这些都是你们自己内部构建的产品吗？
Jeanne DeWitt Grosser： 是的。
Lenny Rachitsky： 关于把它们做得这么好，你有什么经验可以分享吗？有什么技巧可以说——如何做一个真正好用的销售智能体？
Jeanne DeWitt Grosser： 有的，这其实就是我刚才忘了回答的后半段——关于 build 与 buy 的权衡。我们的一个经验是，构建这些智能体并不难，成本也不高。我之前提到过的 lead agent，花了六周时间，一个人只用三分之一的工作时间就做出来了。那个 deal-bott 的 lost bot 版本，基本上两天就搞定了——我们讨论了一下需求，40 小时后他就交出来了。现在我们还在持续迭代，加上我前面提到的其他功能。还有一个很有意思的点——不管对 Vercel 来说是好是坏——那个在 Vercel 上全栈运行的 lead agent，全年运行成本大约只有一千美元。我之前说过我们有 10 个人在做 SDR（销售拓展代表）的工作，从薪资角度来看我支付了超过一百万美元。
我把那缩减到了一个人，然后在背后有一个只花一千美元的 lead agent。总成本降低了百分之九十以上。现在市面上有很多面向智能体的软件。我们的一个体会是，因为整个领域还处于非常早期的阶段，往往你自身那些独特的上下文、内容和流程，才是释放智能体能量的关键。所以我认为自己动手实验、做内部智能体开发是很有价值的。最终我们可能会迁移到更好的集成智能体平台上，也可能会发现 CIO 的角色正从一个软件采购者转变为软件构建者，你会有一个内部 AI 平台，上面跑着一千个智能体覆盖整个组织。我现在还不确定。但我确实认为值得自己尝试，因为你可能会发现它比你想象的要容易得多，而且能很快看到回报。
Lenny Rachitsky： 所以我听到的是，你们发现市面上并没有现成的即插即用工具，真正的优势（alpha）基本上在于自己动手构建。
Jeanne DeWitt Grosser： 我觉得这有一部分是对的。另外也是因为现在工具在快速涌现，你会碰到一个老问题——最终你搞了 20 个工具来做 20 件事，而不是一个集成平台把所有事情都做了。实际上我现在跟客户交流时经常听到这个反馈，他们在部署 AI 时最大的问题竟然是走采购流程。因为他们拿到了一个 AI 预算，基本上可以说是开了张空白支票。我最近听到一个新说法——不叫 ARR，叫 ERR，experimental run rate revenue（实验性运行费率收入），意思就是大家都在说：“嘿，我们先试用一年，之后留不留再说。“但基本上你得采购 20 个不同的东西。大部分产品都还处于起步阶段，所以解决的问题相对窄，这在将来会改变。但我确实认为有一个机会值得把握：搞清楚你内部哪些地方有更特定的工作流，针对这些场景自己构建智能体可能更划算；而对于那些更通用化的需求，再去买现成的解决方案。
如何快速构建智能体
Lenny Rachitsky： 有没有什么平台或工具你想推荐的，让你们能这么快就构建出这些智能体？我知道它们部署在 Vercel 上，所以 Vercel 要提一下。除此之外，你会指引人们用什么？这些 SDR、这些市场进入策略工程师，他们以前是销售人员——他们在学写代码吗？他们在用 vibe coding 来构建这些智能体吗？具体是怎么操作的？
Jeanne DeWitt Grosser： 我们的 sales engineer 都有计算机科学学位，所以他们本质上是以销售角色工作的工程师，会写代码。这些智能体实际上是直接构建在 Vercel 上的。你可以使用 AI gateway 来调用不同的模型；如果需要运行不受信任的代码，有沙箱环境；有 workflows 来构建流程；还有 fluid compute，让你在只需要计算资源的时候高效地使用它们。所以我们基本上是在这里从零搭建。再说一遍，这并不难，不过确实需要写代码。当然现在市面上也有很多 vibe coding 工具，它们提供介于完全所见即所得——几乎像拖拽式——和更偏代码编写之间的工作流构建器，这类工具很多。但我确实认为，Vercel 的市场进入策略工程团队能如此轻松地构建这些智能体，其中一个原因就是 Vercel 平台本身让这件事变得非常简单——用我们的框架找到基础设施，然后非常快速地把智能体投入生产。
Lenny Rachitsky： 你们在这方面有一个相当不错的不公平优势。
Jeanne DeWitt Grosser： 确实，这很有趣……我是真心觉得这家公司在”吃自己的狗粮”这件事上比我见过的任何公司都做得好。每个人都在持续这样做，我们常说”Vercel builds Vercel with Vercel”（Vercel 用 Vercel 构建 Vercel）。你总是在寻找机会——嘿，我们怎么用自己的产品去做我们需要做的事？而这样做的一个结果是，你要么能理解客户想要什么，要么能发现产品中还缺什么、可以改进什么。
把市场进入策略当作产品来打造
Lenny Rachitsky： 顺着这条线，在你刚才描述的这些内容中，我已经多次听到你如何将市场进入策略当作一个产品来思考。这个播客的很多听众如我之前所说，都是做产品的人。所以我觉得这是一种非常好的思考市场进入策略的方式。我猜你已经谈到了其中一些要素，但简单地讲——如何把市场进入策略当作产品来思考？
Jeanne DeWitt Grosser： 好的，我一直……我在职业生涯中大约十多年前就有了这个认知。我大学毕业后的第一份工作是 2004 年在 Gmail 团队。Gmail 在 4 月 1 日上线，我 6 月 1 日加入。我相信你也记得，Gmail 是一项了不起的创新——当时几乎没有的那种大型 JavaScript 应用。它有一个 GB 的存储空间。整整一年之后 Yahoo Mail 才追上来，Hotmail 和其他邮箱花的时间更久。这就是当时 Gmail 和竞品之间的技术代差。而十年之后，云计算让人们能做到以前根本不可能做到的事情。所以我就开始觉得，嗯，软件开始在某种程度上商品化了。当技术差异缩小的时候，还有什么其他东西能让你脱颖而出？
我开始跳出技术领域去思考——我们买很多东西，是因为我们对它们的感受。于是我逐渐形成了这样一个论点：在被销售的过程中的体验，在产品之间只有边际差异的情况下，会越来越成为区分公司的关键因素，驱动购买决策。如果你相信这一点，那你就要创造一种客户购买旅程，让它感觉是非常独特的体验。我们在 Stripe 做了很多这方面的工作，现在在 Vercel 也在复制这套做法。举一个我觉得我们在 Stripe 做得非常好的例子——很多公司的销售流程是这样的：客户通过初步筛选之后，我们决定值得和你进入销售流程，下一步就是 discovery，本质上就是我问你一堆问题，试图挖掘痛点、搞清楚采购决策权在哪里，等等。
这对客户来说有时候挺无聊的。你基本上是在电话里被盘问。所以我们在 Stripe 开始做的改变是，把第一次会议变成一个白板会议。我们会一起坐下来，让你画出你的支付架构，以及支撑你收款、驱动客户成果的所有底层设施。通过这个过程，我们能了解到大量关于你的技术栈的信息、我们需要竞争和替代什么、价值在哪里。但客户自己也学到了很多，因为很多情况下他们从来没画过自己的架构图。所以他们离开那个会议时带走了一份有价值的产出，还有一种感觉——“这个人真的很协作，真心实意在帮我建立思考这个问题的思维模型。“然后我们还有其他的一些做法。
所以这就是我所说的把市场进入策略当作产品来打造——你需要规划出从客户第一次意识到这家公司存在，一直到成为那种五年深度留存的全线产品客户，这整段旅程中的一系列体验。这些体验可以让人觉得是交易性的、平淡的、无聊的，也可以让人觉得非常人性化、个性化、独一无二。所以我们尝试把这些旅程梳理出来，思考如何让产品发挥作用、如何让它充满人情味，最终希望这样能创造出终身客户。
Lenny Rachitsky： 我很喜欢那个白板会议的例子。你还有没有其他例子，关于如何让这种方式真正运作得非常好的？
在每个触点创造价值
Jeanne DeWitt Grosser： 有的。另一个原则——我们也是在 Stripe 真正发展出来的，然后带到了 Vercel——就是在任何触点上都要提供价值，无论这个客户最终买不买。因为即使客户不买，你会发现如果你在这个采购周期没抓住他们，三四年后他们进入下一个采购周期时，他们会回来的。我在 Stripe 待了九年，所以我亲眼看到有多少客户我们当初没能拿下，然后五年后他们出现了，买了。这是另一个原则。我们目前在 Vercel 做的具体例子是：互联网上有很多公开数据能帮助人们了解自己网站的性能，而网站速度实际上会影响 SEO，SEO 又会影响 AEO（Answer Engine Optimization），现在大家都在关注 AEO。所以我们主动联系时会做的一件事，就是直接给对方提供洞察——你的网站在绝对意义上的表现如何，相对于同行的表现如何。理想情况下这能引起你的兴趣，让你想从我们这里了解更多；但即使没有，你至少获得了可能之前并不知道的洞察，也许会促使你思考自己目前的配置是否是最优的。
Lenny Rachitsky： 太好了。我从中听到的核心意思是：当你说”把它当作产品来思考”时，本质上就是——产品人员会思考自家产品的体验，每一步旅程，这里是流程，第一步、第二步、第三步、第四步、第五步，我们怎么让每一步都很棒，让用户持续走完这个旅程。而你思考的就是，从潜在客户的角度，怎么让他们旅程中的每一步都很出色，推动他们继续走下去。
Jeanne DeWitt Grosser： 没错。怎么让它成为一种体验，而不是一笔交易。
Lenny Rachitsky： 而不是感觉销售在冲你推销东西。
Jeanne DeWitt Grosser： 对。
Lenny Rachitsky： 好。继续在这个务实的方向上深入，我还想再进一步。那么在当下，有哪些市场进入策略的策略是你觉得特别有效的——对那些想让别人关注自己产品、购买自己产品的人来说？
提供独特洞察与最佳实践
Jeanne DeWitt Grosser： 我想说一个，跟我刚才讲的衔接得上的，就是：你能围绕自己的产品，或者围绕客户可能处于的次优状态，提供哪些独特的洞察？所以我确实认为，投入数据去挖掘这些洞察是一件事。另一件事，道理很直接但往往做得不够——很多优秀的公司会投入做文档，这很好，但他们到此为止了。特别是当你的销售对象是稍大一点的公司时，像 AWS 所谓的”良好架构指南”（well-architected guides）或”蓝图”（blueprints）这类东西就很重要。很多客户，尤其是大客户，真的很想知道在他们特定的环境里，到底怎样才是实施你产品的最佳实践。一个很好的例子来自 Stripe。Stripe 在 marketplace 方面做得非常出色。Lyft、Instacart、DoorDash，几乎全都在用 Stripe。
Stripe 自然知道为 marketplace 搭建支付系统的最佳方式，因为什么样的我们都见过了。所以当你去卖一个 marketplace 方案的时候，跟对方说”我们有文档，你去看看吧”，他们是不喜欢的。因为他们在想：“每个 marketplace 都跑在 Stripe 上，我不想看通用文档，我要你告诉我，为 marketplace 搭建支付系统的最佳方式是什么。“所以我认为这是另一件关键的事，特别是当你的目标受众已经超越了那种独立开发者或初创创始人群体之后。
然后，我不知道这算不算一种策略，但我确实觉得对创始人来说是一个很好的提醒——尤其是那些还处于创始人主导销售阶段的创始人——就是做好 discovery 的价值。我经常发现创始人太兴奋于讲自己的产品，或者对方只问了一个问题，他们就抓住了话头：“哦，这个我能帮你解决。“但优秀的销售人员，在对话中的说话时间通常远低于一半，因为他们在不停地提问、深挖，往往是在帮助客户自己得出结论。所以学会做”五个为什么”的追问，往深了走，而不是立刻进入问题解决模式。如果对方问了一个问题，你往往应该对那个问题再问一个问题，然后再回答。学会把这些做到出色，我觉得是让人脱颖而出的关键。
Lenny Rachitsky： 所以最后这条建议，我想很多人都能从中受益——就是多听少说。
Jeanne DeWitt Grosser： 对。
用数据驱动洞察：Vercel 的实践
Lenny Rachitsky： 关于第一条建议——分享独特洞察、指出客户的次优状态——你能举一个具体的例子吗？比如说你在卖 Stripe 或 Vercel 的时候，是怎么打动对方的？“你现在缺这个，用了我们的东西你能变得好很多。“有这样的故事吗？
Jeanne DeWitt Grosser： 用 Vercel 来举例，但我会说得更具体一些。性能方面，你可以去看 Core Web Vitals，我们能实际看到他们网站里哪些部分加载快、哪些加载正常等等，任何人都可以查到这些数据。但我们能进一步做的，是帮助他们做同行对标。这一块我们做了很多。另一个我们花了不少时间的方向，是帮助客户理解 MCP 服务器以及什么时候该用。我觉得现在这个话题很热，但很多人不知道如何在自家产品中去思考它。所以这一块我们也挖得比较深。然后跟第一条相关的，是 AEO（答案引擎优化），其实它跟 Vercel 本身有点间接关系。
我们提升性能，性能驱动 SEO，而 SEO 是 AEO 的一个输入。但我们在 AEO 方面花了大量时间分享洞察，因为我们自己在深入研究它，而且我们认为自己比大多数人都更了解它。所以，同样是建立信任关系的一部分，人们可能从我们的 AMA 或那些内容出发，觉得”好，你教会了我很多，所以我想让 Vercel 来帮我做性能优化”。但在很多情况下，他们其实只是觉得”这家公司看起来很有见地，我能从他们身上学到东西，所以我要多关注一下他们”。经过一段时间之后，也许某个触发点让他们决定：“现在是我要去深入了解 Vercel 那方面的时候了。”
Lenny Rachitsky： 很好。所以我在这里听到的，从很多方面来说——这也跟我之前的感受一致——我最近请了 Jenna Abel 来做播客，整期都是关于销售技能、如何做销售的。
Jeanne DeWitt Grosser： 不错。
Lenny Rachitsky： 她的一个建议是：你不应该把重点放在”这是我们解决的痛点和问题”上，而应该放在”用了这个，你能比竞争对手强多少”上。这里有巨大的差距和 alpha 可以争取。如果你用 Vercel，你在速度上就落后了，在 AEO 上会被甩开，所有这些。来看我们怎么帮你把整个支付系统架构做到顶级的水平。你认同这个说法吗？
Jeanne DeWitt Grosser： 认同。有人跟我说过一个数据——是个整数，所以我不敢说完全准确——但大意是，80% 的客户购买是为了避免痛苦或降低风险，而不是为了增加收益。这对初创创始人来说是很值得理解的一点。我们都喜欢谈论”可能性之美”，谈论未来能实现的一切，这很令人兴奋，人人都是远见者。但这种话术真正能产生共鸣的，往往只是另一个创始人。对其他人来说，尤其是企业客户，他们是在规避下一季度完不成收入目标的风险，规避被竞争对手超越的风险，规避品牌受损的风险等等。所以实际上对很多初创公司来说，做这种转向是很难的，因为感觉不太符合品牌调性。但它确实能驱动更多的购买行为——制造一点点担忧，让客户觉得”我可能没有处于有利位置”，或者通过好的提问，让客户清楚地意识到自己哪里不足，而你刚好能帮到他。
购买决策的核心：规避风险
Lenny Rachitsky： 你分享的这个数据太重要了。这个观点其实在播客里之前也出现过——人们购买，很大程度上是为了降低风险，基本上就是不想让自己在职业上受伤、不想让公司受损。这比”我有这个问题需要解决，好，谢谢你，这个帮我解决了”是一个更大的购买驱动因素。April Dunford 上播客的时候也是这样说的——这是一次巨大的职业赌注。我们要引入产品 X，它将会……比如 Stripe，我们不说 Vercel 了。拿 Stripe 来说，“我们要采用 Stripe”——这是一个巨大的决策。如果搞砸了，你的职业生涯会受影响，你的老板会对你不满，你的公司会因此倒退。所以正如你所说，很多购买决策的本质就是”我只是不想把这事搞砸”。
Jeanne DeWitt Grosser： 没错，完全同意。
客户细分入门
Lenny Rachitsky： 好。沿着策略这条线继续，我知道你非常推崇、也帮助很多人思考的一个话题是 segmentation。
Jeanne DeWitt Grosser： 是的。
Lenny Rachitsky： 这是很多创始人纠结的问题。他们知道”我需要确定我的 segmentation 策略，搞清楚我们要打哪里”。能不能给我们一个 segmentation 的入门讲解——大家需要知道什么，为什么这件事重要，以及可以怎么着手去做。
客户细分的框架与维度
Jeanne DeWitt Grosser： 客户细分基本上就是，你如何把地球上所有公司进行切分，以便根据它们不同的购买方式来分析。我用 Stripe 和 Vercel 的例子来说明。一个非常典型的企业客户细分是小、中、大。这是一种合理的方式——小型客户通常只有一个决策者，中型客户是一个小团队，大型客户则很复杂，涉及委员会等等。所以在 SMB、中端市场和企业级之间，购买流程确实不同，但如果你止步于此，很可能会遗漏重要的东西。那么在你的产品中，还有哪些因素也会改变销售方式？
在 Stripe，我们用两种方式进一步细分业务。可以把客户细分想象成一个图表，X轴是企业规模——小、中、大，Y轴是增长潜力。这对 Stripe 很重要，因为它是基于消费的业务模式。如果你的年增长率是200%，你对 Stripe 的价值就远高于年增长率8%的公司。所以我们愿意在那些200%增长的公司上投入更多时间和资金，而不是8%的。这是影响你目标选择策略的一个维度。
Stripe 的另一个细分维度是商业模式。你是 B2B 吗？B2C 吗？还是 B2B2B，比如平台，或 B2B2C，比如 marketplace？为什么这很相关？因为如果是 B2B，你会需要企业支付功能。信用卡在 PLG 功能或 PLG 销售中很有用，但你还需要 ACH 电汇等。而且你可能有订阅制业务，所以会需要 Stripe Billing。如果是 B2C，那就是消费者支付，Apple Pay 非常重要。如果你是平台或 marketplace，你会购买我们的 Connect 产品。这帮助我们构建了更有针对性且可复制的销售方式。
Vercel 的客户细分实践
Vercel 的情况类似——小、中、大，按照购买复杂度划分。我们同样按增长潜力来细分，因为 Vercel 也是基于消费的业务模式。但对我们在 X轴上还叠加了其他几个因素，其中一个是”提升”——互联网站点流量是一个可观测的指标。Google 会发布 CrUX 分数，基本原理是他们从 Chrome 中获取了大量数据，所以他们知道 Lenny 的网站有多少访问量——
Lenny Rachitsky： 数百万。
Jeanne DeWitt Grosser： ——以及 Jeanne 的网站有多少访问量。所以基本上，如果你是一家小公司但流量非常高，情况会更复杂，Vercel 能赚更多钱，所以我们想把你提升到更高级别。一个很好的例子是 OpenAI。OpenAI，我现在忘了它有多少员工，假设是3000人，现在可能已经更多了。按员工数在大多数公司会把它归为中端市场，但它是全球流量排名前25的网站。对我们来说，这会把它推入企业级客户，因为我们需要以更深入的销售流程来服务它。
我们叠加的另一个因素是工作负载类型。如果你是电商公司，销售方式会非常不同——你实际上使用不同的语言，会谈论产品列表页和产品详情页，后端还有订单管理系统。与加密货币公司完全不同，后者可能从上到下都跑在 AWS 上。同样，这帮助我们为你提供真正差异化的销售内容。
Lenny Rachitsky： 太棒了。所以本质上你做的事情是，回到你最初在 Stripe 的故事，你把这个客户宇宙拆分开来，帮助你筛选哪些公司最有可能购买你的产品。你找到的是那些与”它们很可能是优质潜在客户”相关联的属性。
Jeanne DeWitt Grosser： 是的。
如何着手建立客户细分
Lenny Rachitsky： 你推荐用这种 XY 轴的方式而不是其他方式吗？比如一个五列的电子表格之类的。你怎么开始？
Jeanne DeWitt Grosser： X 和 Y 轴的方式确实有它的道理。比如企业规模是否会影响大多数购买决策？而且现在确实有大量的消费型业务模式出现？所以其中有些方面我认为是相对通用的。但我觉得，当我来到 Vercel 时，因为是新产品、新市场，对我来说是一个全新的领域，我有很多要学的。但这确实是我入职前30天里最先做的事情之一。
我坐下来跟这里负责数据科学的 Abhi 聊，问他：什么驱动收入？你能从客户身上预先看到哪些特征，来判断这个客户可能付我们十万美金还是一百万？这些可能就是客户细分框架的一部分。同样的，我们该寻找哪些属性来聚类我们反复赢得的客户群体？这就是我们最终发现 CrUX 排名超级重要——因为你付给 Vercel 的费用跟你的流量相关。工作负载类型也同样重要。
对 Vercel 来说，当我们做了这个分析后，结果非常有趣。我们发现——哇，我们在电商领域渗透率很高，其实也不太令人意外，因为我们驱动的是高性能网站，而电商对超快的网站性能非常看重。但当时，举个例子，如果你看企业级 SaaS 公司，我们的渗透率并不高，尽管你会觉得——前端云，非常开发者导向，软件公司当然应该用我们的产品。但在企业级市场，大多数公司在 Vercel 存在之前就已经构建了他们的 SaaS 产品。要把两百万行代码迁移到 Vercel，那是一个很大的工程。这帮助我们真正理解了我们在哪里赢、在哪里没赢。而现在，举例来说，在 SaaS 公司和企业级市场，我们实际上看到对 AI 云的大量兴趣。这些公司是较早采用”让我们在现有 SaaS 应用中加入 AI 原生功能”这一思路的客户。同样，这帮助我们确定该在哪里瞄准什么。
Lenny Rachitsky： 所以本质上你在做一个回归分析，看什么有效，然后找出与成功最相关的属性。当创始人问我如何确定 ICP、如何确定该聚焦哪里时，我一直推荐的方法是——就想三个属性来缩小范围。比如 A轮公司、创始人主导销售的、marketplace，类似这样的。你觉得这是一个好的起点经验法则吗？
客户细分是全公司的事
Jeanne DeWitt Grosser： 是的，我觉得超过三个属性就开始过于细节了，而且说实话，你也没法切得太细。你一共就五个销售。难不成你要把一个销售分到一个细分里去？所以我认为三个属性是你可以进行有效推理的范围。关于这个话题，还有一点我觉得很重要——很多人认为客户细分是一个市场进入策略层面的事情，但我真的认为它是公司层面的事情。在 Vercel，每位新员工入职的第一周，我都会做一次培训。我们有一个公司价值观叫 KYC，即”了解你的客户”。我负责讲解 KYC 部分，会讲我们的客户细分框架，以及我们的客户群体如何映射到各个细分中去。这非常重要，因为当那些新入职的产品经理离开培训室之后，他们在开发产品时就会思考：好，我在做一个新的后端产品，这是面向谁的？是面向企业还是面向初创公司？基本上，我是不是对”我要在哪里赢、为什么赢”有一个明确的判断？如果从第一天起就养成了这种思维习惯，那么之后跟市场进入策略团队讲同一种语言就容易得多，也能更好地协调：好，我们怎么把这个产品推向市场，同时跟我们其他已有的优先事项保持一致？
让销售组织赢得工程师的尊重
Lenny Rachitsky： 很好，这正好引出我想聊的另外几件事。其中一件是——我从很多跟你共事过的人那里听到，你非常擅长建立一个跟产品和工程团队配合良好的市场进入策略组织。我来读一段你前同事 Kate Jensen 的话。她说你的超能力是：打造一个在工程师眼中”不像销售组织”的销售组织。她建议我问的问题是：要怎么做到这一点？打造一个工程师和产品团队真正愿意合作的销售组织，关键要素是什么？
Jeanne DeWitt Grosser： 我一直给我销售团队的一个试金石标准是：如果你是我团队里的客户经理，我把你放到我们公司十个工程师面前，他们应该要花十分钟才能分辨出你不是产品经理。我想传达的核心是——你需要具备极其深入的产品理解力。原因有两方面。第一，这能让你在产品和工程团队面前建立公信力。第二，我坚信，全球最优秀的市场进入策略组织，既是驱动收入的组织，也是驱动研发的组织。我之所以强调后者，是因为你想，一个产品管理团队可能有 UXR（用户体验研究）团队在外面做调研，产品经理当然也应该出去跟客户交谈。但如果我有一个二十人的销售团队，想想我们一周能跟多少客户对话。所以如果我们能出色地把所有这些反馈转化为有效信号，再反馈到产品路线图中，我们实际上可以成为产品管理团队的延伸。但这要求你善于从噪音中辨别信号，理解什么时候一个异议只需要做异议处理，什么时候它其实是一个市场机会。我觉得这些做法都很有帮助。
Lenny Rachitsky： 作为一个产品经理——或者说我曾经是产品经理，我也不知道我现在到底算什么了——我真的太喜欢这个想法了。就是销售人员跟产品经理之间你分不清区别。最经典的挑战是，销售团队会要求各种功能，而产品经理需要不断回推，考虑这些是否符合整体规划。所以我觉得这里面很大一部分就是要理解得足够深入。
Jeanne DeWitt Grosser： 对，你需要一个能像总经理一样思考的销售组织，而不是只想着怎么把单子签下来，而是想着怎么帮公司把业务建立起来。所以同样，要知道什么时候该说”不”，什么时候该做异议处理，什么时候应该说：“嘿，我在最近三个客户电话里都听到了这个，我真的觉得这会是一个巨大的突破口，能让我们更有竞争力，而且目前没有人这样做。“我认为这需要在招聘时寻找既具备销售技能、又能以产品思维来思考的人才画像。
Lenny Rachitsky： 我很喜欢这个思路。好，再读一段话，来自 Claire Hughes Johnson，她曾经上过我的播客，是一位非常优秀的销售领导者，在 Stripe 跟你共事过。她大致是这么说的：Jeanne 大概是最擅长与产品和工程团队建立连接的市场进入策略领导者，她深入理解产品，而且比我见过的任何人都能为她的对接人提供最有价值的输入。听起来，这其中的另一个要素就是——销售要真正感觉像是产品工程团队的合作伙伴，而不是单纯地说”嘿，帮我把这些事做了”，而是真正做到像伙伴一样。
Jeanne DeWitt Grosser： 归根结底，公司战略基本上就是产品战略与市场进入策略战略的交汇。作为一个市场进入策略负责人，我一直在思考的就是：如何更高效地赚取更多收入？而通常要做到这一点，你需要一个在市场上具有竞争力且商业化做得好的产品。这意味着我真的会深入思考产品战略和定价战略，因为如果这两方面都做到位了，你赢的概率就更高，过程中的摩擦也更少。所以作为收入负责人，你需要戴上一顶总经理的帽子，不仅仅想”我怎么卖”，而是真正去想：我怎样把我从不断与客户对话中获得的洞见，转化为对公司战略更有效的推动？
PLG 的当下与局限
Lenny Rachitsky： 说到产品，稍微换个方向——PLG（产品驱动增长）这个词之前一度非常火热，每个人都说”你必须走 PLG，这是唯一赢的方式。做销售已经不可能了，未来是 PLG 的天下。“现在感觉这种论调已经消退了。当然，仍然有很多公司通过 PLG 实现增长。你对 PLG 怎么看？如今一家公司在什么情况下应该考虑用 PLG 来实现增长？
Jeanne DeWitt Grosser： PLG 对很多公司在起步阶段是适用的，除非你非常明确地就是在为企业级市场打造产品。以 Sierra 为例，他们非常明确地瞄准 Global 2000 或类似量级的客户。PLG 对他们来说不会太有用，因为他们从第一天起就要争取八位数量级的合同。但对于很多产品来说，创始人在初期瞄准的是初创公司用户群，然后逐步增加更多功能，最终持续向上拓展市场。所以我认为 PLG 依然非常相关。它是 Vercel 增长的一个主要驱动力，也是 Stripe 增长的重要驱动力。但人们常犯的错误是——PLG 通常是有天花板的。客户一般不会通过自助服务流程给你一百万美金。所以到了某个阶段，如果你想维持增长率，你的交易规模就必须越来越大。而我觉得很多公司卡住的地方在于——在 PLG 上等了太久。因为建立一个可复制的销售流程是需要时间的，而这个销售流程最初通常由 inbound 喂入，之后你还得加上 outbound。而把 outbound 变成一个可预测的引擎，本身就需要相当长的时间。所以我觉得公司碰到墙的情况，通常就是因为他们没有及早把销售这一块加上去。
Lenny Rachitsky： 所以本质上每家公司最终都得建立一个销售组织——有些从产品驱动起步然后加销售，有些则从一开始就有销售。
Jeanne DeWitt Grosser： 对，我同意。可能有一些不错的垂直 SaaS 平台是面向 SMB 的大公司，但即便它们最终也建立了 Velocity 销售团队。所以我好像想不出有哪家千亿美元级别的公司是纯 PLG 的。
Lenny Rachitsky： 是的，感觉即使你增长很快，不建销售也是在把钱留在桌上。我知道 Atlassian 长期以来是一家 PLG 公司，但最终也”屈服”了——不知道这个词用得对不对。好，你刚才提到了定价。我知道你对定价和定价策略有很强的观点。对于正在思考如何给产品定价的人，你有什么建议？
把定价当作产品来思考
Jeanne DeWitt Grosser： 这其实跟刚才的话题一脉相承，但我认为第一件事是——你必须把定价当作一个产品来思考。你选择怎样为产品定价，这件事真的非常重要。你是否真正理解客户从哪里获得价值？你是否真正理解你在哪里产生成本？你有没有聪明地把这两件事对齐？有很多公司严重低估价格的例子——你有点害怕为自己实际提供的价值收费。我觉得也有很多例子是人们默认加入免费增值策略，但那其实并不是一个真正的策略。Stripe 有一个很好的例子——我们几年前推出了 Stripe Billing，当时采用了免费增值策略，因为”大家都这么做”。后来我们看了看，发现”实际上接入 Stripe Billing 是需要一些工作的。所以一旦你接入了，你大概率会留下来。“于是我们把免费试用砍掉了，砍到零，没有任何负面影响。
在 Vercel，我们也在经历类似的转型。我们本质上是一个基于消费的业务模式，但起初我们基本上把它打包成了一个看起来像 SaaS 的价格。随着我们不断增加更多功能，这个模式不再奏效了。所以我们做了拆分。实际上我们在八月份做了一个相当大的定价调整，现在有企业版和专业版两个档位。如果你看企业版——它叫企业版是有原因的，是面向企业客户的。但实际上企业版上大约一半的用户是初创公司，这说明企业版里有些东西是初创公司真正想要的。于是我们把很多这类功能从企业版中拿出来，让用户可以自助在线购买——你猜怎么着，人们确实在买。
这极大地推动了我们 PLG pipeline 的增长，对初创公司来说非常好，因为效率极高——他们可以直接买到想要的东西。对我们来说也很好，因为不需要人工介入。所以把这些旋钮都调到正确的位置，是同时实现优质客户体验和最优营收结果的关键。
销售薪酬与招聘策略
Lenny Rachitsky： 在进入非常精彩的闪电问答之前，也许再问一个问题。这是个组合问题——我听说你对销售薪酬有一个与众不同的观点，关于如何给销售人员定薪酬，以及招聘销售人员时应该招什么样的人。能聊聊你的看法吗？
Jeanne DeWitt Grosser： 我对销售薪酬一直有些纠结，因为它的核心是按绩效付酬，这我当然赞同，但它会让你的组织变得不够灵活。因为你基本上要提前 12 个月决定”我重视什么”，而尤其在当下这个时刻，这些重点可能发生变化。举个很好的例子——我们在 Vercel 制定今年的销售计划时，AI cloud 还不存在。我们当时卖的是前端云和 VZero，然后年中推出了 AI cloud。我们当然有各种好的方式来继续激励大家去卖它，但我觉得你想具备创新能力、想灵活转向，而当你有一个精心设计的、非常结构化的销售计划时，这会很有挑战性。
所以我的”热观点”就是——我在思考如何既保留销售激励的优势（销售确实能激励人，它是一个可量化的职能，这很好），又保留改变主意的灵活性。因为我觉得现在很多公司在做年度规划时都很吃力。这是第一点。关于人才画像，我一直比较看重多元化的组合。我坚信销售是一项技能，所以你的组织中需要有真正有销售经验的销售人员。但我觉得把他们和更非传统的背景搭配起来也有价值，尤其是咨询或银行背景的人。这些人在销售中更量化和分析的部分非常擅长——就是我们一开始谈到的那个咨询式销售的部分。我发现当你把这些人混搭在一起时，咨询/银行背景的人会意识到”哦，销售是一门技能，我之前并不真正具备它”，于是他们会去向有销售经验的客户经理学习。而你的客户经理则会学到——好，我怎么看一份损益表？我怎么跟一个 CFO 对话？我怎么更有效地呈现 TCO（总拥有成本）分析？这样就创造了一个更丰富的学习环境，大家互相碰撞想法。
Lenny Rachitsky： 太棒了，我很喜欢这个策略。好，最后一个问题。还有什么想分享的吗？在进入精彩的闪电问答之前，还有什么想留给听众的？
Jeanne DeWitt Grosser： 天哪，我觉得我们已经聊得很透彻了。
Lenny Rachitsky： 好的，谢谢。那我就——
Jeanne DeWitt Grosser： 这个问题把我问住了。
Lenny Rachitsky： 哈哈，这就是我的目的。那么 Jean，我们进入了非常精彩的闪电问答环节。我会很快，我知道你得走了。我只问你两个问题。
Jeanne DeWitt Grosser： 好。
人生信条与竞技精神
Lenny Rachitsky： 第一个，我直接跳到你的人生座右铭。你有没有一个最喜欢的人生座右铭，在工作中和生活中经常回来找到它、觉得很有用的？
Jeanne DeWitt Grosser： 有的。我发现我因为说几句话而被人记住——我自己不一定意识到，但当你离开一个组织的时候，人们会告诉你什么话留在了他们心里。有一句我觉得确实是我的标志。从小到大，我妈妈总对我说：“艰难时刻，强者奋起。“在销售中，你总会遇到某个季度没有达到节奏的时候。这句话我确实经常用到，因为在我看来——这句话还有另一个版本，我妈妈也常说：“有志者事竟成。“我认为你永远可以选择找到一条前进的路，即使那条路不那么清晰。
Lenny Rachitsky： 我很喜欢这些。好，最后一个问题。我读到你在大学早期是一个非常competitive的跳水运动员。我很好奇，你从那段经历中学到了什么，带到了后来的工作中，帮助你取得了今天的成功？
Jeanne DeWitt Grosser： 嗯，首先我应该说，我在队里基本上是三个人中排第三。
Lenny Rachitsky： 第三名，也不错了。
Jeanne DeWitt Grosser： 我在大学时确实做到了，但那也是那段运动生涯的全部。跳水是一项讲究精确的运动，也是一项不断重复的运动。而且它还有一个特点——当你平背着水，真的就在你游向池边的时候，背上已经开始起鞭痕了，你仍然会被要求百分之百地立刻回到跳板上，把刚才那个完全相同的动作再跳一次。所以我觉得这其中有很多东西可以迁移到工作和销售中。对我来说，我对卓越有一种执念。而在销售领域，销售的核心在于可复制性——你如何驱动可预测的结果，你的预测能力有多出色？我想我把这些品质带到了销售工作中。类似地，在销售中你会听到很多”不”。所以另一位销售培训大师曾经对我说过一句话——“yes”很好，“no”也很好，“maybe”才会害死你。所以你要学会坦然接受，“不”其实是一件好事，它给了你数据，你现在可以据此做点什么。
Lenny Rachitsky： 用这种方式来结束这次对话，真的很有启发，也很鼓舞人心。Jeanne，非常感谢你来参加节目。
Jeanne DeWitt Grosser： 非常感谢你的邀请，Lenny。聊得很开心。
Lenny Rachitsky： 大家再见。
感谢大家的收听。如果你觉得这期节目有价值，可以在 Apple Podcasts、Spotify 或你喜欢的播客应用上订阅。也请考虑给我们评分或留下评价，这真的能帮助更多听众发现这个播客。你可以在 lennyspodcast.com 找到往期所有节目或了解更多信息。下期见。
术语表
| 原文 | 中文 |
|---|---|
| Abhi | Abhi（人名，Vercel 数据科学负责人） |
| account executive | 客户经理 |
| AEO (Answer Engine Optimization) | AEO（答案引擎优化） |
| agent | 智能体 |
| AI gateway | AI gateway（AI 网关） |
| alpha | alpha（超额收益/竞争优势，保留原文） |
| AMAs | AMA（问答活动，Ask Me Anything） |
| April Dunford | April Dunford（人名，保留原文） |
| ARR (Annual Recurring Revenue) | ARR（年度经常性收入） |
| Ben Salzman | Ben Salzman（人名，Stripe 前员工，后创办 AI 原生 GTM 公司） |
| blueprints | 蓝图 |
| Claire Hughes Johnson | Claire Hughes Johnson（人名，保留原文） |
| closer | closer（成单者） |
| consumption-based | 基于消费的（业务模式） |
| core web Vitals | Core Web Vitals（核心网页指标，保留原文） |
| CrUX | CrUX（Chrome 用户体验报告，Google 发布的网站性能数据集） |
| customer success | 客户成功 |
| deal-bott | deal-bott（智能体名称，保留原文） |
| discovery | discovery（需求探索） |
| dogfooding / eat its own dog food | 吃自己的狗粮（使用自家产品） |
| economic buyer | 经济买家 |
| ERR (Experimental Run Rate Revenue) | ERR（实验性运行费率收入） |
| fluid compute | fluid compute（弹性计算） |
| forward-deployed engineering | 前线部署工程 |
| founder-led sales | 创始人主导销售 |
| freemium | 免费增值 |
| Global 2000 | Global 2000（全球2000大企业榜单） |
| go-to-market engineer | 市场进入策略工程师 |
| Gong | Gong（销售智能平台，保留原文） |
| GTM (Go-To-Market) | 市场进入策略 |
| ICP (Ideal Customer Profile) | ICP（理想客户画像） |
| inbound | inbound（入站） |
| install-based sales | 基于已安装产品的销售 |
| Jenna Abel | Jenna Abel（人名，保留原文） |
| Kate Jensen | Kate Jensen（人名，保留原文） |
| KPI (Key Performance Indicator) | KPI（关键绩效指标） |
| KYC (Know Your Customer) | KYC（了解你的客户） |
| lead agent | 线索智能体 |
| lost bot | lost bot（智能体名称，保留原文） |
| lost opportunity review | 失利机会复盘 |
| LTV (Lifetime Value) | LTV（客户生命周期价值） |
| marketplace | marketplace（交易市场） |
| MCP servers | MCP 服务器 |
| mid-market | 中端市场 |
| objection handling | 异议处理 |
| outbound | outbound（外站/外呼） |
| outbound prospecting | 外呼拓客 |
| P&L (Profit and Loss) | 损益表 |
| penetration | 渗透率 |
| pipeline | pipeline（销售管道/商机管线） |
| platform architect | 平台架构师 |
| PLG (Product-Led Growth) | PLG（产品驱动增长） |
| professional services | 专业服务 |
| revenue operations | revenue operations（营收运营） |
| sales engineer | 销售工程师 |
| SDR (Sales Development Representative) | SDR（销售拓展代表） |
| segmentation | 客户细分 |
| self-serve | 自助服务 |
| Sierra | Sierra（AI 客户服务公司，保留原文） |
| skew | 档位/版本（产品定价中的不同层级） |
| SMB (Small and Medium Business) | SMB（中小企业） |
| TCO (Total Cost of Ownership) | TCO（总拥有成本） |
| technical buyer | 技术买家 |
| UXR (User Experience Research) | UXR（用户体验研究） |
| Velocity sales team | Velocity 销售团队（高效、标准化的面向中小客户的销售团队） |
| vibe coding | vibe coding（氛围编程，保留原文） |
| well-architected guides | 良好架构指南 |
| workload type | 工作负载类型 |
此文档由 AI 分片翻译（translate_long_document）
What world-class GTM looks like in 2026 | Jeanne DeWitt Grosser (Vercel, Stripe, Google)
GTM as a Product
Lenny Rachitsky: I’ve been getting so many asks for go-to-market help.
Jeanne DeWitt Grosser: With AI, it’s just intensified because you have 10 players pursuing the same market opportunity and so your ability to actually bring the product to market to differentiate yourself from the competition has become more strategically important than it was previously.
Introducing the Guest
Lenny Rachitsky: I had Jenna Abel on the podcast recently, one of her tips is you don’t want to be focusing on here’s the pain and problem we’re solving and instead focus on here’s how you will be better than your competitors.
Jeanne DeWitt Grosser: 80% of customers buy to avoid pain or reduce risk as opposed to increased upside, which is a good thing for startup founders to understand. We all love to talk about the art of the possible, everything we’re going to enable in the future, but that’s often really a sale that’s going to resonate with another founder. For everybody else, particularly enterprises. You’re avoiding the risk of not making your revenue target next quarter.
What is GTM?
Lenny Rachitsky: I’ve heard a lot about how you think about go-to-market as a product.
GTM Lifecycle Integration
Jeanne DeWitt Grosser: We buy a lot of things because of how we feel about them. The experience that you have of being sold to will increasingly actually differentiate a company and drive buying decisions if products are only different at the merchant. And so then you really want to create a customer buying journey that feels like very unique experiences.
Biggest Recent GTM Shifts
Lenny Rachitsky: Something I’ve heard from so many people you’ve worked with is that your superpower is building a sales org that doesn’t feel like a sales org to engineers.
Frontline Engineering vs GTM Engineering
Jeanne DeWitt Grosser: The litmus test I have always given my sales team is if you are an account executive in my org and I put you in front of 10 engineers at our company, it should take them 10 minutes to figure out you aren’t a product manager.
Lenny Rachitsky: Today my guest is Jeanne Grosser. Jeanne was chief product officer at Stripe where she built their very early sales team from the ground up. She’s currently COO at Versel where she oversees marketing, sales, customer success, revenue ops and field engineering. Jeanne has built world-class go-to-market teams at multiple unicorns and has advised dozens of companies on doing the same. In our conversation, we go deep on what a world-class go-to-market team looks like, including what the heck is go-to-market, the rise of the go-to-market engineer and how this role is already enabling her team to operate 10 times faster. A bunch of very specific tactics to level up your go-to-market skills, a primer on segmentation, how to think about your go-to-market process like a product, her favorite go-to-market tools, her hot takes on PLG and sales comp and sales hiring, and so much more. If you are looking to get smart on the latest and greatest in go-to-market thinking, this episode is for you.
A huge thank you to Claire Hughes-Johnson, Kate Jensen and James Ditt for suggesting topics for this conversation and Kelly Schafer for the connection. If you enjoy this podcast, don’t forget to subscribe and follow it in your favorite podcasting app or YouTube. It helps tremendously. And if you become an annual subscriber of my newsletter, you get an entire year free of a ton of incredible products including Devon, Lovable, Replit, Bold [inaudible 00:03:00], Linear, Superhuman, Descript, whisperer flow Gamma, Perplexity, Warp, Granola, Magic Patterns, Ratecast JPRD, Mob In Hand, Stripe, Atlas. Head on over to Lenny’s newsletter.com and click product pass. With that, I bring you Jeanne Grosser after a short word from our sponsors.
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Jean, thank you so much for being here and welcome to the podcast.
From Project Rosalind to AI Prospecting
Jeanne DeWitt Grosser: Thanks for having me. Lenny.
Lenny Rachitsky: What I wanted to get out of this conversation by the end of this to basically have this conversation be the thing that we send people when they’re like, “I want to get better go to market. I’m trying to figure out what to do and get to market.” We send them this versus having to hire someone for a lot of money and usually they can’t find amazing people, because they’re all snatched up. So let me start with just the basics. When people hear at the term go to market, what does that mean? What does that encompass?
From Stripe to AI-Native GTM
Jeanne DeWitt Grosser: I think there are two answers to this. Often what people think of is sort of the tip of the spear of what drives revenue, which is marketing and sales. For me, I think of it as any function that is going to touch a customer or make a dollar, and actually my remit at Vercel is that, so that includes marketing, sales, all of your technical sales roles like sales engineers or post-sales platform architects is what we call them at Vercel. It’s customer success, it’s support, it’s partnerships. And the reason I say that is my experience throughout my career has been that those functions often have this Venn diagram strategy where marketing’s pursuing one thing, it overlaps with what sales is pursuing, but not perfectly, which also overlaps with what support is pursuing but not perfectly. Examples of this would be slightly differing segmentation frameworks, et cetera.
And so one of the things I think you’re going to want to see more in this particular moment is that that become a really integrated lifecycle. In particular, I think we’re going to see a lot of the functions of go-to-market get redefined, so we’ve gone through a period of hyper-specialization in go-to-market depending on how you count them. There are, I think somebody quoted 17 different roles within go-to-market these days and I hypothesize that a lot of those are going to start to collapse. And so if you think of go-to-market more holistically, I think you can kind of go back to what are the jobs to be done from making a customer prospect aware of your product all the way through to high LTV, five years on the platform, fully wall-to-wall, and you’re going to want to map that out and orchestrate it the way you would think about that within your own product.
The Full GTM Engineer Role
Lenny Rachitsky: Awesome. We’re going to go through that whole cycle of go-to-market, but so is it safe to say just for most companies that are especially starting out when they say go-to-market, that mostly is sales and then there’s marketing as maybe a smaller fraction of that and then as you become more advanced and grow, customer success plays into it, tech sales, things like that?
Jeanne DeWitt Grosser: Yeah, that’s probably where most start is getting sales or frankly just because a lot of companies also start PLG, you might actually start with marketing and then you’re layering in sales when it’s time to do the sales assistant and ultimately sales led portions. So I think it can, depending on your product and your initial target market, it can either mean marketing or sales or a combination of those two.
Getting Sales Reps Talking to Customers
Lenny Rachitsky: Awesome. So essentially it’s like the term go-to-market tells you what we’re talking about. How do you take your product to market, get people aware of it, using it, sticking with it?
Jeanne DeWitt Grosser: Yep, absolutely.
Preventing AI Emails from Becoming Spam
Lenny Rachitsky: What has most changed in the world of go-to-market over the last few years? You’ve done this for a long time at Google, at Stripe, you built it for sales team, now you’re doing that at Vercel. What’s changed most in the skill and art of go-to-market?
Jeanne DeWitt Grosser: There are a number of things. So when consumption-based business models started, I think you saw go-to-market shift into being meaningfully more consultative because often that first land was the very beginning of the journey and represented a very small percent of what you were ultimately going to do with that customer. And so you had to go from being transactional to a lot more. You had to more deeply understand what that customer was trying to do so you could align that ultimately to your product. I think that has played out that much more with an AI because right now everyone knows they need to change, but they don’t necessarily know exactly what they need to change to, whether that’s their customer-facing product or their internal productivity and workflows. And so I think you’re seeing a lot more of go-to-market orgs leaning into the art of the possible best practices, helping you actually think things through as if they were a consultant.
And so one of the things you see more of right now is forward-deployed engineering, which on some level is kind of a rebrand of professional services but kind of not. And a big part of that is, hey, how do I actually get into your environment, ride alongside you better understand what you’re trying to do and then help you actually bring the technology to life and learn a lot along the way.
Often you’re not only making that customer successful, but you’re then taking all of that back to your product and engineering organization to figure out, okay, what was generalizable that we ought to build into our offering versus what is something that ultimately is going to be more of a professional service in the fullness of time. So I think that has been a biggie, is actually just really getting embedded with your customer. And then unsurprisingly, I think bringing AI to bear on the sales process is another big one. And so you’ve seen the rise in probably the last 18 to 24 months of the go-to-market engineer, which different folks define slightly differently, but it’s kind of bringing one technical prowess to bear on go-to-market in general so you can have a lot better tooling, data use, et cetera. And then two, increasingly bringing AI to bear as well to re-architect your workflows and also make it so that it’s easier to have a personalized experience with customers but do so at scale.
Results: From 10 SDRs to 1
Lenny Rachitsky: Amazing. Okay, let’s follow the thread on this go-to-market engineer, so what was it like before and what are these engineers doing at companies?
When to Hire a GTM Engineer
Jeanne DeWitt Grosser: So I think maybe an interesting story to tell. When I was at Stripe, we went to launch an outbound SDR function. So outbound prospecting and Stripe always ran lean. The company at that time had an operating principle which was efficiency is leverage. And so if you looked at the sales organization I was running, most companies out there probably would’ve had 30 SDRs and I was going to get four. So there’s no way I was going to do the typical SDR approach and be successful. And so we thought to ourselves, okay, what can we do? We’ll be super data-driven. And so we went and we started building project Rosland. Rosland is the scientist who originally mapped A-DNA. And what this was was effectively a company universe. So you can think of this as a massive database. Every row was a different company on the planet and every column was an attribute about that company that would help you sell to them in a more targeted fashion.
So at Stripe an example would be knowing that their business model was a marketplace was super helpful, because that would mean you wanted to sell Stripe Connect versus vanilla payments. And so the goal was basically, hey, can we create a mad Libs where I will come up with sort of a predefined email template, but 80% of it will be fill in the blank based on the different attributes of that customer. So if they’re this industry or this business model, then pull this customer, reference this value prop, send it to this persona, not that. And we were trying to do this in 2017 and it was very hard and didn’t actually totally work our ability to the false positive rate and we worked deeply with DSI and it just never really got there. And now that we’re literally redoing here at Vercel as we speak and it actually works and you can bring AI to bear on it.
And so what’s different is we now, I have a data scientist just like I did back in 2017, but I have a go-to-market engineer whereas before I just had someone in systems that was helping me configure outreach or sales off and my go-to-market engineer is helping me build an agent where we’re coming up with, okay, well what’s the human workflow that you would’ve done? And then how do you encode that using Vercel workflows as an example in actual code that’s both deterministic and less so where an agent’s going out and trying to replicate what a human might’ve done to produce that, fill in the blank, matlit.
When to Hire Your First Sales Rep
Lenny Rachitsky: I love the ambition of that project. What is this, like eight years ago?
Jeanne DeWitt Grosser: Yes.
The Ideal GTM Engineer Profile
Lenny Rachitsky: I love the big thinking there. We’re going to map the entire universe of companies and then here’s how we sell to them. And then just I’m trying to picture doing that without AI. It’s like crazy to imagine trying that without AI and that’s so much simpler to even imagine.
Jeanne DeWitt Grosser: Well the thing that’s amazing about that, just to geek out on a second, so I was working on that with a bunch of folks at Stripe on my team, obviously at a gentleman named Ben Salzman who went on to go to ZoomInfo and then actually recently just founded a go-to-market startup that is basically sort of productizing that concept of a company universe and then layering AI on it on top of it. And ultimately his view is actually AI will get to the point that you won’t have to do outbound prospecting because it will just sort of company and product match. So it’s fun to sort of see back in 2017 some of the folks doing that now work at OpenAI, they work at Anthropic, they also are doing GTM Eng. You’ve got him starting a totally AI native GTM company and then here I’m at Vercel trying to do the same.
The GTM Tech Stack
Lenny Rachitsky: Okay, so what’s cool is this is an emerging role, an emerging skill that I don’t think a lot of people have recognized as something that is happening. So one example I’m hearing of what this role does is they automate outbound emails essentially and outbound outreach. They figure out, they write workflows and agents that figure out here’s the company to go after, here’s how we message them. Does that end up being kind of like an email that’s custom designed and written for this prospect?
Using Agents to Diagnose Sales Bugs
Jeanne DeWitt Grosser: That’s one version. So it’s broader than that really. Basically the full remit of GTM Eng will be to go through each of the different functions within go to market and break down all the different workflows that they do and then turn those into agents where AI is better placed than the human to do that task. So right now we started with actually inbound and are now moving to outbound because that workflow is most legible. And by legible I mean you can basically write it down. It’s relatively replicable, mostly deterministic. So it’s more likely that AI will do it well and we actually built the agent and then we keep a human in the loop. But from there we’re starting to look at outbound and with an outbound we’re starting more at the lower end of the market, where you tend to have slightly less customization because there’s a single decision maker at the company.
But I think it’ll take a while before we’re able to really do that in a very large enterprise. There we might use an agent for research but maybe not all the way to actually send a message and that’s just within the prospecting function. So other places that we’re looking at this would be for install-based sales. So again there it’s a little bit more deterministic because you’ve got awesome internal data on what a customer is and isn’t using, what’s the next best action? What’s the thing they should get most value from? So that’s where we’re starting to map, hey, what does that ideal workflow look like? But basically you want to get to a state where as long as I’ve been in sales, they release these annual reports that help us all benchmark ourselves relative to one another. And one of the stats is what percent of time do your sellers actually spend in front of customers?
And for the 20 years I’ve been in sales, it’s always been somewhere around 30% to 40%. So the minority of time is actually talking to other humans and I think we’re getting to a point where with layering in agents, ideally we finally get salespeople to a point where they’re actually spending 70% of their time interacting with humans and we can get the research, the follow-up, the things that are a little bit more rote and don’t use the entirety of your human capacity done by an agent and then sort of unleash you to go deeper with your customers.
Lenny Rachitsky: I love that this is such a great example of where AI is contributing in a very meaningful high ROI way, taking on all this work that people… like, you have to hire say 50 SDRs as you described to do and now you could do with a lot more. So it’s a really cool example of leverage that AI gives you. One thing that I know a lot of people think about when they hear this is, okay, I’m going to get more of these really bad emails trying to pitch me on stuff and just like this isn’t going to work. I can tell this is AI. What have you learned about how to do this where people actually receive emails that actually convert and do well?
Insights AI Sees That Humans Miss
Jeanne DeWitt Grosser: Our processes all always have human in the loop. And so basically where we’ll start is we take a go to market engineer and we have them shadow the highest performing individual in that function. And so you can go and you shadow an SDR and you can see, oh wow, they’ve got seven tabs open. They’re looking up the person on LinkedIn, they’re reading about the company, they’re doing chatGPT on this, they’re looking in this database to get these sets of attributes. And so that’s how you sort of inform the initial workflow. And then what we do is we let the agent make a call. So in the specific example with inbound, you have to determine whether or not you think the lead is likely to be qualified and then you have to determine what to say to it. And so we’ll let the agent make those two calls.
It ultimately then does some deep research, pulls in a bunch of information from our databases and crafts a response, but we have a human review all of those and actually hit send. Now for us, we had 10 SDRs doing this inbound workflow and now we just have one that is effectively QA-ing the agent. The other nine we deployed on outbound, so we got to move them up the value chain. At some point I think we’ll get to a place where we feel like, “Hey, the human reviewer is saying yes enough of the time that we feel confident that these will be on brand targeted, et cetera,” but right now we’re still trying to train the agent and it incorporates feedback on what we choose to reject, edit, et cetera.
The Build vs Buy Dilemma
Lenny Rachitsky: And you shared that it’s already having a lot of impact. Like you said, you had 10 SDRs and now one can do the job of 10.
Jeanne DeWitt Grosser: So before we did that move, I mean the other thing that’s just incredible about this is the person who built the lead agent was a single GTM engineer. He spent maybe 25-30% of his time on this. It was six weeks before we felt confident going from 10 to one. So it wasn’t like this was a multi-quarter process, it actually moved super quickly and then again now we just sort of keep that agent manager working with the agent to get it to a point where we say, “Hey, we’re ready to roll.” Actually throughout the process we also tracked all of the KPIs that you typically would hold an SDR accountable to. We were looking at our lead to opportunity conversion rate, we’re looking at the number of touches it takes the time to convert, and basically what we were able to do is hold that lead to opportunity conversion rate flat. So the agent is as good as our humans were, but it’s actually condensed the number of touches it takes to convert because it’s so much quicker at responding relative to leads inevitably sitting in the queue or coming in at nighttime and no one can get to it, that type of deal. So that’s sort of when we knew it was ready to pull nine people off and shift them into outbound.
How to Build Agents Fast
Lenny Rachitsky: That’s incredible. Okay, that’s interesting. So you shift them to outbound. What I love about this is this SDR that is now doing this is, as you said, doing the things they enjoy more, they’re talking to customers more, they’re not doing all this kind of top of funnel rote work. I don’t want to get into whole jobs AI discussion, but there’s always been this talk about AI SDRs basically replacing SDRs. It feels like that’s one thing where everyone’s like this is a hundred percent going to be AI in the future. What I’m hearing here is it gives one Aster a lot more leverage and obviously you still need people running the show. Thoughts there? Just like do you think AI will replace all this at some point? And then I don’t know, you don’t need salespeople?
Jeanne DeWitt Grosser: I think on prospecting it can replace a fair amount because the average SDR wasn’t doing overly sophisticated research in the first place. So where I, think the last part to go as I mentioned will be in deep enterprise prospecting where you can be at multiple layers in an org chart, you’ve got to pick between business lines, you’ve got to triangulate those. But I do think for the things that are more repetitive that often don’t take that much time to learn and get ramped, AI will be good at that. And in my view, no one graduated from college and was like, “Yes, I just went to college for four years to become an SDR.” It was more, “Okay, that’s where you are forced to start.” But I think the average SDR could have gone straight into outbound or straight into an SMB closing role. And so basically what we’re just doing is shifting folks into something that uses more of their full capacity right out of the gates rather than sort of the forcing function of working your way up the totem pole.
Building Your GTM Like a Product
Lenny Rachitsky: Awesome. Since a lot of people listening to this aren’t salespeople don’t have a lot of background in sales, we’ve used this term SDR, there’s also the term AE. Can you just help people understand what is an SDR, what do they do, what’s an AE, and then what’s the role above?
Creating Value at Every Touchpoint
Jeanne DeWitt Grosser: Sure. So SDR is typically in charge of generating pipeline. They’re meant to talk to prospective customers and get them to a point where it is worth investing time to run them through a sales process. You typically have two types of an SDR, have an inbound one. So this is where people come to your website, they fill out contact sales, they’ll be the first call to make sure that it’s actually worth a more expensive account executive to go and run a sales process or you then have outbound. So this is where when you want to grow faster than your inbound demand, they will go out and at this point you probably have a point of view on where you think you have product market fit. And so they will target that part of the market and try to drum up interest from folks who weren’t otherwise raising their hand saying, I’d like to talk to you.
So that’s sales development basically. Pipeline generation account executives are closers. So it’s their job to take somebody from, “Okay, hey, I’m interested in learning about your solution, I have a legitimate problem. I potentially could make a decision,” to, “I now believe that your product is the best in the market for me and I’m willing to pay for it.” And then account executives, depending on the segments that your company sells into E.G. small business, mid-market enterprise, et cetera, they may work their way up the food chain from selling to a smaller company like an SMB or a startup. Those tend to be a little bit more of a transactional sale. You often have a single decision maker to then going into a mid-market or a commercial role where now maybe you have an economic buyer like somebody in finance and a technical buyer like somebody in engineering to getting into enterprise where you have procurement and you have committees and 10 people have to weigh in and you’ve got to help them figure out how to de-risk the fact that they’re probably migrating from something so much more complicated coordination effort to sell.
Lenny Rachitsky: That was extremely helpful. So SDR, pipeline generation, i.e., closer. Such a simple way of thinking about it. Okay, this is great. Going back to the GDM engineer, a few questions for people that may want to try this at their company, what scale do you think it makes sense to start hiring for this role? Having someone automate in the go-to-market process?
Delivering Unique Insights and Best Practices
Jeanne DeWitt Grosser: What’s interesting about this is it will force companies to be more rigorous about their sales process early. So often startups when they go from founder led sales to say, I’m going to have my first sales person, whether that’s an actual account executive who has sales experience or your general athlete, wicked smart, who’s going to go figure it out. Often founders will just say, “Okay, sales is showing up and talking to people. Isn’t that what I just did for the last couple of years?” But actually sales is more than that. It’s a skill just like writing code as a skill or building a financial model as a skill, it’s about discovery. So asking all the right questions that help you identify challenges in pain, willingness to pay, et cetera, and then going through a process to handle those objections and showcase where are you at enough value such that somebody ultimately wants to hand over some money.
So often startups will get, particularly ones with strong product market fit to pretty significant scale without really having a replicable process. And you can’t really apply go to market engineering unless you actually have a point of view on what best practice should look like. And so I think basically this is going to force folks to have more of a playbook out of the gates, what’s working, what’s not? Can I document it? Do I have content for the different parts of the sales process? And then once you do that, which maybe 10 people is a good size and scale for that, ostensibly a GTM engineer can come in and turn that into an agent. You could also argue that if you’re a founder who wants to bring in a general athlete profile and that person is technically minded, that you could have a hybrid AE GTM engineer who figures out what their best practice is and then tries to turn that into an agent that’s riding alongside them and making them more effective as well.
So I don’t know that I have a point of view yet on what’s the optimal size and scale, but I forever have given founders the advice that you often want to bring in revenue operations, which is basically the analytical arm of sales earlier than you think because having data, having process is actually what gives you insights as a founder into what is and isn’t working. And so I would argue just like it’s a good idea to have that sooner than later, increasingly it’ll probably be a good idea to have GTM engine and be looking to bring agents to bear on your process at the outset.
Data-Driven Insights: The Vercel Way
Lenny Rachitsky: While we’re on this topic, just a quick tangent, the advice for hiring your first salesperson that I usually hear is wait until you’re around a million in ARR. When you have a repeatable process, you can teach someone anything there. Does that seem right? What would you recommend?
Jeanne DeWitt Grosser: Yeah, I think that seems about right. I do think as a founder you want to stay deeply connected to customers and get it to a scale and get it to a point where you use the word, there’s some repeatability there. I think that’s one of the things that not all founders get right is founders are incredible salespeople. They convinced a VC angel investors to fork over a bunch of money, so clearly they’re going to inspire people to buy. But if you’re getting to a million in ARR and the set of customers you have look nothing like one another, you still have very much like an evangelist sale, very much founder led sale versus if you can say, “Hey, I now have an ICP here, or ideal customer profile, e.g something you can write down. We are good. Our product fits with startups with less than a hundred employees who are typically building SaaS applications,” something like that.
Then you’re probably ready to hand over the reins. And then what founders have to remember is to actually hand over the reins. So you’ve got to enable the person who comes in, what is it that you’re doing effectively, what’s your content, what are the discovery questions you are asking? How are you handling objections so you can transition that knowledge but also don’t handle them over entirely. You want to stay connected to the customer because you still have a fair amount of R&D to do to figure out where is the product next going to resonate, where are you getting stock as you scale, etc.
The Core of Buying: Risk Avoidance
Lenny Rachitsky: To close the loop on the go-to-market engineer, what’s the profile of the ideal go-to-market engineer, may be your first.
Jeanne DeWitt Grosser: What we have found works really well is somebody who does have go-to-market experience. So at Vercel, our first three go-to-market engineers we’re actually sales engineers. So Vercel hires very technical sales engineers, all of them were front end developers before they decided they wanted to get into sales. And so we just said, “Hey, three of you, congrats.” You’re now founding members of our GTM Eng team. And the thing that works well there is you do understand aspects of what is good GTM, what does a process look like? It’s been really interesting actually. So the gentleman who runs GTM Eng for me, we were going through this lead agent and QA-ing it. And so I’m going and I’m looking at some of the responses that we’ve ultimately had the lead agent send and realized, “Oh, I wouldn’t have sent that and that’s because I have 20 years of sales experience and we modeled the lead agent off our best person, but our best person who has two years of sales experience.” So it actually is important to understand the art and the science of sales and how you bring best practice to bear. Either you’ve done it and so you know some best practice or you’re going to geek out on sales, read a bunch of books, learn a thing or two, and try to incorporate some of those into your agent development.
Intro to Customer Segmentation
Lenny Rachitsky: That is really interesting. So come from the sales side, not from the engineering side. And I imagine this is such a cool opportunity for salespeople to do something completely different and move closer to engineering.
Customer Segmentation Frameworks and Dimensions
Jeanne DeWitt Grosser: Yeah, I mean we’re having a lot of fun with it. At Vercel in particular, we basically get to be customer zero. So everything that we’re building with agents, we’re building on Vercel’s AI cloud. So these agents now have multiple steps that they go through. So we’re using Vercel’s workflow SDK and workflow offering. We use the AI gateway to call the different models that we use to do deep research or other enrichment that we do. So for us it’s great because we basically sort of bang on everything the engineering team is building and get to go be a discerning customer before we actually get it out the door to real customers.
Customer Segmentation at Vercel
Lenny Rachitsky: What a fun time to be alive. I could tell the fun that you guys are having, just from the way you describe it.
Stripe handles the massive scale and complexity of many of the world’s fastest growing enterprises, including 78% of the Forbes AI 50 and more than half of the Fortune 100 enterprises like Atlassian, Figma and Urban Outfitters use Stripe to create fully branded and customized checkout pages with access to more than 125 global payment methods. There’s a reason I’ve had more leaders from Stripe on this podcast than any other company. They know how to build great products that scale and that people love. And Stripe is a lot more than payments. They’ve also got a category leading billing solution and a highly optimized checkout experience built specifically to increase your checkout conversion. Join the ranks of industry leaders like Salesforce, OpenAI and Pepsi that are using Stripe to grow faster and to grow the world’s GDP, learn how Stripe can help your business grow at Stripe.com. Zooming out a little bit in terms of you mentioned tools and tools that you use. I’m curious just what are kind of the state of the art tools within the go-to-market stack that you love that you’d recommend?
Jeanne DeWitt Grosser: Well, so I’m going to have an interesting answer to this, so I’ll give you one. And it’s not state-of-the-art per se, although I don’t mean that disparagingly, it’s just that it’s been around for a while now and a lot of folks use it, but I think Gong has gotten just meaningfully more interesting in the last year. And then second half of my question I will get into, I think the calculus on build versus buy is changing. So all right, Gong. Gong is incredible because you can run agents against it now. So we take all of our Gong transcripts and we dump them into an agent called the deal-bott, and that deal-bott then can do a bunch of things. So the first thing we had it do was lost opportunity review. So we had just finished Q2, we had a list of our top losses for the quarter sorted by deal size, and we ran it against that and it was incredibly interesting.
So the biggest loss that quarter according to the account executive was lost on price. And when you ran the agent over every Slack interaction, every email, every GONG call, it said actually you lost because you never really got in touch with an economic buyer. And when you talked to somebody about ROI and total cost of ownership, it was clear from their reaction that they didn’t really buy your mass. And so really the reason we lost was an inability to demonstrate value, which upon reflection I’ve got work to do to build out how we quantify the value of Vercel, which actually is very easily quantifiable. It’s one of the things I love about selling this product, but we got to codify that for the go-to-market team. So that was incredibly interesting and now we run it against all of our lost opportunities and actually do a much better job of categorizing why it was we really, really lost.
And then either feeding that back into the engineering team or back into marketing sales leadership on, hey, where are we falling short in the sales process? And so that was awesome, but then we’re like, well, it’s not very fun to lose, so why don’t we pull that forward? And so we went from lost bot to deal-bott and now the deal-bott is running in real time and we basically feed insights into Slack. Vercel is incredibly heavy users of Slack, so we have a channel for every single customer, either opportunity or existing one. And so now we’re feeding insights into that Slack channel which is, “Hey, you’re this far into the sales process and you haven’t talked to an economic buyer, you should think about that.” Or, “Hey, you just got off that call with an economic buyer, didn’t sound like it went that well. Here’s some things to consider and how you might follow-up.”
And last thing before I pause, the other thing that’s really interesting and how we’re using this too is we are in this moment where I have never seen an iteration velocity exists now in my career. My 20 plus year career has all been in tech. And so for go-to-market teams, that’s really hard. If you are launching something every other day, the ability to be enabled on that is actually quite challenging. And so this bot agent is now also letting us, where we’re starting to go with it is we’ll release something, we’ll do our best to enable the team, then we’ll go run the agent across calls, interactions, and we’ll diagnose where we did a bad job of objection handling, where we’re getting stuck. And then at the end of the week we can have a huddle and say, okay, what are all the places that our agent would suggest we aren’t selling effectively?
And then almost like an engineering team, we’ll now run sprints, which is like those are just bugs. They’re bugs in your go-to-market process, so you should not have them. And by the next week we’re going to add content to our objection handling to guide. We’re going to add content to a discovery guide, we’re going to figure out something we need to change about our demo, so on and so forth. So that’s early. That’s a little bit of a preview, but that’s where we’re talking about taking things right now within our go-to-market orgs.
How to Start Customer Segmentation
Lenny Rachitsky: Jeanne, you’re blowing my mind in so many ways, it just sounds so fun and just you guys are going to win is what I’m feeling when I hear all this. Incredible. What I love about this is this AI tool, this agent you built sees things that humans were not seeing. The fact that you were surprised of just like this is a completely different conclusion is such a big deal. This is the whole promise of ai, it’s going to do things we aren’t even thinking about or capable of.
Customer Segmentation is a Company-Wide Effort
Jeanne DeWitt Grosser: It is. We had a really interesting, one of the things we’re doing at Vercel, we have an AI cloud, so people use that to put AI-native features into their customer-facing applications, but they’re also using it to build internal applications to improve productivity or outcomes. And we are talking to a very large airline and that airline obviously gets tons and tons of support queries. So of course they would want to go apply AI to hey, how can we have AI answer these so that our cost to support goes down, sort of the obvious thing. But the more interesting conversation was actually with one of the C-level executives who said, we also actually transcribe every single one of those support calls. And so what I really want to know is why are they calling and how do I make it so that fewer people call the next week? And so again, this is now with AI, you can rapidly go through all of that content and actually be able to much more quickly than having a human in your CRM sort of pick some status why it was that folks were calling the airline this week and what if anything you can do to make it less the case next week.
Making Sales Earn Engineers’ Respect
Lenny Rachitsky: I imagine many people hearing this are like, “I need one of these deal-botts and lost bots.” These are all internal products that you all built?
Jeanne DeWitt Grosser: Yes.
The Current State and Limits of PLG
Lenny Rachitsky: Is there anything that you’ve learned about making them this good? Any tips you can share of here’s how to make a really good bot for sales?
Thinking of Pricing as a Product
Jeanne DeWitt Grosser: Yes, so actually that’s the second half of my answer that I forgot.
Sales Compensation and Hiring Strategies
Lenny Rachitsky: That’s perfect.
Jeanne DeWitt Grosser: Which is sort of like bill versus buy calculus. So I think one of our learnings is that it’s not that hard to build these agents and they aren’t that expensive either. So I mentioned the lead agent that was a six-week process with one human, a third of his time, that deal-bott, the lost bot version was two days basically we riffed on it, he had it 40 hours later. Now we’re continuing to refine it for the other things I mentioned. And what’s also interesting about them is they for better or for worse for Vercel, but that lead agent which runs full stack on Vercel, will cost us about a thousand dollars to run for the entire year. If you remember I told you we had 10 people in the SDR function, so I’m paying well over a million dollars for that from a salary perspective.
I got that down to one. And then behind that I have a lead agent that costs a thousand bucks. So that’s like a 90%-plus reduction in total cost there. And there’s lots of software for agents out there right now. And I think one of the things we’re learning is because this whole space is so nascent, often your own esoteric context, your content, your workflow is really key to unlocking the power of the agent. And so I think there’s real value in experimenting with your own internal agent development. We may ultimately end up on better integrated agent platforms in the fullness of time, or we may find that the CIO increasingly goes from a procurer of software to a builder of software and you’ll have an AI internal platform with a thousand agents running across your org. I’m not really sure yet. But I certainly think there’s value in trying it yourself because you may find that it’s meaningfully easier than you think and you get returns pretty quickly.
Life Philosophies and Competitive Drive
Lenny Rachitsky: So what I’m hearing here is that you’re finding that there are not tools out there to plug and play. The alpha is essentially in building your own stuff.
Jeanne DeWitt Grosser: I think that’s partially true, and I think because you also have all these tools proliferating right now, you get into the perennial problem where you wind up with 20 of them to do the 20 jobs to be done basically, rather than an integrated platform that’s doing all of them. I’m hearing this a lot actually when I’m talking to customers right now where their biggest issue in deploying AI is actually just getting through procurement and it’s because got an AI mandate, you kind of have a blank check. I recently heard the term of instead of ARR, it’s ERR, which is experimental run rate revenue, which is to say everyone’s out there sort of, Hey, we’re going to give this thing a go for a year and then TBD on whether or not we keep it. But basically you’re having to procure 20 different things. Most things are getting off the ground and so they’re solving something relatively narrow and that’ll change in the fullness of time. But I do think there’s an opportunity to figure out, hey, where do I likely have a more specific workflow internally. For that it might be worth building your own agent and then maybe for the things that are a little bit more generalizable, you go get something off the shelf.
Lenny Rachitsky: Are there any platforms or tools that you want to shout out that allow you to build these agents so quickly? I know they sit on Versel, so shout out Vercel. But just anything that you point people to you to… These SDR, these GTM engineers, they’re former salespeople. Are they learning to code? Are they byte coding these agents? How does that work?
Jeanne DeWitt Grosser: So our sales engineers all have CS degrees. So they were engineers in a sales capacity, so they’re writing code and actually these agents, they’re building directly on Versel. So you get the AI gateway that lets you call different models. You have a sandbox if you’re running untrusted code, you’ve got workflows that let you build the process. You’ve got fluid compute, which lets you really efficiently use compute when you only need it. So we’re just sort of building it from the ground up here. Again, it’s not that hard. Now you do need to write code for that. Certainly there are a lot of vibe coding tools out there that also give you more workflow builders that are somewhere between fully WYSIWYG, almost like drag and drop and a little bit more code forward. So you’ve got a bunch out there along those lines. But I do think we’ve sort of found one of the reasons actually the GTM Eng team at Versel can build these agents so easily is because the Versel platform is making it that easy to use our framework to find infrastructure and get that agent onto into production very rapidly.
Lenny Rachitsky: What a neat, unfair advantage you all have to do this stuff.
Jeanne DeWitt Grosser: Yes, it is fun to… I mean, I do think this company is better than any I’ve seen at eating its own dog food and just everyone is constantly, we say Versel builds Versel with Versel. So you’re just always looking for ways to, Hey, how can we use our product to go do what we need to do? And as a result, either understand then what a customer would want or what’s missing from our product that we could go make better.
Lenny Rachitsky: Along these lines, something that’s already come across a lot in the way that you described this stuff is I’ve heard a lot about how you think about go-to-market as a product. A lot of people listening to this, as I’ve said, are product builders. So I think this is a really nice way of thinking about go-to-market. I’m guessing you’ve already talked about elements of this, but just what’s a way to think about go-to-market as a product?
Jeanne DeWitt Grosser: Yeah, I’ve always, so I had this realization probably a little over a decade ago in my career. So my first job out of college was working on Gmail in 2004. So Gmail launched on April 1st, I joined on June 1st. And as I’m sure you’ll remember as well, Gmail was this incredible innovation, massive JavaScript application that didn’t really exist at the time. And it had this gig of storage. It was a full year before Yahoo Mail caught up and even longer before Hotmail and others did. So that was the level of technical differentiation between Gmail and the next best. And a decade later, you had cloud computing enabling folks to do stuff that you never would’ve been able to do previously. And so I kind of felt like, huh, software’s starting to commoditize a little bit. And so when that happens, when technical differentiation kind of narrows, what are other things that will differentiate you?
And I was started thinking outside of tech, we buy a lot of things because of how we feel about them. And so I started to develop this thesis that actually the experience that you have of being sold to will increasingly actually differentiate a company and drive buying decisions if products are only different at the margin. And so if you believe that, then you really want to create a customer buying journey that feels like very unique experiences. And so we did a lot of this at Stripe and now we’re looking to replicate this here. But an example of one of the things I think we did really nicely at Stripe was a lot of companies sales, the first call after you’re qualified, we’ve decided you’re worth engaging in sales process is discovery, which is basically let me ask you a lot of questions to try to under-uncover paint, figure out where buying power lies, et cetera.
And so that is kind of boring sometimes for a customer. You’re basically being quizzed often on the phone. And so what we started to do at Stripe was that first session was a whiteboarding session, and we would actually get together and have you draw your architecture for payments and all the other things that were under the hood to enable you to take money and drive customer outcomes. And through that we would learn a ton about what was in your stack, what we were going to have to compete with, displace where value lied. But the customer also learned a lot themselves because in many cases they’d never drawn their architecture diagram. And so they left that meeting with an asset and a sense of like, “Wow, this is a really collaborative person who’s deeply interested in helping me develop a mental model for how to think about this.” And then we had other things that we would do.
So that’s sort of how I think about building go-to-market-like a product is basically you need to go through from the first time you become aware that the company exists to again, that sort of five-year heavily retained wall-to-wall customer a set of experiences. And those experiences can feel transactional, flat, boring, or they can feel very human, personalized and unique. And so we try to go map those out and figure out how do you bring the product to bear, make it really human, and hopefully that creates a customer for life in the end.
Lenny Rachitsky: I love that whiteboarding example. Are there any other examples of what you’ve done to make it actually work really well in this way?
Jeanne DeWitt Grosser: Yeah. Another principle, we really developed this at Stripe too and I brought it to Vercel, was just the idea of adding value at any touch point regardless of whether or not that customer bought. Because even if customers don’t buy, you often find that if you miss them on that buying cycle, three or four years later when they’re in another buying cycle, they do come back. I was at Stripe for nine years and so I saw the number of customers that we lost and then half a decade later, here they are and they bought. So that was sort of another one. So examples of this that were doing at Vercel is there’s great data on the internet that helps people understand the performance of their website and how fast your website is actually impacts SEO. And SEO impacts AEO and everybody’s thinking about AEO right now. And, so one of the things we try to do when we reach out is actually give folks insight immediately into how they’re performing on an absolute basis, how they’re performing relative to peers. So ideally that piques your interest and you want to learn more from us, but even if it doesn’t, you still have insights that you may or may not have been aware of that maybe make you contemplate whether or not you’ve got the optimal setup.
Lenny Rachitsky: Awesome. So what I’m hearing here is when you say, think of it like a product that’s basically a product person thinks about the experience of their product, that every step of the journey, here’s the flow, step 1, 2, 3, 4, 5, how do we make every step awesome, keep them going along that journey. And so what you think about is just from the prospect’s perspective, how do we make every step of that journey awesome, continue them down that journey.
Jeanne DeWitt Grosser: Yeah. How do you make it be an experience rather than a transaction
Lenny Rachitsky: Versus just feel like sales coming at you trying to sell you stuff?
Jeanne DeWitt Grosser: Yeah.
Lenny Rachitsky: Okay. Staying along this track of staying tactical, I want to go even further there. So what are just some go-to-market tactics that you find really effective these days for people trying to just to be more successful in getting people to pay attention to their stuff, to buy their stuff?
Jeanne DeWitt Grosser: I mean, one I would sort of say dovetails with where I just ended, but is what are the unique insights that you can bring to bear about your product or how that customer may be in a suboptimal state? So I do think investing in data to tease that out is one thing. I think the other thing this is straightforward but often not done enough is a lot of good companies invest in docs, good thing to do, but they stop there. And particularly if you are selling into a slightly larger company doing things like, AWS calls it well-architected guides or blueprints, a lot of customers, particularly larger ones, really want to know the best practice for how exactly to implement your product with their particular setup. A great example of this, this is from Stripe, was Stripe was excellent at marketplaces. Most, Lyft, Instacart, DoorDash, they were all on Stripe.
And so Stripe definitely knew the best way to set up payments for a marketplace because we’d seen them all. And so when you then would go and sell a marketplace and say, “Oh yeah, we’ve got docs, go check them out.” They didn’t like that, because they’re like, “Hey, every marketplace runs on Stripe. I don’t want to look at generic docs. I want you to tell me what’s the best way to set up payments for a marketplace.” And so I think that’s another key thing to be doing, particularly as you move past that sort of solo developer startup founder as potentially a target audience.
And then, I don’t know if this is a tactic per se, but I do think just a good reminder for founders in particular who are still in that maybe founder-led sales moment is just the value of really good discovery. I often find founders are so excited about talking about their product or you ask one question and now they’ve got a hook of like, oh, I can fix that for you. But excellent salespeople typically will talk well under half the time in a conversation because they’re out asking questions, probing often helping a customer arrive at conclusions on their own. And so learning how to do five why’s, go deep rather than immediately going into problem solving mode. If they ask a question, you respond often. If they ask a question, you should ask a question about the question and then respond. So learning to be great at that, I think differentiates people.
Lenny Rachitsky: So the last tip, I think there’s something a lot of I bet everyone could learn is just listen more and talk less.
Jeanne DeWitt Grosser: Yep.
Lenny Rachitsky: On that first piece of advice, this kind of sharing unique insights and how your suboptimal, is there an example you could share of how you did that? Maybe a story of just how you convinced someone you’re selling Striper or Vercel like care or something you’re missing. Here’s how this could help you become much better.
Jeanne DeWitt Grosser: So with Vercel, sort of giving an example, but I’ll make it more specific. So the performance point, you can go and look at core web Vitals, and so we can actually see the different things within their site that are fast or load correctly, et cetera, so anyone can go look that up. But what we can do is actually then help with benchmarking relative to peers. So that’s been a big one that we’ve gone out and done. The other one that we’ve spent some good time on is just around helping customers understand MCP servers and when it would make sense to use one. So I think those are all the rage, but often people don’t know how to contemplate them within their own product. So that was another one that we’ve gone pretty deep on and then related to, the first one is AEO Answer engine optimization is actually somewhat tangential to Vercel right.
So we drive performance, performance drives SEO. SEO is an input into AEO, but we have spent a ton of time sharing insights on AEO because we ourselves focus deeply on it and think we understand it better than many. And so again, as part of just building a trusted relationship, folks may go from those AMAs or that content into, okay, great, you taught me a lot and therefore I want Vercel to help me with performance. But in many cases, they actually now are just like, “This is a company that seems insightful, it seems like one I can learn from, and now I’m going to pay a little bit more attention to them.” And over the fullness of time, maybe they see something that triggers them to decide, “Now is the time I want to go investigate that aspect of Vercel.”
Lenny Rachitsky: Awesome. So what I’m hearing here in many ways, and this resonates, I had Jenna Abel on the podcast recently and it was all about sales skills and how to sell.
Jeanne DeWitt Grosser: Nice.
Lenny Rachitsky: And one of her tips is you don’t want to be focusing on here’s the pain and problem we’re solving and instead focus on here’s how you will be better than your competitors. Here’s the big gap and alpha that you can achieve. If you use Vercel, you were missing out on speed and you’re going to get screwed in AEO and all these things. Here’s how you can architect your entire payments system to be top tier. Does that resonate?
Jeanne DeWitt Grosser: Yeah, I was told this stat. It’s round numbers, so I can’t imagine it’s entirely accurate, but basically that customers, 80% of customers buy to avoid pain or reduce risk as opposed to the other one out of five to increase upside, which is a good thing again for startup founders to understand. So we all love to talk about the art of the possible, everything we’re going to enable in the future. It’s very exciting. Everyone’s visionaries, but that’s often really a sale that’s going to resonate with another founder. And for everybody else, particularly enterprises, you’re avoiding the risk of not making your revenue target next quarter, the risk of being outdone by the competition, the risk of having brand damage, et cetera. And so it’s really hard actually for many startups to make that pivot because it feels off brand, but it does actually drive more buying behavior, is setting up a little bit of that concern that either I might not be well positioned or again through good question asking. I know exactly where I’m not well positioned and you can help me, that
Lenny Rachitsky: That is such an important stat you shared. This has come up actually before in this podcast that buying, people are buying in large part to reduce risks, to basically not hurt themselves in their career, not hurt the company. That’s a bigger factor in the buying decision than, “I have this problem I need to solve. And okay, thank you, this is solving.” And the way April Dunford came in the podcast and talked about this of just like it’s such a massive career bet. We are going to bring in product X and it’s going to become, like Stripe, let’s say, let’s not talk about Versel. But let’s say Stripe, we’re going to adopt Stripe. That’s a huge decision. If it doesn’t go well, your career is hurt, your manager is going to be mad at you, it’s going to set your company back. So a lot of the buying decision, as you’ve said is I just don’t want to screw this up.
Jeanne DeWitt Grosser: Right. Absolutely.
Lenny Rachitsky: Okay. Along the line of tactics, something that I know you’re a big fan of and help people think about is segmentation.
Jeanne DeWitt Grosser: Yes.
Lenny Rachitsky: This is something a lot of founders struggle with. They know, “Okay, I need to figure out my segmentation strategy and here where we’re going after.” Can you just give us a primer on segmentation, what people should know about why this is important and then how they might approach this.
Jeanne DeWitt Grosser: So segmentation is basically how do you carve up the world of companies that exist on the planet to reason about them where they buy differently? So I’ll give examples from Stripe and Versel to bring this home. So a very typical company segmentation is small, medium, large. That’s a rational way to do things. Small, you often have a single decision maker, medium, a small team, and large, it’s complex, it’s a committee, et cetera. So the buying process does change across SMB, mid-market enterprise, but if you stop there, you are likely missing. But what are the things within your offering that also change the way something gets sold? So at Stripe, there were two ways we further cut the business. Way one was, so think of segmentation as a graph. So X-Access was size, so small, medium, large, y-access was growth potential. And that was important for Stripe because it was a consumption-based business.
So if you were going to grow at 200% year-on-year, you were more valuable to Stripe than if you were going to grow at 8% year-on-year. And so we wanted to spend more time, spend more money going after the 200% growers than the 8%. So that was one that informed your strategy on who you targeted. And then for Stripe, the other thing that we cut it was business model. So are you a B2B? Are you B2C? Are you B2B2B, E.G. a platform or B2B2C, E.G. a marketplace and why is that relevant? Well, if you’re B2B, you are going to need business payments. Credit card was useful for a PLG function or PLG sale, but you were going to need ACH wires, etc. And you probably had a recurring business, so you were going to want Stripe billing. If you were B2C, that’s consumer.
So you’re going to want consumer payments. Apple Pay is super important. If you were in the platform or the marketplace, you were going to buy our connect product. So it helped us basically then craft a more targeted and replicable sales. Vercel, sort of similar deals. So small, medium, large buying complexity. We also do the same thing on growth potential because we are similarly a consumption based business, but for us, a couple other things on the X-axis, we layer in promote, which is one of the things that is observable is traffic, site traffic on the internet. So Google publishes a Crux score, which is basically they have a bunch of data in Chrome, and so they know that Lenny’s site gets a million XC amount-
Lenny Rachitsky: Millions.
Jeanne DeWitt Grosser: … volume that Jeanne’s site does. And so basically if you are a small company but you have super high traffic that’s going to be more complex, Vercel is going to make more money and so we want to promote you.
So great example of this would be OpenAI. OpenAI, I forget these days how many employees it has. Let’s say it’s 3,000, it’s probably more than that at this point, but so that’s going to put it in the mid-market at most companies, but they’re a top 25 traffic site on the internet. So for us, that’s going to push them in our enterprise because we need to go lean in with a much more in depth sales process. And then the other thing we layer on is a workload type. So if you are an e-commerce company, that’s going to be a very different sale. You actually use different language. You talk about product listing pages and product description pages, and you’ve got an order management system as the back end. Super different from a crypto company where you might be running soup to nuts on AWS. And so again, that helps us start to then have a really different buying content for you.
Lenny Rachitsky: Okay, this is awesome. So essentially what you do is you break up this universe coming back to your original story at Stripe to help you sort essentially which companies are most likely to buy your product. And what you’re coming up with is these attributes that are correlated with they’re likely to be great potential customers.
Jeanne DeWitt Grosser: Yep.
Lenny Rachitsky: Do you recommend using this XY axis as the approach versus something else? There’s like a spreadsheet with five columns. I don’t know, how do you start?
Jeanne DeWitt Grosser: There’s probably something to be said for X and Y. like do you think size is going to play into most buying decisions and then these days there is a fair amount of consumption happening? So there’ll be aspects of this that I think are somewhat universal. But I think basically when I came to Vercel, because new product market product offering, for me it’s a new market. I had a lot to learn, but this is one of the first things I did in the first 30 days. And so basically I sat down with the gentleman Abhi who leads data science here and said, okay, what drives revenue? So what are the things that you can look at X ante about a customer to know this person’s likely to pay us a hundred thousand dollars versus a million? That’s probably going to be part of a segmentation framework. And then similarly, okay, what attributes would we look for to cluster where we seem to be winning repeatedly? And that was how we ultimately got at, okay, Crux rank is going to be super important because what you pay Vercel is correlated with your traffic. And then workload type was super important as well.
And for Vercel, when we did that, it was really interesting because we saw, wow, we have a lot of penetration and e-comm not that surprising actually, given that we drive highly performant sites and e-comm having a superfast performance site really matters. But at the time, if you looked at as an example, an enterprise SaaS companies, we didn’t have a lot of penetration, even though you would’ve thought, okay, front-end cloud, very developer oriented. Of course software companies would be on us, but in enterprise, most of those companies built that SaaS offering before Vercel existed. So migrating 2 million lines of code to Vercel, that’s a big lift. So it helped us really understand where are we winning, where are we not? And now as an example, within SaaS companies and enterprise, we’re actually seeing a lot of interest in the AI cloud. Those are some of the earlier adopters of, “Hey, let’s add AI native functionality to our existing SaaS app.” And so again, it helps us figure out what to target where.
Lenny Rachitsky: So essentially you’re doing this regression analysis on what’s working and then here’s the attributes that are most correlated with success. Something I always recommend when founders ask me for how do I figure out my CPE? How do I figure out where to focus, my heuristic is just think of three attributes that narrow them down. So it’s like series A company that’s angel-led, that’s the marketplace, something like that. Does that feel like a good just rule of thumb just to start?
Jeanne DeWitt Grosser: Yeah, I think beyond three, that’s getting pretty detailed and reasonably speaking, you’re not going to cut. You have five sellers. So, what, you’re going to put one seller in five different segments? So I do think three is something you can reason about. The other thing I’ll say on this topic that I think is really important is a lot of times folks think segmentation is a go-to market thing. I really think it’s a company thing. So when you Vercel, I actually deliver and every new hires first week, one of our company values is KYC, know your customer and I deliver the KYC section and talk through our segmentation framework how our customer base maps into those segments because it’s really important as those new product managers leave the room that when they’re building something, they think to themselves, okay, I’m building a new back end product. Who is this targeted at? Is it targeted at an enterprise or a startup? Basically, do I have a point of view on where I’m trying to win and why? And if you’re doing that out of the gates, then it’s much easier to then go speak the same language with the go to market org and figure out, okay, how are we going to take that to market in line with the other emotions that we have in play?
Lenny Rachitsky: Okay, this is a great segue to, there’s a couple other things I want to talk about. One is something I’ve heard from so many people you’ve worked with is that you are amazing at building a go-to-market org that works really well with product and engineering. So I’ll read this quote from your former colleague, Kate Jensen. She said that your superpower is building a sales org that doesn’t feel like a sales org to engineers. So the question she suggested asked just what does it take to do that? What are the ingredients to building a sales org that engineers and product teams really like working with?
Jeanne DeWitt Grosser: The litmus test I have always given my sales team is if you are an account executive in my org and I put you in front of 10 engineers at our company, it should take them 10 minutes to figure out you aren’t a product manager. And what I’m trying to get across is you need to have incredible product depth. And the reason for that is twofold. One, it gives you credibility with the product and engineering org. And two, I also believe that the best go-to-market orgs on the planet are equal parts revenue driving and R&D and D. And the reason I emphasize the latter is if you think about a product management organization, you may have a UXR team out doing research, product managers certainly should be out talking to customers. Well, if I have a 20-person sales team, think of the number of customers that we talk to in a week. And so if we can do an excellent job of translating all of that feedback into signal and then feeding that into the road map, we can be actually an extension of the product management org. But that takes being really good at discerning signal from noise, understanding when something is an objection that should be overcome versus an opportunity in the market. So I think those things have helped.
Lenny Rachitsky: I just love this as a product manager, maybe form a product manager. I don’t know what the hell I am these days. I just love the idea of the salesperson. Like you not knowing the difference between a product manager and a salesperson. The most classic challenge is sales orgs ask for all these features and PMs are constantly having to push back and think about does this fit into everything. So it feels like that’s a big part of this is to understand that deeply.
Jeanne DeWitt Grosser: Yeah, you want a sales org that can think like a general manager, so that’s not just trying to get deals done but is trying to help build a business. And so again, knows when to say no, knows when to do objection handle versus knows, Hey, I’ve actually heard this on the last three calls and I do think this would be a really big unlock that would make us more competitive, would be something that new that nobody’s doing. So I think that takes looking for a profile that both has sales skills but also is going to think with that product mindset.
Lenny Rachitsky: I love that. Okay, so another quote from Claire Hughes Johnson, former podcast guest, amazing sales leader, worked with you at Stripe. She said something along these lines, but a little different. Jeanne is probably the best go-to-market person at connecting with product and engineering, deeply understanding the product and providing the most valuable input to her counterparts of any I’ve ever seen. It sounds like just another ingredient here is just sales feeling like a real partner to product engineering actually, not just being like, “Hey, do these things for me, but actually feeling like a partner.”
Jeanne DeWitt Grosser: Ultimately company strategy is basically product strategy meets go-to market strategy. And so I spend guess as a go-to market leader, I’m constantly trying to figure out how do I make more money more efficiently? And you typically do that by having a winning product in the market that is well commercialized. And so that means that I really lean into thinking about product strategy and thinking about pricing strategy because if those two things are optimal, you’re going to win more often and there’ll be less friction in it. And so that’s sort of where got to put as a revenue leader, like a GM hat on and not just think, how do I sell? But actually how do I enable the insights I’m getting from talking to customers constantly to have the company strategy be more effective?
Lenny Rachitsky: Speaking of product, going in a slightly different direction, PLG product-led growth, it felt like it was very hot for a while where everyone’s like, “You got to go PLG, that’s the only way to win. It’s impossible to do sales. The future is PLG.” It feels like that’s gone away. And in large part, obviously still companies grow through PLG and work through PLG. What’s just kind of your thoughts on PLG and when does it make sense for a company these days to actually think this is how they’ll grow for a while?
Jeanne DeWitt Grosser: PLG makes sense for a lot of companies at the outset, unless you are very explicitly building a product for enterprise. So Sierra as an example, right? They are very clearly going after Global 2000 or something close to that. PLG is not going to be overly useful to them because they are trying to win eight-figure deals from day one. But for a lot of products, folks are targeting a startup audience at the outset and then they’re adding more functionality so that they can ultimately continue to scale up market. So I think PLG is still super relevant. It’s a major driver of Vercels growth. It was a big driver of Stripe’s growth. The thing that folks get wrong is it does typically have a ceiling. So people are generally not going to give you $1 million via self-serve flow. So at some point if you want to sustain growth rates, you’re going to have to have your deal sizes get bigger and bigger. And where I think folks get stuck is waiting too long on PLG because it does take a while to build a replicable sales process and a sales process, which often you’re getting fed by inbound at the beginning and then you got to add outbound. It takes a while actually to turn outbound into a predictable engine. So I think where you see companies hit walls is just when they don’t add the sales portion of it soon enough.
Lenny Rachitsky: So essentially every company ends up having to build a sales org, some start product-led and then at sales, some just start sales and have it from the beginning.
Jeanne DeWitt Grosser: Yeah, I would agree. There are probably some good examples of large vertical SaaS platforms that are SMB, but even they wind up with Velocity sales team. So yeah, I don’t know that I can think of a 100 billion company that’s PLG-only.
Lenny Rachitsky: Yeah, it just feels like you’re leaving money on the table even if you are growing really fast. I know Atlassian was a long-time PLG company but eventually succumbed. I don’t know if that’s the right way to put it. Okay. You mentioned pricing. I know you have strong opinions on pricing and pricing strategy. What’s just a couple of tips you might share with someone thinking about how to price their product?
Jeanne DeWitt Grosser: Yeah, this is kind of on the theme, but I think the first thing is you got to think about pricing like a product. So it’s another one where it actually really matters how you choose to price a product. Do you really understand where customers are going to drive value? Do you really understand where you incur costs? And are you doing a smart job of aligning those things? You’ve got lots of examples of companies grossly underpricing, you’re sort of afraid to charge for the value that you actually provide. I think there are a lot of examples where people default to including a freemium strategy without that actually being a strategy. A good example at Stripe, we launched Stripe Billings years ago. It had a freemium strategy because that’s what you do. And then we sort of looked at it and we’re like, “actually integrating straight billing takes a little bit of work.So if you do that, you’re probably going to stay.”
And so we killed that, killed the free trial to zero downside. So that’s another one. At Vercel, we’ve been going through that transition where we’re a consumption-based business model ultimately, but at the outset we basically kind of bundled that into what looked like a SaaS-like price and as we’ve added a lot more functionality that wasn’t working anymore. And so we did an unbundling and right now actually we did a pretty substantial pricing change in August where we have an enterprise at a pro-skew. And if you looked at the enterprise skew, it’s called Enterprise for a reason, enter, it’s meant to be sold to an enterprise. And actually about half of the folks on the enterprise skew were startups, which suggests that there’s stuff in the enterprise skew that a startup really wants. So we kicked a lot of that stuff out of the enterprise skew and made it so you could buy it self-serve online and what do you know, people are.
So now that’s really driven a lot of growth in our PLG funnel, which is awesome for startups because it’s super efficient. They can just buy things, they want that. It’s awesome for us because you don’t have to have a human intermediate that. So getting all of these knobs really tuned is a key to both a great customer experience and optimal revenue outcomes.
Lenny Rachitsky: Maybe just one more question before we get to a very exciting lightning round. It’s going to be a combo question. I hear you have a hot take on sales comp, how to comp salespeople that’s different from other people and also who to hire when you’re hiring folks in sales. Can you just talk about your takes there?
Jeanne DeWitt Grosser: I struggle with sales comp because it’s all about pay for performance, which I’m obviously a fan of, but it makes your organization less flexible because you basically have to decide 12 months in advance, these are things I value and particularly in this moment that could be different. As a great example of this, when we wrote the sales plans for this year at Vercel, the AI cloud did not exist. We were selling our front-end cloud and we were selling VZero and introduced the AI cloud halfway through the year. Now we had all sorts of good ways to still incentivize that, but I think you want to be able to be innovative and pivot and when you have a well-designed sales plan or a very structured sales plan, that can be challenging.
So that’s a little bit of my hot take is just I’m trying to figure out how do you have the upside of sales of motivates people. It’s a quantitative function, which is great, but also the flexibility to change your mind because I think a lot of companies right now are having a hard time doing annual planning. So that’s one. On profiles, I have always valued just sort of a diversified portfolio. So I strongly believe that sales is a skill and so you want salespeople with actual sales experience in your organization, but I think there’s value in pairing them with more nontraditional backgrounds, in particular consulting or banking background. Those folks are really good at more quantitative and analytical aspects of sales. So getting into that consultative part, which I think we talked about at the outset. And so I find that when you mix these together, the sort of consultant banker profile realizes, “Oh wait a minute, sales is a skill and I didn’t really have it.” And so they go learn from your account executives with that background and then your AEs learn more about, okay, how do I think about a P&L? How can I talk to a CFO? How do I present a TCO analysis more effectively? And so just creates a much richer learning environment where people are bouncing ideas off each other.
Lenny Rachitsky: That is awesome. I love that strategy. Okay, final question. Just is there anything else you wanted to share? Anything else you want to leave listeners with before we get to our very exciting lightning round?
Jeanne DeWitt Grosser: Oh man. I feel like we’ve been very thorough.
Lenny Rachitsky: All right, thanks So too.
Jeanne DeWitt Grosser: Yeah, you stumped me on that one.
Lenny Rachitsky: Okay. That’s the goal. With that Jean, we’ve reached our very exciting lightning round. I’m going to make it very quick. I know you got to run. I’m going to ask you just two questions.
Jeanne DeWitt Grosser: Okay.
Lenny Rachitsky: One is I’m going to skip to your life motto. Do you have a favorite life motto that you often come back to find useful in worker and life?
Jeanne DeWitt Grosser: I do. I actually have found that I’m known for saying a handful of things that I didn’t necessarily realize it, but when you leave an organization, people tend to tell you what stuck with them. But there is one that I think I am known for saying growing up, my mom always said to me, when the going gets tough, the tough get going. And in sales, you’re always going to have a quarter when you’re not on pace. And so that’s one that I feel like I pull on, not infrequently because in my view, there’s another version of this, my mom also always says was where there’s a will, there’s a way. So I think you can always choose to find a path forward even when that’s not super clear.
Lenny Rachitsky: I love these. Okay, last question. I read that you were a very competitive diver in college early on. I’m just curious if there’s something you learned from that experience that brought with you that helps you be as successful as you’ve become?
Jeanne DeWitt Grosser: Well, I mean, first of all, I should say I was generally coming in third place out of three on my team.
Lenny Rachitsky: Third place, that’s not bad.
Jeanne DeWitt Grosser: I managed to do it in college, but that was the extent of that career. So diving is a precision sport and it is a repetitive sport. And it is also a sport where when you land flat on your back, and literally as you are swimming to the side of the pool, welts are forming on it, you always 100% of the time will be forced to immediately get back on the diving board and do that exact same dive again. And so I think that has a lot of stuff that’s transferable to work and to sales. So for me, I just have an obsession with excellence and within sales. sales is about replicability. How do you drive predictable outcomes, how excellent are you at your ability to forecast? And so I think I bring that to bear within sales a lot. And then similarly, you get a lot of nos in sales. So another phrase that a sales guru said to me once or in a training was yeses are great, nos are great, maybes will kill you. And so how do you get really comfortable that no is a great thing and that just gave you data and now you can go do something with it.
Lenny Rachitsky: This is a really inspiring and empowering way to end the conversation. Jean, thank you so much for being here.
Jeanne DeWitt Grosser: Thanks so much for having me, Lenny. It was a lot of fun.
Lenny Rachitsky: Bye, everyone.
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.
Glossary
| English | 中文 |
|---|---|
| Abhi | Abhi（人名，Vercel 数据科学负责人） |
| account executive | 客户经理 |
| AEO (Answer Engine Optimization) | AEO（答案引擎优化） |
| agent | 智能体 |
| AI gateway | AI gateway（AI 网关） |
| alpha | alpha（超额收益/竞争优势，保留原文） |
| AMAs | AMA（问答活动，Ask Me Anything） |
| April Dunford | April Dunford（人名，保留原文） |
| ARR (Annual Recurring Revenue) | ARR（年度经常性收入） |
| Ben Salzman | Ben Salzman（人名，Stripe 前员工，后创办 AI 原生 GTM 公司） |
| blueprints | 蓝图 |
| Claire Hughes Johnson | Claire Hughes Johnson（人名，保留原文） |
| closer | closer（成单者） |
| consumption-based | 基于消费的（业务模式） |
| core web Vitals | Core Web Vitals（核心网页指标，保留原文） |
| CrUX | CrUX（Chrome 用户体验报告，Google 发布的网站性能数据集） |
| customer success | 客户成功 |
| deal-bott | deal-bott（智能体名称，保留原文） |
| discovery | discovery（需求探索） |
| dogfooding / eat its own dog food | 吃自己的狗粮（使用自家产品） |
| economic buyer | 经济买家 |
| ERR (Experimental Run Rate Revenue) | ERR（实验性运行费率收入） |
| fluid compute | fluid compute（弹性计算） |
| forward-deployed engineering | 前线部署工程 |
| founder-led sales | 创始人主导销售 |
| freemium | 免费增值 |
| Global 2000 | Global 2000（全球2000大企业榜单） |
| go-to-market engineer | 市场进入策略工程师 |
| Gong | Gong（销售智能平台，保留原文） |
| GTM (Go-To-Market) | 市场进入策略 |
| ICP (Ideal Customer Profile) | ICP（理想客户画像） |
| inbound | inbound（入站） |
| install-based sales | 基于已安装产品的销售 |
| Jenna Abel | Jenna Abel（人名，保留原文） |
| Kate Jensen | Kate Jensen（人名，保留原文） |
| KPI (Key Performance Indicator) | KPI（关键绩效指标） |
| KYC (Know Your Customer) | KYC（了解你的客户） |
| lead agent | 线索智能体 |
| lost bot | lost bot（智能体名称，保留原文） |
| lost opportunity review | 失利机会复盘 |
| LTV (Lifetime Value) | LTV（客户生命周期价值） |
| marketplace | marketplace（交易市场） |
| MCP servers | MCP 服务器 |
| mid-market | 中端市场 |
| objection handling | 异议处理 |
| outbound | outbound（外站/外呼） |
| outbound prospecting | 外呼拓客 |
| P&L (Profit and Loss) | 损益表 |
| penetration | 渗透率 |
| pipeline | pipeline（销售管道/商机管线） |
| platform architect | 平台架构师 |
| PLG (Product-Led Growth) | PLG（产品驱动增长） |
| professional services | 专业服务 |
| revenue operations | revenue operations（营收运营） |
| sales engineer | 销售工程师 |
| SDR (Sales Development Representative) | SDR（销售拓展代表） |
| segmentation | 客户细分 |
| self-serve | 自助服务 |
| Sierra | Sierra（AI 客户服务公司，保留原文） |
| skew | 档位/版本（产品定价中的不同层级） |
| SMB (Small and Medium Business) | SMB（中小企业） |
| TCO (Total Cost of Ownership) | TCO（总拥有成本） |
| technical buyer | 技术买家 |
| UXR (User Experience Research) | UXR（用户体验研究） |
| Velocity sales team | Velocity 销售团队（高效、标准化的面向中小客户的销售团队） |
| vibe coding | vibe coding（氛围编程，保留原文） |
| well-architected guides | 良好架构指南 |
| workload type | 工作负载类型 |
Reformatted by reformat_english.py