In a product-led organization, data truly is the new sales currency. Join our expert panel as we delve into the transformative power of product usage data. We'll explore how leading companies are harnessing this wealth of information to identify high-potential leads, deeply understand customer behavior, and craft hyper-personalized sales interactions. Our panelists will discuss the cutting-edge tools and techniques that empower sales teams to become strategic advisors, leveraging product insights to drive customer success and accelerate revenue growth. Expect a dynamic conversation covering: - Identifying and prioritizing key product usage metrics. - Integrating product data with existing sales and marketing systems. - Empowering sales teams with actionable product insights. - Building a culture of data-driven decision-making in sales. - Real-world examples of successful product data implementation. This session is designed to provide practical takeaways and spark insightful discussions on how to transform your sales approach from reactive to proactive, powered by the intelligence of your product. Bring your questions and be ready to engage with the future of data-driven sales in a PLG world

As machine learning (ML) technologies become increasingly integrated into products, managing these offerings presents unique challenges and opportunities that set them apart from traditional product management. The dynamic nature of ML models, the necessity for continuous training, and the complexities of data management all contribute to a distinctive approach to product management. In this keynote, we will explore: - Examine key differences in managing ML products. - Emphasize iterative development's importance in ML product management. - Highlight the need for cross-functional collaboration between data scientists and product teams. - Address challenges in ensuring model performance. - Consider ethical considerations in ML products. - Gain insights into best practices for effectively managing ML products. - Focus on understanding user needs for ML products. - Optimize model feedback loops. - Drive alignment across stakeholders. - Provide product leaders with tools to navigate ML complexities. - Enable delivery of successful, impactful ML products.