收入团队的人工智能团队成员
根据我的经验，优秀和劣质的收入团队之间的差距一直很大，而那些做对了的团队则拥有巨大的优势。这往往取决于他们与客户保持联系的频率和一致性。及时跟进、在问题升级之前发现问题，或者确保没有机会被遗漏。然而，大多数团队没有时间或纪律去做到这一点。
这就是我开始构建 https://chuff.co 的原因。它允许您用自然语言创建代理，帮助进行外联、跟进、支持和账户管理。
这些代理可以使用电子邮件、API 和网络搜索等工具查找信息或采取行动。它们还有一个小型内部数据库，用于跟踪人员和公司，这与您已经使用的工具并行。
与创始人和收入团队交流时，我看到了一些有趣的用例，例如：一个代理在 Shopify 中检查订单状态，并在客户发送电子邮件时草拟更新；另一个代理跟踪 Stripe 中的发票状态，并跟进逾期付款；或者同步收件箱，并请求代理建议重新接触的对象并撰写跟进邮件。
这仍然是一个早期阶段。我很想听听其他在这个问题上工作或考虑将 AI 应用于面向客户工作流程的人的看法。
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In my experience the difference between good and bad revenue teams has always been huge and a massive advantage for those that get it right. It often comes down to how consistently they stay in touch with customers. Following up at the right time, surfacing issues before they escalate, or just making sure no opportunity slips through. But most teams don’t have the time or discipline to do this.<p>That’s what led me to start building https://chuff.co. It lets you create agents in natural language that help with outreach, follow-ups, support, and account management.<p>Agents can use tools like email, APIs, and web search to look things up or take action. They also have a small internal database to keep track of people and companies, which sits alongside whatever tools you already use.<p>Speaking to founders and revenue teams, I’ve seen some interesting use cases, for example: an agent that checks order status in Shopify and drafts updates when a customer emails in, another that tracks invoice status in Stripe and follows up on overdue payments or syncing an inbox and asking an agent to suggest who to re-engage and write follow-ups.<p>Still early. Would be curious to hear from others working on this problem or thinking about AI in customer-facing workflows.