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In this episode, host Michael Bernzweig talks with Tim Davidson, co‑founder of Clean Commit, about how to use experimentation to grow faster and smarter. Tim explains why “trying stuff” isn’t real testing, outlines a simple hypothesis‑driven framework, and shows how to prioritize ideas by potential impact and effort. He shares examples from e‑commerce brands, including price tests that lifted revenue and profitability, and talks about his own channel experiments in cold email and LinkedIn to highlight how data can guide where you focus next.
In this episode:
- The importance of treating business as a laboratory with thousands of A/B tests
- How industry giants like Amazon utilize rigorous experimentation
- A simple framework for hypothesis-driven testing: hypothesis, variables, measurement, and learning
- Prioritizing high-impact tests such as pricing, cart, and product pages
- Overcoming common hurdles: low traffic, data silos, and trial vs. testing
- Practical tips for small teams to implement a robust testing program
- The value of embracing failure as data for long-term growth
Timestamps:
00:00 - Introduction to Tim Davidson and his CRO philosophy
02:30 - How Amazon and large enterprises approach testing at scale
05:00 - Building a hypothesis-driven testing framework
07:15 - The key levers for quick wins: cart and product pages
09:45 - Overcoming low traffic challenges with smart prioritization
12:30 - Difference between trying and testing: ensuring rigor in experiments
15:00 - Practical steps for small teams to start testing in e-commerce
18:20 - The importance of documenting and iterating tests over time
20:50 - How to handle failures and extract value from experiments
23:10 - The role of competitor insights and research in testing ideas
26:00 - Applying testing principles across channels: price, messaging, and outreach
29:30 - Connecting data sources for better decision-making in HR and operations
33:15 - Building a culture of continuous experimentation
36:20 - Common mistakes and how to avoid them
39:10 - Final thoughts on moving from guesswork to data-backed growth
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