{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The Experimentation Edge","title":"Dilligent explains why moving on from an experiment might cost you","html":"","width":"100%","height":180,"duration":1311,"description":"SummaryDan Layfield, Director of Product Management at Diligent, joins host Ashley Stirrup on The Experimentation Edge to trace what fifteen years of A/B testing across Codecademy, Uber Eats, and the Fortune 1000 boardroom actually taught him. He breaks down the Codecademy trial-model rebuild that took four months and several rounds to deliver a 35% conversion lift, why moving on from a losing experiment too early is one of a PM's costliest mistakes, how to escape the B2B feature factory with metrics that genuinely ladder up, why retention should ride a product's natural use case instead of fighting it, and where AI is already replacing weeks of research and analysis. It's a practitioner's guide for product managers, growth leaders, data scientists, and engineers bringing experimentation rigor to both B2C and B2B.\n\nChapters00:45 Meet Dan Layfield and Diligent01:45 Two worlds of experimentation, Codecademy and Uber03:45 The trial model that lifted conversion 35%06:20 What to do with a losing experiment08:50 Two flavors of experimentation09:45 Reading forty metrics at Uber Eats13:10 Escaping the B2B feature factory16:45 Anchoring the North Star to real usage19:15 Where AI fits in research and analysis\n\nTakeawaysA losing experiment is often inconclusive, not negative; treat it as a map of the funnel rather than a verdict, and know when a big problem is worth another round.Persistence paid off at Codecademy: four months and three to four rounds of trial-model testing produced a 35% conversion increase.Separate your two experimentation modes; high-volume CRO chases many small wins, while big, uncertain bets are worth taking multiple shots to de-risk.Most B2B product teams are feature factories; the fix is a top-down OKR system, and planning usually breaks in the connections between layers, not inside them.Anchor retention and engagement to the product's natural use case, and use AI to synthesize research and simple A/B analysis in hours instead of weeks.\n\nConnect with...","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}