OpenAI演示ChatGPT Work销售收入智能报告

At OpenAI, our revenue learning system is always on and always learning with ChatGPT work. One way we surface this intelligence is through a weekly revenue learning report built by ChatGPT work. Here's a demo example for Blossom Systems focused on the launch of their new product, Blossom Observability. The report ChatGPT work created for both web and mobile brings together signals from a wide array of sources, including seller activity, customer conversations, and signals from the market, then turns them into insight and action.

First, it gives me trends over time so I can track our performance. Then, it provides an executive summary, making sure I understand the key points of the report before I dig in further. From there, it jumps into the objections we're hearing across our strategic deals, broken down by category. This is followed by our competitors' presence in deals based on what's coming up in customer calls. As I scroll down, the report starts to surface opportunities for our team.

Things customers are calling out and areas where we may want to shift our operational resources. One area that we really focus on is how the field is performing. What are our best reps doing and how can we use those behaviors to up-level the rest of the team? Finally, these insights become recommendations and actions that we can use to refine our product strategy, strengthen seller enablement, and improve how we operate.

But, this isn't just a static report. I can converse with the intelligence behind it and dig deeper. Using our team's revenue intelligence plugin, I can ask questions like, "What are the most common objections and competitive threats in our strategic deals? How are our best performing reps handling them compared with our bottom performers? Should any of these objections change our priorities? I want examples." It works through the underlying data and gives me a detailed, well-cited set of answers along with recommendations for what we should prioritize as a business.

Now, let's turn those insights into action. I'll use ChatGPT work to draft a succinct set of proposed changes that I can share with my leadership team directly in Slack. It summarizes the key insights, recommends next steps, and suggests an owner for each area, turning the learning into coordinated action. Now those learnings are in the flow of work. The team can act, the results feed directly back into the system, and the next cycle begins with better evidence.

That's what makes this a revenue learning system. It's always on, always learning, and constantly improving how we sell and operate.