{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Making Sense of Martech","title":"Beyond Just Do It with Nike's Linda Cereda","html":"","width":"100%","height":180,"duration":3262,"description":"\"At Nike, we learned the hard way — tech wasn't the constraint. The operating model was.\" – Linda\nMartech leaders love to talk about AI in marketing, next-best-action, personalization at scale, and the promise of a composable CDP, but most teams still struggle to connect data strategy to an operating model that can actually ship. In this episode, Jacqueline sits down with Linda Cereda, former Global VP of Marketing Data at Nike and the first GM of the SNKRS app, to unpack how one of the world's most iconic brands built a marketing data engine that didn't collapse under its own ambition.\n\nLinda shares her journey from digitizing scarcity-driven product drops on SNKRS to overseeing global next-best-action models and enterprise measurement systems. She breaks down the real work behind decisioning: defining the actions worth nudging, ranking them by LTV, aligning cross-functional teams around measurable business goals, and building model families that connect audience, timing, channel, and content — without turning the organization into a science fair.\n\nWe also explore why so many companies are \"stuck in 2017\" despite owning expensive tools, and why buying a vendor contract feels like progress but rarely is. From reducing forecasting error by 44% using zero-party data to rethinking seasonal planning in favor of contextual, real-time nudges, Linda makes one thing clear: modern AI tooling is useless if your workflows, governance, and measurement rhythm aren't built to support it.\n\nTimestamps\n01:02 — From Men in Black with Raybans to Nike \n08:00 — Why global brands struggle to update their \"operating system\" and the trap of tool-led progress \n16:32 — Building the SNKRS app: Solving scarcity, hype, and the #IAmUpset consumer crisis \n20:44 — Using machine learning and zero-party data to slash forecasting errors and improve fairness \n29:20 — The reality of Nike's $XXm Adobe deal and the risks of vendor lock-in \n40:02 — The future: Composable CDPs, AI decisioning engines,...","thumbnail_url":"https://img.transistorcdn.com/2xBD0217Lw_p8ycegYQlNgN3_3yOZ9LCNtHUu1dKlq8/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9lZjUz/OGYwZDVlYWU2MzVj/MTA0NjA3Mzc4Zjkw/MTQ0Ni5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}