『Product Mastery Now for Product Managers, Leaders, and Innovators』のカバーアート

Product Mastery Now for Product Managers, Leaders, and Innovators

Product Mastery Now for Product Managers, Leaders, and Innovators

著者： Chad McAllister PhD
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概要
 Welcome to Product Mastery Now, where you learn the 7 knowledge areas for product mastery. We teach product managers, leaders, and innovators the product management practices that elevate your influence and create products your customers love as you move toward product mastery. To see all seven areas go to https://productmasterynow.com. Hosted by Chad McAllister, PhD, product management professor and practitioner.Copyright © Product Mastery Now and The Everyday Innovator™ · All rights reserved. マネジメント マネジメント・リーダーシップ マーケティング マーケティング・セールス 出世 就職活動 経済学
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  • 617: Test your gut, don’t trust it –the scientific mindset for product teams – with Stefan Thomke, PhD

    617: Test your gut, don’t trust it –the scientific mindset for product teams – with Stefan Thomke, PhD
    2026/11/09
    Experiments to inform product decisions Watch on YouTube TLDR Stefan Thomke, professor at Harvard Business School, and author of The Scientific Mindset: How Thinking Scientifically Drives Better Business Decisions, shares five elements of a scientific mindset, four types of uncertainty in innovation, and three tests of a good hypothesis. Learn why good product decisions are rooted in testing more and assuming less. Introduction When Stefan Thomke studied almost 22,000 online experiments (mostly A/B tests) run by more than 1,300 companies, only about one in five changes made a measurable difference. Half of the changes made things worse. If experienced teams can’t predict the effect of a small change, how confident should any of us be about a creating a new product? Stefan is the William Barclay Harding Professor of Business Administration at Harvard Business School, where he has studied experimentation and innovation for more than three decades. He started as an engineer, learning experimental design to solve a production problem in a semiconductor plant. He is the author of Experimentation Matters and Experimentation Works. His new book, The Scientific Mindset: How Thinking Scientifically Drives Better Business Decisions, completes the trilogy. Stefan was on the show in 2020 to explain how to run product experiments. This time we look at the thinking that decides which experiments are worth running and what you do with the answers. You’ll learn how to find the assumptions hiding in a new product idea using four kinds of uncertainty, how to turn an assumption into a strong hypothesis, and why “customers want this feature” isn’t yet a hypothesis. Summary of Concepts Discussed for Product Managers Five Elements of a Scientific MindsetStefan opens with a question: What did you do the last time you didn’t know what to do? He quotes Richard Feynman: “The first principle is that you must not fool yourself, and you’re the easiest person to fool.” The human brain is a belief engine. We see connections between unrelated things and confuse correlation with causation. To overcome this, we need to think scientifically about decisions. Stefan identifies five elements of a scientific mindset that apply to business decisions: Be a knowledgeable skeptic.Articulate testable hypotheses.Produce hard evidence.Probe cause and effect.Create an experimentation culture, a shared scientific mindset. Skeptics vs DenialistsA skeptic is not a denialist. A skeptic is neutral. They look at the evidence with an open mind and want the truth. A denialist starts with a conclusion, picks evidence that supports it, and dismisses evidence that contradicts it. Stefan calls this running the scientific method in reverse. It’s easy to fall in love with a conclusion and only question the evidence that disagrees with us. Product managers do this when they run customer research to validate a solution instead of finding unmet needs. Instead, fall in love with the customer’s problem, not your solution. Test More and Assume LessThe greatest danger zone is when you are most confident in your assumptions and under pressure to act. That is the time to test your assumptions. Stefan explains that information is most valuable when it is most surprising. Effective leaders don’t trust their gut. They test their gut. Four Types of Uncertainty in InnovationStefan sees four types of uncertainty in innovation: R&D uncertainty: Does the product, service, technology, or process work as intended?Scale-up uncertainty: Can we produce it at high volume, high quality, and reasonable cost?Market or customer uncertainty: Are customers interested, will they pay, and how should we price it?Investment uncertainty: How much money and how many resources should we commit? Why Experience and Data Aren’t EnoughStefan explains that while experience and data are both valuable, they have limits. Relying on experience and data too heavily can prevent you from testing your assumptions. It is easy to mistake correlation for causality. Use experiments in addition to experience and data, because experiments can prove you wrong. Three Tests of a Good HypothesisStefan describes three tests for a hypothesis: It has measurable variables.It is simple.It can be proven wrong. Wolfgang Pauli would reply to a bad hypothesis with “This is not only not right. It’s not even wrong.” A hypothesis that cannot be falsified is worse than one that is simply incorrect. Stefan gives an example of a good hypothesis, which Kohl’s tested: “Opening our stores one hour later will have an insignificant impact on store revenue.” This is a measurable hypothesis they could test in a subset of stores. On the other hand, “We can take our brand upmarket,” is a bad hypothesis since it is not measurable. Designing Fast, Frugal ExperimentsExperiments should be: reversibleinexpensive, at least until they lead to large commitmentsat an appropriate level of rigor for how much ...
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    1分未満
  • 616: TRIZ for product managers, without years of training – with Jay Nakagawa and Bill Reid

    616: TRIZ for product managers, without years of training – with Jay Nakagawa and Bill Reid
    2026/11/02
    AI tools for accelerating TRIZ and JTBD Watch on YouTube TLDR Jay Nakagawa and Bill Reid recap their workshop from the 2026 PDMA Ignite Innovation Summit on combining Jobs-to-be-Done and TRIZ and using AI tools to accelerate their analyses. These tools help product managers eliminate contradictions between competing customer demands instead of settling for trade-offs. Introduction Your customers want it faster, better, and cheaper, all at once. Most product teams pick two and call it a trade-off. What if the trade-off is a contradiction you can solve, resulting in much greater value? I recorded this episode at the 2026 PDMA Ignite Innovation Summit, where that question was answered. Two returning guests join me. Jay Nakagawa is Co-Executive Director of the Society for Artificial Intelligence and Competitive Intelligence (SAICI) and is also the Director of Competitive Intelligence at Dell Technologies. Bill Reid is Vice President at Boulder Imaging and has spent more than 30 years developing products at companies including Ford, GE Aircraft Engines, and GM. He is named on more than 20 US patents. At the PDMA Ignite Innovation Summit, they led a workshop that combines Jobs-to-be-Done (JTBD), TRIZ (Theory of Inventive Problem Solving), and generative AI to turn hard constraints into product breakthroughs. Let’s learn a practical, 80/20 way to apply TRIZ principles and how generative AI can speed up inventive problem-solving so you don’t have to be a TRIZ expert. Summary of Concepts Discussed for Product Managers TRIZ: A Framework for Solving ContradictionsGenrich Altshuller developed TRIZ while working in a patent office. He believed anyone could be an inventor. He looked at patents to study how people solve problems and found patterns that held across fields. His tools include separation principles, technical contradictions, substance-field analysis with 76 standard solutions, and 12 trends of technical evolution. Bill describes TRIZ as a how-to book for solving known contradictions among constraints. Bill and Jay have combined TRIZ with Jobs-to-be-Done and competitive intelligence to identify and prioritize customer needs. AI Tools for Faster TRIZ AnalysisUsing AI tools can accelerate TRIZ analysis from weeks to minutes. At the PDMA conference, Jay and Bill led a workshop on using TRIZ and Jobs-to-be-Done with AI tools. Jobs-to-be-Done and TRIZThe workshop started with guiding product managers to understand their customers’ Jobs-to-be-Done statements. Bill used the example of a quarter-inch hole. The JTBD statements are to minimize the time to make the hole, minimize damage to the wood, and maximize accuracy. Improving speed leads to increased damage to the wood. That is a contradiction among customer needs. Workshop participants used a contradiction matrix from TRIZ to plot technical parameters corresponding to customer needs. Teams then used the contradiction matrix to brainstorm solutions. Know Fundamental Principles to Evaluate AI OutputAnalysis can be accelerated using AI tools, but as Bill and Jay explain, in order to know if the output of an AI tool is reasonable, you still need to understand the basic principles behind JTBD and TRIZ. It is still critical to have humans in the loop. Training AI on Jobs-to-be-Done and TRIZIn the workshop, Jay and Bill provided some files to train AI models to use JTBD and the 40×40 TRIZ matrix. Each participant loaded these files onto their own AI tools. The models were then ready to answer questions using these principles such as how a company can make its product more valuable or how difficult each contradiction would be to resolve. Participants described customer objectives, and the AI models used their training to give ideas on how to meet those objectives, using JTBD and TRIZ. Combining JTBD and TRIZ to Identify and Solve ContradictionsSolving one contradiction often reveals a new one, which is why a manual TRIZ analysis can take weeks or months. Jobs-to-be-Done identifies customer needs and prioritizes desired outcomes. The TRIZ framework points product managers to possible solutions by eliminating contradictions. Useful Links Watch Jay and Bill’s pre-conference webinar on TRIZ and JTBD, “From Contradictions to Breakthroughs”Download files on TRIZ and JTBD for training an AI model: TRIZ Contradictions MatrixKnow Your Customers’ “Jobs to Be Done”, Clayton ChristensenJobs to Be DoneLLM-based Extraction of Contradictions from Patents40 TRIZ Principles Connect with Jay and Bill on LinkedInLearn more about Jay’s Society for Artificial Intelligence and Competitive IntelligenceCheck out Jay’s book, The UnConventional Strategist603: Survive the AI revolution of product management – with Bob Cooper, PhD Innovation Quotes “And suddenly the inventor appeared.” – Genrich Altshuller, creator of TRIZ “Get rid of the box.” – Jay Nakagawa Application Questions What are some contradicting outcome statements for your product?What do you ...
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    36 分
  • 615: How to separate signal from noise in customer feedback – with Kristyn Corrigan

    615: How to separate signal from noise in customer feedback – with Kristyn Corrigan
    2026/10/26
    Make the most of AI tools for voice-of-the-customer research Watch on YouTube TLDR Kristyn Corrigan, Principal at Applied Marketing Science, joins me to discuss how product teams can move from drowning in customer feedback to identifying the core needs that drive product decisions. We explore shifting the Voice of the Customer (VOC) from a formal research method to an organizational mindset, the risks and opportunities of using AI in analyzing feedback, and actionable tools like the VOC focus brief to enhance cross-functional alignment and innovation. Introduction Your customers are telling you what they need every day, through usage data, support tickets, sales calls, online communities, and more. AI can summarize all of it in seconds — that’s no longer the hard part. Instead, it’s knowing which needs are worthwhile — what’s the signal to act on and what’s the noise to avoid. To help us answer that, Kristyn Corrigan joins us again. She is a Principal at Applied Marketing Science, where she leads the Insights for Innovation practice. She has more than 20 years of experience helping companies uncover customer needs and turn them into products, and she trains teams to run their own VOC programs. At the PDMA conference, she led a workshop on moving from a pile of customer feedback to the few needs that should drive product decisions. She’s here so we can learn to do that too. Summary of Concepts Discussed for Product Managers Reframing Voice of the Customer (VOC)Kristyn explains that VOC has shifted from being just a set of research interviews to a comprehensive mindset that leverages the wealth of customer insights found throughout an organization. This includes feedback from CRM data, recorded sales conversations, social media, forums, and support tickets. Product teams need to intentionally curate these sources to ensure the insights inform key product decisions. Kristyn has had success helping salespeople learn to ask voice-of-the-customer questions to help sales interviews provide more valuable insights. Separating the Signal from the Noise in Customer ResearchWith the explosion of data from internal and external sources, product teams risk getting lost in noise. Kristyn discusses the importance of aligning the entire organization on research and business questions and identifying which data sources are relevant. Not all feedback holds equal value so strategic focus is essential. Responsible and Effective Use of AI for VOC ResearchAI tools like Copilot and Claude can rapidly synthesize and summarize large volumes of feedback, but Kristyn warns they often miss meaningful innovation details and tend to smooth out themes, potentially losing valuable nuance. These tools can’t be used without trained humans guiding the process and critically evaluating the customer needs that AI identifies. Kristyn notes that AI tools are built to affirm the user. AI is good at summarizing and smoothing themes, but innovation lives in the details, so effective VOC can be lost when using AI summaries alone. A customized AI tool built for customer research and trained human analysts can avoid this problem. What is Voice-of-the-Customer?In order to evaluate AI-generated customer needs, it’s important to understand what VOC is. Kristyn defines VOC as a systematic understanding of customers needs or benefits they seek from using your product. That’s very different from the solution that meets those needs. Real-World Example of Finding the Signal in VOC ResearchKristyn shares a case study from a time her team worked with an HVAC company. They embarked on an effort to understand feedback related to a specific component of the HVAC system. They explored existing customer feedback on contractor forums, review sites, YouTube, and social media, and used both AI tools and humans to analyze the data. By outlining their research questions and having a clear picture of existing feedback, they were able to zero in on a few important insights: First, in addition to contractors, homeowners were talking online about this product, which provided additional insights about customer needs. Second, the research pointed the team to a specific area of the installation process they needed to explore more. To separate the signal from the noise, the team recognized these needs in the existing data and validated their findings further with traditional techniques like ethnography and interviews. This combination allowed the team to move faster and have more resources for traditional research. VOC Focus BriefFor teams that don’t have access to AI tools designed specifically for VOC research, Kristyn recommends using a VOC focus brief, a shared document where teams state research questions, hypotheses, and possible data sources. Next, create an analysis plan and ensure the entire team is well-aligned. Kristyn also recommends developing the VOC brief with others from cross-functional teams and sharing it widely across the ...
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    36 分
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