Here are some study notes that I created whilst learning more about the Scrum.org Professional Product Discovery and Validation (PPDV) course and assessment.
These are shared as a revision resource intended to summarise the key elements.
Defining Work As Problems To Solve
Product development is unpredictable, and assumptions often prove incorrect. Engaging directly with customers can validate assumptions, reduce waste, and improve product value.
Framing work as problems encourages creative thinking and iterative experimentation. Clearly defining a business problem involves stating goals, identifying issues, and leaving space for solutions. A well-defined problem statement focuses on customer value and avoids rushing into solutions. Without this clarity, teams risk making poor decisions and failing to solve the problem.
The Truth Curve
The Truth Curve helps balance experimentation and product development by mapping evidence against investment. It emphasises starting with low-cost experiments to gather evidence before making significant investments. Moving up the curve increases confidence in a hypothesis, guiding teams from validating if a solution solves a problem to assessing if it’s a viable business. Falling above or below the curve risks analysis paralysis or overbuilding without evidence. Negative feedback signals the need to pivot or stop. Learning from failure is valuable and prevents wasting resources on low-value products, especially in traditional organizations resistant to admitting failure.
Experimentation Practices
Running experiments to validate product assumptions is essential to gather evidence before committing significant resources. These experiments help identify whether features address user needs, reducing risks and optimising decision-making. They can be low-investment, helping teams test hypotheses effectively.
Types of Experiments:
- Observation: Directly observe users interacting with products to identify needs and pain points.
- Interviews: Collect qualitative insights on user needs, preferences, and pain points.
- Pre-Order Page: Measure demand by allowing customers to pre-order before product launch.
- Feature Fake: Test interest in a feature by offering a clickable, non-functional button.
- Wizard of Oz: Simulate product functionality with humans managing operations behind the scenes.
These experiments help validate assumptions, pivot when necessary, and prevent wasted resources.
DIBB Framework
DIBB (Data, Insights, Beliefs, Bets) is a product management framework that moves from raw data to strategic actions. It ensures decisions are data-driven, hypothesis-based, and validated through structured experimentation for improved product outcomes.
The Double Diamond Method
The Double Diamond method is a structured design process with two phases: discovering/defining the problem and developing/delivering the solution. Each phase involves divergent thinking (exploring possibilities) and convergent thinking (narrowing down options). In the first phase, user research and data analysis help define the problem. In the second, brainstorming, prototyping, and testing generate and refine solutions. The method encourages a thorough exploration of problems and disciplined solution development.
Problem Fit, Solution Fit & Market Fit
Problem fit, solution fit, and market fit are essential stages in product discovery and validation.
- Problem fit focuses on identifying and validating genuine user problems through research, interviews, and observations.
- Solution fit involves creating, testing, and refining prototypes to ensure the solution effectively addresses the problem and is desirable for users.
- Market fit confirms the solution’s performance in the market, considering competition, demand, and positioning for sustainable growth.
Each stage builds on the previous one, ensuring the product solves real problems, meets user needs, and achieves commercial success.
Product Discovery & Validation With Scrum
Embedding product discovery and validation in Scrum ensures continuous alignment with user needs, early validation, and data-driven decisions. This iterative approach reduces risks, enhances product success, and involves the entire Scrum team in ongoing learning. Discovery work spans Sprints, encouraging transparency and prioritization of learning activities.
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