The Experimentation Edge

Twitch on why false negatives kill product ideas

Summary How do you make a high-stakes product decision when the safe choice is to never test it at all? In this episode of The Experimentation Edge, host Ashley Stirrup talks with Arun Bodapati, director of data science at Twitch, about the discipline behind trustworthy experimentation. Drawing on his experience at Schwab, Uber, and Twitch, Arun explains why false negatives are the most dangerous result a team can produce, what hygiene to nail before you push play, and how Twitch used geo-fenced experiments and causal inference to finally settle a pricing question it had avoided for years. It's a practical conversation for product managers, engineers, data scientists, and growth leaders who want experiments that hold up and earn executive trust. Chapters 00:00 Welcome and introduction 01:15 Arun's background and marketing experimentation at Schwab 04:15 Uber's mature, experiment-driven culture 06:30 Coming to Twitch: from Python notebooks to a shared standard 08:30 The pricing problem Twitch had long avoided 10:30 Geo-fenced experiments, matched markets, and elasticity 13:15 The gifted-subs surprise and testing promotions 16:15 The discipline that matters before you push play 18:15 Why false negatives are worse than false positives 20:05 Enrollment triggers and broad explore experiments 22:45 AI, the Kiro tool, and what's next for experimentation Takeaways False negatives are more dangerous than false positives — they get institutionalized as "we tried that, it didn't work" and quietly kill good ideas for years. The most valuable experiment work happens before you push play: clear enrollment logic, a plain-English hypothesis, and no optimizing ahead of the test. If an intervention sounds weak when you write it out in plain English, don't run the experiment — you're just wasting time. Run a broad explore experiment first; small, over-narrowed populations lack power and raise the odds of a false negative. Find the responsive segment with heterogeneous treatment effects afterward. Twitch used geo-fenced experiments with matched markets and causal inference to measure true price elasticity, turning a feared pricing decision into a measured, accretive one. Connect with the Guest LinkedIn: https://www.linkedin.com/in/abodapati/ Website: https://www.twitch.tv Sponsor Growthbook helps you ship features with confidence by bringing experimentation and feature flagging into one open-source platform. No more guessing whether that new checkout flow actually moved the needle, waiting weeks for data team bandwidth, or flying blind on rollouts. Growthbook gives you a single place to run A/B tests, manage feature flags, and analyze results against your existing data warehouse. With powerful stats built in, it takes the complexity out of experimentation, helps you catch regressions before they hit every user, and makes it easy to test ideas that keep your product improving and your metrics moving in the right direction. See a demo at https://www.growthbook.io/

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Squarespace killed its blank template and built something better

Summary What do you do when your big launch increases engagement and tanks conversion? On this episode of The Experimentation Edge, host Ashley Stirrup talks with Lina Blackman, Director of Product Analytics at Squarespace, about the blank template launch that flopped — and how its learnings became Blueprint, Squarespace's AI-guided website builder. Lina explains how her embedded analyst team runs 150–200 experiments a year for 3 million customers, the two questions she asks every time a test loses, why teams only need one or two big wins a quarter, how Squarespace calibrates statistical certainty to business stakes, and where AI belongs (and doesn't) in the A/B testing workflow. For product managers, data scientists, and experimentation leaders who want to extract more learning from every test. Chapters 00:00 Introduction: Lina Blackman, Director of Product Analytics at Squarespace 01:45 Squarespace's business and 3 million website customers 02:30 Decentralized analysts, centralized experimentation program 04:15 150–200 experiments a year: onboarding, mobile, checkout, pricing 04:55 The blank template disaster that became Blueprint AI 07:45 Two questions for every losing test 09:30 Moving ship-first teams up the experimentation maturity curve 12:30 A/B test logs and insights rituals 13:30 North Star metrics and the KPI tree 16:35 AI in the A/B testing workflow — and what stays manual. Takeaways Stated preference lies: users asked for a blank canvas, but behavior demanded guided design — and only the experiment could referee. Close every losing test with two questions: did it work for a granular segment, and is the idea worth further investment? One or two big wins a quarter is a healthy hit rate when you run 150–200 experiments a year. Calibrate certainty to stakes — tight bounds on revenue and pricing tests, wider bounds on engagement tests so teams don't spin on noise. Hand AI the mundane parts of the workflow (tracking, assignment setup), but if AI runs the brief and the analysis, ask why you're running the test at all. Connect with the Guest LinkedIn: https://www.linkedin.com/in/linanguyen Website: https://www.squarespace.com Sponsor Growthbook helps you ship features with confidence by bringing experimentation and feature flagging into one open-source platform. No more guessing whether that new checkout flow actually moved the needle, waiting weeks for data team bandwidth, or flying blind on rollouts. Growthbook gives you a single place to run A/B tests, manage feature flags, and analyze results against your existing data warehouse. With powerful stats built in, it takes the complexity out of experimentation, helps you catch regressions before they hit every user, and makes it easy to test ideas that keep your product improving and your metrics moving in the right direction. See a demo at https://www.growthbook.io/