HeyJaredThe Experimentation Edge
How do product teams decide what to build and what not to? Each episode explores how you can use experimentation, A/B testing, and evidence-based decision-making to ship better features, reduce risk, and drive measurable business impact.
- Indexed episodes, last 90 days
- 16
- Latest publication
- Sep 10, 2026
- Audience
- Checking…
- Earliest in this view
- Jul 7, 2026
Latest episodes
Realtor.com on using your AI as a junior data scientist (opens the original)
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Summary What do you do when your biggest experiment win turns out to be a loss? Whitney Perez, Director of Product Management at Realtor.com, joins host Ashley Stirrup to share the checkout bundling test that posted a 300% attach rate and still lost revenue, the 30/30/30 rule she uses to set expectations for a new experimentation team, and how AI is turning an English major into an aspirational data scientist. This episode is for product managers, engineers, and data scientists building experime
How Zalando connects every experiment to its North Star (opens the original)
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Summary How 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
Learneo on testing the opposite of every hypothesis (opens the original)
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Summary Rich Liebling, senior director of engineering at Learneo, joins host Ashley Stirrup to explain the practice that came out of growing Shop It To Me from 50,000 subscribers to one million in nine months: test your hypothesis, and test its opposite. Rich covers the page where cutting text lost and adding text won, why the inverse wins more often than teams expect, and why small focused tests are the only ones where "the opposite" means anything. He also walks through translating a million s
The four questions Early Warning asks before any A/B test (opens the original)
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Summary What separates a valid A/B test from an expensive guess? Priya Singhee, VP of Enterprise Analytics & Data Science at Early Warning — the bank-owned consortium that fights payment fraud and operates Zelle, which processed a trillion dollars last year — joins host Ashley Stirrup to share the experimentation playbook she built leading storefront analytics at Wayfair. She walks through the four questions to ask before launching any A/B test, why 85 to 90% of tests are supposed to fail, how p
How Supercell A/B tests 300 million players without breaking trust (opens the original)
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Summary On this episode of The Experimentation Edge, Ashley Stirrup talks with Shan Huang, data scientist on the central experimentation team at Supercell, the Helsinki mobile game company behind Clash of Clans, Clash Royale, Brawl Stars, Hay Day, and Boom Beach. Shan explains how a famously decentralized, creative-first company with 300 million monthly active users runs fewer than 100 A/B tests a quarter and why that restraint is deliberate, how Supercell announces experiments to players in adv
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