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In this talk, we'll explore split testing as a way to not only increase revenue and conversion through simple, surface-level changes, but also to dig deeper in order to help guide a product's roadmap by discovering which features customers really want and how much they're willing to pay. Help us caption & translate this video! http://amara.org/v/FGaj/
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In the talk titled **"Split Testing for Product Discovery"** at Rails Conf 2013, Bryan Woods explores the concept of split testing as a strategic method for enhancing web businesses. The discussion emphasizes that split testing is not just about superficial changes aimed at improving revenue and conversion rates; it serves as a tool for uncovering customer preferences and potentially guiding product development. Woods, despite lacking a formal business or economics background, presents some foundational economic concepts to illustrate his points. ### Key Points: - **Understanding Scale:** Traditional businesses face limitations in scaling, but web businesses can achieve higher profits with smaller margins due to the lack of physical constraints. The goal for developers, likened to a "black box," is to maximize revenue through effective testing and optimization. - **Speed vs. Data:** While rapid deployment of features is crucial in agile development, it is essential to back it up with data analysis and metrics. Woods warns against merely focusing on speed without assessing whether progress is meaningful. - **Basic A/B Testing:** The video outlines common A/B testing practices, which can lead to incremental gains. Bryan introduces tools like Visual Website Optimizer and Optimizely, which allow marketers to experiment with minor changes and measure their impact on user conversion rates. - **Going Deeper with Testing:** Beyond basic A/B tests, Woods encourages teams to identify customer needs and the value of potential features. He emphasizes the importance of testing fundamental assumptions about business models, service pricing, and user engagement strategies. - **Case Studies:** Woods shares experiences from his dating website, howaboutwe.com. He illustrates that receiving more messages can incite users to engage more, and introduces features like a "surprise me" button to spark creativity in date postings. - **Statistical Rigor:** He discusses the importance of using sound statistical methods to validate test results, suggesting that data analysis should guide decision-making rather than merely following best practices blindly. - **Tackling Customer Feedback:** The talk highlights how A/B testing can serve as a defense against conflicting user feedback, allowing organizations to rely on data rather than subjective opinions. ### Main Takeaways: - Rapid development is not fruitful without a direction backed by data. - Incremental improvements through consistent A/B testing can yield significant results over time. - Engaging deeply with user behavior and preferences can lead to better product discovery. - A/B testing should challenge and validate assumptions about the business model and user experience. Woods advocates for robust, data-driven experimentation as a pathway to not only optimize web applications but also to foster genuine understanding of what leads users to convert and engage productively.
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