{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The Experimentation Edge","title":"How Zalando connects every experiment to its North Star","html":"","width":"100%","height":180,"duration":1237,"description":"Summary\nHow do you keep 1,000 experiments a year pointed at one North Star? In this episode of The Experimentation Edge, host Ashley Stirrup talks with Mi Tian, Head of Applied Science at Zalando, about running experimentation inside a central economics org that reports to the CFO. Mi shares how Zalando balances safe confirmatory tests with game-changing bets, how the team measured the discovery feeds homepage launch when success had no established metric, and how a KPI tree cascades the company North Star down to the controllable inputs teams ship every day. She also looks ahead to LLM-based agents as a simulation layer for screening hypotheses. A practical conversation for anyone building an experimentation program that wants both rigor and ambition.\n\nChapters\n00:45 About Zalando and its marketplace model\n02:00 Mi's path from engineering to experimentation\n03:10 Economists and data scientists in one decision-making org\n04:15 Running over 1,000 experiments a year\n05:55 What makes an experiment high risk\n07:10 Sharing learnings through standardization and champions\n09:10 The discovery feeds launch and its measurement plan\n11:15 Balancing the experimentation portfolio\n12:35 Growing a KPI tree from the North Star\n16:05 LLM agents and the future of experimentation at Zalando\n18:35 New missions for a longstanding business\n\nTakeaways\n- Treat experimentation as a portfolio: balance confirmatory tests that protect the business with game-changing bets that can win big.\n- Assess risk tiers when building the roadmap so measurement rigor scales with the stakes instead of slowing every decision down.\n- When a launch is too new to have a success metric, pair short term A/B tests with long term holdouts from day one.\n- Connect every experiment to the company North Star by cascading it down to sensitive proxy metrics and controllable inputs.\n- Use LLM based agents as a cheap simulation layer to screen hypotheses, not as a replacement for real A/B tests.\n\nConnect with the Guest...","thumbnail_url":"https://img.transistorcdn.com/D9kLs0HSsqR4ttk_5ESEdC1jX-wmD76GK-OHmb3a9B8/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS80YTFk/MGU1MjJlODhlNjJh/MTdlZTZkN2Q1ODY5/OTdjYy5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}