For years, VOC has meant surveys, NPS scores, and advisory boards. Useful, but always late. By the time a quarterly report hits the table, the customer has already made their decision. Customers are speaking all the time in signals. The drop in logins, the rise of ticket escalations, the sudden silence of an executive sponsor. The frustration in a chat, the tone of a review, the way transactions slow before they stop. In a B2B2C world, those signals aren’t just coming from end-customers, they’re coming from partners and merchants too. This talk is about moving VOC from a rearview mirror into a radar. I’ll show how GTM leaders can connect scattered signals with AI to anticipate churn, uncover hidden blockers, and even predict when customers are about to switch. To make it real, Dhvani will share signal stories GTM leaders will instantly recognize: how SaaS signals churn long before renewal, how payments friction signals merchant attrition, and how consumer sentiment spirals before it goes viral. She’ll also share a simple model - think “ChatGPT for VOC.” A framework any team can map onto their world: - Inputs: conversations, usage data, transactions, ecosystem feedback - AI signal layer: sentiment analysis, anomaly detection, predictive models - Outputs: dashboards, risk maps, sentiment clouds, and plain-language briefs It’s not about theory. It’s about giving GTM leaders a way to anticipate, not just react, and a model they can take back Monday morning.