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Do yo know these 9 common pitfalls that can sink your experimentation program? A thread on why A/B testing programs fail (and how to fix them) 🧵

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1/ Low Experiment Frequency Reality check: Your win rate is probably <30% Solution: Lower the cost per test and run MORE tests Pro tip: Automate everything you can (we can help)

2/ Biases & Myopia Even that "perfect" feature? Test it True story: Revisiting a paywall revealed it wasn't optimized at all, contrary to our beliefs Remember: We're not as smart as we think we are 🧠

3/ High Cost Problem: Most platforms charge by tracked users 📈 Solution: Consider open-source alternatives or build in-house ROI tip: Focus on cost per experiment, not total cost