BONUS: Unleashing Agile Experimentation, Accelerating Learning Cycles With 24h Experiments, With Vasco Duarte Read the full Show Notes and search through the world’s largest audio library on Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes. Merry Christmas, everyone! In today's Christmas week BONUS episode, we're diving into the world of Agile experimentation, specifically exploring how to enable 24-hour experiments—a topic that promises to revolutionize the way we approach product development. As we explore this exciting subject, remember that each episode this week is a companion to the Coach Your Product Owner e-course, accessible at bit.ly/coachyourpo. This week's episodes are designed to equip Scrum Masters with essential tools to assist their Product Owners in mastering five key areas. Today, we unravel the secrets of conducting rapid experiments that speed up your learning and development cycles. First: Defining Experiments and Their Purpose in Product Development Let's start by revisiting the Lean Startup methodology and its famed Build-Measure-Learn cycle. Lean Startup emphasizes that the purpose of "building" is not merely to deliver software but to validate hypotheses. Before running an experiment, it's crucial to define what you're trying to achieve, as discussed in Monday's episode on how to define value. Taking Amazon’s website as an example, suppose a team aims to boost the conversion rate for users who have added items to their cart. The hypothesis could be: "Removing shipping costs from the checkout overview will result in a higher conversion rate for people who've added an item to their cart." This hypothesis, or belief, becomes the trigger for experimentation. Second: Designing the Experiment Once the hypothesis is clear, it's time to design the experiment. Think of it as crafting a story, but with a focus on experimentation. Here's a template to structure your experiment: Title: Increase Conversion Rate for People Who've Added an Item to the Cart by Removing Shipping Costs from the Checkout Overview. We Do: Specify the actions to set up the experiment. We Expect: Clearly state the anticipated outcomes. Data We Collect: List the crucial data points needed for analysis. Conclusion: Outline the lessons learned and set a date for analysis. This structured template ensures clarity and sets the stage for a comprehensive understanding of the experiment. Third: Designing 24-Hour Experiments Before we start brainstorming stories and features that would need to be implemented by the development team, we should think about running experiments in 24 hours or less. We do this, because we want to avoid one of the major sources of waste in our processes: doing work that would not need to be done at all in the first place! As we explore the experiment above, we define the key question as, "Would a person not shown shipping costs be more likely to complete a purchase after adding an item to the cart?" To achieve faster learning, consider alternatives to software changes. In this episode, we explore some concrete alternatives to writing and deploying software when it comes to answer the key question defined above.  One example we explore is: conducting usability tests using paper prototypes with a small group. Through qualitative analysis and user feedback, you can gain confidence in your hypothesis without investing time in extensive software modifications. Running 24h Experiments: Summary In summary, today's episode tackled critical aspects of designing and running experiments, emphasizing the importance of learning. Key takeaways include: Lean Startup's Build-Measure-Learn cycle focuses on validating hypotheses. Experiment design involves specifying actions, expected outcomes, data collection, and drawing conclusions. The concept of 24-hour experiments emphasizes the need for rapid learning. All episodes t