The sales process involves sales functions converting leads or opportunities to customers and selling more products to existing customers. The optimization of the sales process thus is key to success of any B2B business. In this work, we introduce a principled approach to sales optimization and business AI, namely the Causal Predictive Optimization and Generation, which includes three layers: 1) prediction layer with causal ML 2) optimization layer with constraint optimization and contextual bandit 3) serving layer with Generative AI and feedback-loop for system enhancement. We detail the implementation and deployment of the system in LinkedIn, showcasing significant wins over legacy systems and sharing learning and insight broadly applicable to this field.

Artificial intelligence promises to change the way we work and sell both online and offline. But so far, most sales organizations fall short when it comes to the use of AI. When organizations experiment with various sales enablement technologies, they typically don't see a strong return on their investment because, thus far, the tools lack the kind of predictive modeling that actually moves the needle on win rates, deal size, time to close, and revenue. Most companies simply haven't invested in the right data modeling to use AI engines to link sales actions to better outcomes. Across the business-to-business sector of e-commerce, it is much easier today than a few years ago for buyers to shop online for products and complete purchases with little or no involvement from sellers until much later in the process.

AI and Predictive Technologies can make it happen! Star Sales Reps (SSR) are hard to find, hard to develop and hard to retain. Sales leaders have struggled with this issue since the day selling was invented. Why do SSRs perform so much better? Can the Average Sales Rep (ASR) emulate the SSR to deliver high performance?