Converting inbound leads into customers at OpenAI

这是我们关于 OpenAI 如何在自身技术上构建解决方案系列的一部分。

当 ChatGPT Enterprise 和 Business 相继推出时，入站需求激增。成千上万的公司——从早期创业公司到跨国企业——每月都有公司联系我们。需求非常可观，系统承受了真实的压力。

把这些潜在客户按表单和静态流程来分流已无法应对。太多潜在客户只收到自动回复，叫他们在线注册；太少人的问题得到解答。结果是错失了机会，购买体验也无法与客户对我们的信任相匹配。

挑战不仅是规模，还有质量。买家想要具体的答案：

• 该产品在医疗环境中合规吗？
• 我们如何比较不同方案并选择合适的一个？
• 我们行业内的同行看到了哪些结果？

“我们每月收到成千上万条线索，但只有能力与其中一小部分沟通。有些线索只需要回答几条问题就能极大提升购买体验，但我们无法提供那种个性化体验，”负责市场推广创新的 Harsha Chilakamarri 说。

传统的自动化无法带来那种细腻度。按线性方式大量雇人也不可持续。我们需要一种不同的方法。

构建入站销售助理

我们创建了一个由 AI 驱动的入站销售助理，目标不是替代销售代表，而是扩展他们的覆盖能力——并通过销售人员的反馈进行训练和改进。

其核心是我们的内部连接器。产品文档、政策库、客户案例和作战手册被拉入模型可推理的上下文中。助理不会胡乱猜测。它以准确的信息、以潜在客户的语言，直接回应他们的问题。

这意味着潜在客户能在几分钟内得到个性化回复，用他们的语言表达，且基于他们的实际问题。

• 东京的一家公司收到的是日文回复，而不是英文模板信件。
• 一家医院系统在首次交流中就能得到合规细节，而不是等上好几天。
• 如果潜在客户符合企业标准，线程会无缝移交给销售代表，且上下文保持完整。

“这个模型让我们能够与每一位客户进行高度个性化的互动，”Chilakamarri 说。

这并不是为自动化而自动化，而是能立刻带来价值的自动化。

与销售代表共同构建，为销售代表而建

突破不仅来自助理的第一封回复，还来自其背后的反馈循环。

在训练模型时，每一份回复草稿都会回到销售代表那里由他们进行修正。每一次修正都成为训练数据。准确率在几周内从 60% 提升到超过 98%。助理不再使用千篇一律的模板，而开始听起来像我们团队的最佳化身，把判断力编码化并实现规模化可用。

“我们只靠我和另一位工程师就建立了一个非常复杂的评估系统……一旦我们能以自动化方式做这些评估，我们就能迅速从 60% 的准确率提升到 90%，现在首次邮件回复的准确率已达 98%。”——市场推广创新，Harsha Chilakamarri

对销售代表而言，变化立竿见影。收件箱不再被不合格的线索淹没。他们接手的是已经在推进中的对话，潜在客户有真实的购买意向并且问题已被回答。

这些评估也让领导层更有信心。它们展示了可量化的进展，而不仅是零星的案例，证明助理可以负责任地扩展。

从错失的线索到高速增长

影响是立竿见影的。一家曾经被队列淹没的小公司提交问题后，在几小时内收到了详尽回复，几天后就签署了企业合同。这类故事不断重演。

曾经的死胡同变成了我们最强劲的增长渠道之一。数月内，解锁了数百万的年经常性收入。

“我们最大的顿悟时刻是首次推出助理时。我们意识到，如果给入站线索提供个性化体验并迅速回答关键问题——即便只是通过邮件——许多人会非常迅速地产生购买意愿。”——市场推广创新，Harsha Chilakamarri

对于被移交合格线索的销售代表来说，变化同样宝贵。他们不再需要在泛泛的线索中挖掘，而是看到带有明确意图的活跃对话。首次出现了没有人被落下的感觉。

参与互动的新标准

这不仅仅关乎入站线索。它也指向更广泛的机会：入职、续约和支持都能从值得信赖的个性化对话中受益。

教训很简单：当你通过 AI 将最优秀销售代表的优秀实践进行规模化时，会改变整个团队的可能性。

正如 Chilakamarri 所说：“领导层对此极为兴奋。这证明我们可以用 OpenAI 构建 OpenAI，并将我们的技术直接展示给客户。”

为每一条线索提供个性化服务不是一种战术，而是为所有互动带来更好方式的一部分。

准备将 ChatGPT 引入您的企业了吗？

与我们的团队联系：https://openai.com/contact-sales/

This is part of our series about how OpenAI is building its own solutions on our technology.

When ChatGPT Enterprise and Business each launched, inbound demand surged. Tens of thousands of companies—from early-stage startups to multinational enterprises—were reaching out every month. The demand was remarkable. The strain on our systems was real.

Routing those leads through forms and static workflows couldn’t meet the moment. Too many prospects got an automated reply telling them to sign up online. Too few had their questions answered. The result was missed opportunities and a buying experience that didn’t match the trust customers were placing in us.

The challenge wasn’t just scale. It was quality. Buyers wanted specific answers:

• Is this product compliant in a healthcare environment?
• How do we compare plans and choose the right one?
• What results are peers in our industry seeing?

“We were getting thousands of leads a month and only had capacity to talk to a small fraction. Some leads needed a couple of questions answered to really make a great buying experience, but we weren’t able to provide that personalized experience,” says Harsha Chilakamarri, Go-to-Market Innovation.

Traditional automation couldn’t carry that nuance. Hiring linearly wasn’t sustainable. We needed a different approach.

Building the inbound sales assistant

We created an AI-powered inbound sales assistant designed not to replace reps, but to extend their reach—trained and refined with rep feedback.

At its core are our internal connectors. Product documentation, policy libraries, customer stories, and playbooks are pulled into context the model can reason over. The assistant doesn’t guess. It responds with accuracy, in the prospect’s language, directly tied to their question.

That means prospects get a personalized response within minutes, written in their own language, grounded in their actual question.

• A company in Tokyo receives an answer in Japanese, not an English form letter.
• A hospital system asking about compliance gets the details in their first exchange, not after days of waiting.
• If the prospect is enterprise-qualified, the thread is seamlessly handed off to a rep, with context intact.

“This model allows us to engage with and provide every customer a hyper personalized experience,” says Chilakamarri.

This isn’t automation for its own sake. It’s automation that delivers value, right away.

Built with reps, for reps

The breakthrough wasn’t just the assistant’s first reply. It was the loop behind it.

When training the model, every draft response went back to sales reps for corrections. Every correction became training data. Accuracy climbed from 60 percent to more than 98 percent within weeks. Instead of generic templates, the assistant started to sound like the best version of our team, codifying judgment and making it available at scale.

“We built a very complex eval system with just me and one other engineer… Once we had a way to do those evals, especially in an automated fashion, we were able to quickly go from 60% accuracy to 90%, and now 98% on first emails.”

For reps, the shift was immediate. Inboxes weren’t clogged with unqualified leads. They opened conversations already in motion, with prospects who had real intent and real questions answered.

The evals also gave leadership confidence. They showed measurable progress, not just anecdotes. They proved the assistant could be scaled responsibly.

From missed leads to high growth 

The impact was immediate. A small company once lost in the queue submitted questions, got thoughtful answers within hours, and signed an enterprise contract days later. Those stories repeated again and again.

What had been a dead end became one of our strongest growth channels. Within months, multimillions in annual recurring revenue were unlocked.

“Our biggest aha moment was when we first launched the assistant. We realized that if we give inbound leads personalized experiences and quickly answer key questions—even over email—many are eager to buy really quickly.”

For reps getting passed qualified leads, the shift was just as valuable. Instead of digging through generic leads, they saw active conversations with clear intent. For the first time, no one felt left behind.

A new standard for engagement

This isn’t only about inbound leads. It points to a broader opportunity: onboarding, renewals, and support can all benefit from trusted, personalized conversations.

The lesson is simple: when you scale the excellence of your best reps through AI, you change what’s possible for the entire team.

As Chilakamarri put it: “Leadership could not be more excited by this. It’s proof that we can build OpenAI on OpenAI and showcase our technology directly to customers.”

Personalizing every lead isn’t a tactic. It’s becoming a better way for all engagement.

Ready to put ChatGPT to work in your business?

Generated by RSStT. The copyright belongs to the original author.

Source