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Episode 4: How to Build an Experimentation Machine and Where Most Go Wrong
Nov 7, 2024
Ramesh Johari, a Professor at Stanford University, dives into the evolution of online experimentation, especially for tech companies and marketplaces. He discusses how organizations can shift to self-learning models and the common pitfalls they encounter, such as risk aversion. The conversation touches on the transformative impact of generative AI on experimentation processes. Ramesh also shares strategies for cultivating a culture of learning from failure and integrating data scientists to enhance business value, all while moving beyond traditional A/B testing methods.
51:16
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Quick takeaways
- Organizations must evolve their experimentation practices from basic A-B testing to innovative, risk-embracing strategies that foster learning from all outcomes.
- Prioritizing data quality is essential for effective experimentation, as better data enables organizations to derive more significant insights and make informed decisions.
Deep dives
The Evolution of Online Experimentation
Organizations are evolving their experimentation practices to become faster, more adaptive, and self-learning. Initial experimentation often involves basic tests like A-B testing, where businesses simply try to determine which version of a product is better. However, as these organizations mature, they are shifting towards more innovative practices that encourage riskier testing and emphasize learning from both successes and failures. This evolution helps organizations not only identify winners but also understand the rationale behind outcomes, leading to more strategic decision-making.
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