{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The Experimentation Edge","title":"What Samsung learned bringing B2C rigor to B2B","html":"","width":"100%","height":180,"duration":1814,"description":"Summary\nWhat changes when you take a mature B2C experimentation practice and apply it to a B2B storefront? Anuradha Tempe, Lead Product Manager at Samsung Electronics America, joins host Ashley Stirrup to share what she learned managing both sides of Samsung.com. She explains why B2B is not a sidekick to B2C, how a single bulk order can push a test into a false positive unless you normalize the data, and why not everything deserves an A/B test. She walks through the add-on experiment that nearly doubled attach sales once her team realized business buyers decide on mobile and purchase on desktop, and the Buy Now test that lost because B2B buyers value clarity over faster conversions. The conversation closes with her approach to North Star and guardrail metrics and the AI copilot she built to draft A/B test plans with a human still in the loop. A practical episode for product managers, engineers, data scientists, and growth leaders running experimentation across different customer types.\n\nChapters\n00:45 Meet Anuradha Tempe: from chip design to leading Samsung e-commerce\n02:15 Why B2B is not a sidekick to B2C\n03:30 Not everything needs an A/B test\n05:15 Spreading experimentation practice across a global conglomerate\n07:00 Bringing B2C rigor to a fast-paced B2B team\n08:45 The add-on experiment that nearly doubled attach sales\n11:15 Buyers decide on mobile and purchase on desktop\n16:30 The Buy Now test that lost\n21:30 North Star metrics, guardrails, and the EPP discount fix\n26:05 An AI copilot for A/B test planning\n\nTakeaways\n- Treat B2B as its own customer base with its own testing discipline; a single bulk order on one day can inflate a B2B test into a false positive unless order data is normalized before results are read.\n- Not everything needs an A/B test; route lower-risk changes through UAT feedback or pre/post comparisons and reserve full experiments for features where being wrong is expensive.\n- Map where the decision happens, not just where the purchase...","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}